FABIO FREITAG - Federal University of Rio Grande do Norte · fabio freitag natal, 2013. universidade federal do rio grande do norte programa de pÓs-graduaÇÃo em neurociÊncias

UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE

PROGRAMA DE PÓS-GRADUAÇÃO EM NEUROCIÊNCIAS

I N S T I T U TO D O !C É R E B RO

ENCODING MECHANISMS BASED ON FAST OSCILLATIONS IN THE RETINA OF THE CAT AND THEIR DEPENDENCIES ON

ANESTHESIA

F A B I O F R E I T A G

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ANESTHESIA


TRABALHO APRESENTADO AO PROGRAMA DE PÓS-GRADUAÇÃO EM NEUROCIÊNCIAS DA UNIVERSIDADE

FEDERAL DO RIO GRANDE DO NORTE COMO REQUISITO PARCIAL PARA A OBTENÇÃO DO

G R A U D E M E S T R E

ORIENTADOR: Prof. Dr. SERGIO NEUENSCHWANDER

NEUROBIOLOGIA DE SISTEMAS E COGNIÇÃO

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ANESTHESIA


DISSERTAÇÃO APRESENTADA AO PROGRAMA DE PÓS-GRADUAÇÃO EM NEUROCIÊNCIAS

DA UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE

COMO REQUISITO PARCIAL PARA A OBTENÇÃO DO GRAU DE MESTRE. ÁREA DE CONCENTRAÇÃO: NEUROBIOLOGIA DE SISTEMAS E COGNIÇÃO.

APROVADA EM: 27.08.2013

B A N C A E X A M I N A D O R A : PROF.DR. SERGIO NEUENSCHWANDER

PROF. DR. ADRIANO TORT

PROF. DR. JEROME BARON

AB S T R A C T

Processing in the visual system starts in the retina. Its complex network of cells with different properties enables for parallel encoding and transmission of visual information to the lateral geniculate nucleus (LGN) and to the cortex. In the retina, it has been shown that responses are often accompanied by fast synchronous oscillations (30 - 90 Hz) in a stimulus-dependent manner. Studies in the frog, rabbit, cat and monkey, have shown strong oscillatory responses to large stimuli which probably encode global stimulus properties, such as size and continuity (Neuenschwander and Singer, 1996; Ishikane et al., 2005). Moreover, simultaneous recordings from different levels in the visual system have demonstrated that the oscillatory patterning of retinal ganglion cell responses are transmitted to the cortex via the LGN (Castelo-Branco et al., 1998). Overall these results suggest that feedforward synchronous oscillations contribute to visual encoding.

In the present study on the LGN of the anesthetized cat, we further investigate the role of retinal oscillations in visual processing by applying complex stimuli, such as natural visual scenes, light spots of varying size and contrast, and flickering checkerboards. This is a necessary step for understanding encoding mechanisms in more naturalistic conditions, as currently most data on retinal oscillations have been limited to simple, flashed and stationary stimuli. Correlation analysis of spiking responses confirmed previous results showing that oscillatory responses in the retina (observed here from the LGN responses) largely depend on the size and stationarity of the stimulus. For natural scenes (gray-level and binary movies) oscillations appeared only for brief moments probably when receptive fields were dominated by large continuous, flat-contrast surfaces. Moreover, oscillatory responses to a circle stimulus could be broken with an annular mask indicating that synchronization arises from relatively local interactions among populations of activated cells in the retina.

A surprising finding in this study was that retinal oscillations are highly dependent on halothane anesthesia levels. In the absence of halothane, oscillatory activity vanished independent of the characteristics of the stimuli. The same results were obtained for isoflurane, which has similar pharmacological properties. These new and unexpected findings question whether feedfoward oscillations in the early visual system are simply due to an imbalance between excitation and inhibition in the retinal networks generated by the halogenated anesthetics. Further studies in awake behaving animals are necessary to extend these conclusions.

Key words: retina, oscillation, synchronization, halothane, visual.

RESUMO

O processamento da informação visual se inicia na retina. A sua complexa rede de células com diferentes propriedades permite que a informação visual seja codificada em canais paralelos e transmitida para o núcleo geniculado lateral (LGN) e o córtex. Na retina, tais respostas estão frequentemente acompanhadas por oscilações sincronizadas de alta frequência (30 – 90 Hz) em uma maneira dependente do estímulo. Como demonstrado em estudos na rã, coelho, gato e macaco, respostas oscilatórias ocorrem em geral a estímulos relativamente grandes, podendo codificar propriedades globais do estímulo como o tamanho e continuidade (Neuenschwander and Singer, 1996; Ishikane et al., 2005). Além disso, registros simultâneos em diferentes níveis do sistema visual têm mostrado que o padrão de oscilação nas células ganglionares retinianas é transmitido para o córtex visual via LGN (Castelo-Branco et al., 1998). De uma forma geral, esses resultados sugerem que oscilações sincronizadas em uma maneira feedforward são importantes na codificação da informação visual.

No presente estudo feito no LGN de gatos anestesiados, investigamos o papel das oscilações retinianas no processamento de informação visual através da apresentação de estímulos complexos, como cenas naturais, pixels aleatórios no tempo e espaço, além de grades em movimento. Esse é um importante passo para o entendimento de mecanismos de codificação em condições naturais, já que grande parte dos estudos que investigaram o papel de oscilações retinianas utilizaram-se de estímulos simples e estacionários. Análises de correlação de respostas neuronais (spiking responses) confirmaram resultados prévios mostrando que respostas oscilatórias na retina (observadas aqui a partir de registros no LGN) dependem do tamanho e estacionariedade do estímulo. Para filmes de cenas naturais (em escala de cinza e preto e branco) oscilações apareceram apenas por breves momentos provavelmente quando os campos receptores foram dominados por padrões extensos e contínuos (para ambas as escalas). As atividades oscilatórias parecem ser dependentes de uma massa crítica de células ativadas sugerindo que esse padrão regular de atividade surge através de interações horizontais na retina.

Nossos resultados mostram, além disto, que surpreendentemente oscilações da retina no gato são dependentes da anestesia mediada por halotano. Na ausência deste, atividades oscilatórias estiveram ausentes independentemente das características dos estímulos visuais. Resultados semelhantes foram obtidos para o isoflurano, anestésico com propriedades farmacológicas similares. Esse novo e inesperado resultado nos faz questionar se oscilações feedforward no sistema visual não seriam resultado de um desequilíbrio entre correntes de excitação e inibição nas redes retinianas gerado pelos anestésicos halogenados. Experimentos futuros em animais acordados serão necessários para confirmar essas conclusões.

Palavras-chave: retina, oscilação, sincronização, halotana, visão.

LI S T O F F I G U R E S A N D TA B L E S

Figure 1│ Schematic representation of the LGN in the cat.

Figure 2│ Stimulus size modulates spiking responses of an ON-center cell in the LGN.

Figure 3│ The binding by synchronization hypothesis.

Figure 4│ Oscillatory responses in the retina.

Figure 5│ Synchronous oscillations in the retina arises from population interactions.

Figure 6│ Feedforward synchronization in the retinogeniculate pathway.

Figure 7│ Synchronous oscillations in the retina encode stimulus continuity

Figure 8│ Schematic representation of the electrode recording system used in this study.

Figure 9│ Quartz-insulated tungsten-platinum electrodes.

Figure 10│ Stimulus size and circle annular mask protocols.

Figure 11│ Percolation stimulus protocol.

Figure 12│ Dynamical visual stimuli.

Figure 13│ Stimulus size and luminance modulate fast oscillations in the retinogeniculate system.

Figure 14│ Correlated activity across large distances in the retina.

Figure 15│ Breaking stimulus continuity disrupts oscillatory responses to a circle stimulus.

Figure 16│ Percolation threshold does not lead to a phase transition in the oscillatory responses.

Figure 17│ Fast oscillations appear only briefly for homogeneous image segments in natural scene movies.

Figure 18│ Oscillatory responses to gratings as function of stimulus temporal frequency.

Figure 19│ Halothane anesthesia greatly affects fast oscillations in the retina.

Figure 20│ Effects of halothane on LGN responses to a linear ramp stimulus.

Figure 21│ Oscillation strength as a function of halothane levels for responses to a linear ramp stimulus. Population data.

Figure 22│ Retinal oscillations in response to natural scene movies vanish in absence of halothane.

Figure 23│ Oscillation strength as a function of halothane levels for responses to natural scene movies. Population data.

Figure 24│ Retinal oscillations in LGN responses to grating stimuli.

Figure 25│ Oscillation strength as a function of halothane levels for responses to grating stimuli. Population data.

Figure 26│ Isoflurane, an halogenated anesthetic similar to halothane, is also responsible for the appearance of fast oscillations in the retinogeniculate system.

Figure 27│ Example of a small-world network.

Figure 28│ Visual processing using coupled oscillators.

Table 1│ Mean values and standard deviation for modulation amplitude and firing rate of the 10 different sizes in 3 different levels of luminance.

Table 2│ Means and standard deviation for modulation amplitude and firing rate for masked and unmasked circular stimulus.

SUMMARY

AB S T R A C T A N D K E Y W O R D S …………..…….…….………………… 04

RE S U M O E PA L AV R A S-C H AV E …………..…….…….……………… 05

LI S T O F F I G U R E S A N D TA B L E S ………….….….….………………… 06

SU M M A RY ……………………..…….….….….…………………… 07

IN T R O D U C T I O N ……………………..…….….….….……………… 09

1.1 PA R A L L E L C H A N N E L S I N T H E R E T I N O G E N I C U L AT E S Y S T E M ………… 10

1.2 NE U R O N A L E N C O D I N G …………………………….……….……… 11

1.3 TH E B I N D I N G B Y S Y N C H R O N I Z AT I O N H Y P O T H E S I S …………..…........ 13

1.4 SY N C H R O N O U S O S C I L L AT I O N S I N T H E E A R LY V I S U A L S Y S T E M .............. 15

1.5 RE T I N A L O S C I L L AT I O N S A S A N E N C O D I N G M E C H A N I S M ........................ 18

1.6 AN E S T H E S I A E F F E C T S…………………….………….………….… 20

2. OB J E C T I V E S ……………………..…….….….….……………… 21

Specif ic goals ……….……….……………….……….…………… 21

3. ME T H O D S ………………………………………………………… 22

3.1 EX P E R I M E N TA L S E S S I O N S…………..…..…..…..…..…..…..…..…. 22

3.1 .1 SU R G I C A L P R O C E D U R E S , A N E S T H E S I A A N D L I F E S U P P O RT… 22

3.1 .2 RE C O R D I N G D E V I C E A N D E L E C T R O D E S .….…....…..…..…. 23

3.1 .3 S I G N A L C O N D I T I O N I N G A N D D ATA A C Q U I S I T I O N…………… 25

3.2 . V I S U A L S T I M U L I ………………………………………………… 26

Circle size …………………………………………………… 27

Circle annular mask ………………………………………… 27

Percolation stimulus ………………………………………… 27

Natural movies and gratings ………………………………… 28

3.3 . HA L O T H A N E E F F E C T S ……………………………………………… 29

3.4 . DATA A N A LY S I S …………………………………………………… 29

4. RE S U LT S …………………………………………………………… 31

4.1 . DE P E N D E N C I E S O N S T I M U L U S S I Z E ………………………………… 31

4.2 . BR E A K I N G S T I M U L U S C O N T I N U I T Y ………………………………… 33

4.3 . PE R C O L AT I O N S T I M U L U S ………………………………………… 34

4.4 . OS C I L L AT O RY R E S P O N S E S T O DY N A M I C A L S T I M U L I ………………… 35

4.5 . DE P E N D E N C I E S O N H A L O T H A N E L E V E L S………… ………………… 37

5. D I S C U S S I O N ………… …………………………………………… 43

5.1 ST I M U L U S D E P E N D E N C I E S…………………………………………… 44

5.2 EF F E C T S O F H A L O T H A N E A N E S T H E S I A………………………………… 47

6. CO N C L U S I O N ………………………………………………………… 49

7. RE F E R E N C E S ………………...………………………………………… 50

8. TA B L E S……………………...………………………………………… 56

9. AC K N O W L E D G M E N T S……….………………………………………… 58

1. IN T R O D U C T I O N

It is impossible to think about how we interact with the world without focusing

on sensory systems. Our personal sensory experiences and the memories resulting from

them contribute directly to shaping our identity. Moreover, perceived environmental

cues like odors, colors, shapes and sounds, are essential for building appropriate

responses during our social interactions. Being able to recognize a threatening predator,

a potential prey, or a receptive female, for example, are crucial for the maintenance of

the life of an individual and eventually transmission of its genes to the next generations.

Given their central importance, its is not surprising to see that during evolution different

sensory modalities appeared associated with distinct neural machinery. Yet, despite

being represented by multiple inputs, objects are perceived not as a collection of

independent attributes but as unified percepts. What are the neural mechanisms

responsible for the representation and coordination of inputs into coherent percepts?

One strategy that the brain adopted to represent sensory information was through

parallel processing (Nassi and Callaway, 2009). This notion was first put forward for the

somatosensory system, where the sensations of pain and temperature were found to be

transmitted independently through parallel pathways (Gasser and Erlanger, 1929). This

concept was later extended to the visual system based on the observation that at least

three groups of axons (with distinct calibre and conduction velocities) could be

identified in the optic nerve of the rabbit (Bishop, 1933). The concept of parallel

pathways was further supported by studies in the visual system of cats and monkeys

showing independent forward processing channels from the retina to the lateral

geniculate nucleus (LGN) and to the primary visual cortex (Casagrande and Kaas, 1994;

Sherman and Guillery, 2006). In addition to parallel channels, the visual system is

organized in multiple areas, which provide multiple representations of the visual field

(Gattass et al., 2011).

Despite all the data accumulated on how neural systems represent environmental

bits of information we still know very little about the nature of the integrative process in

the brain. In particular we do not know how perceptual binding is achieved based on the

highly distributed architecture of the sensory systems.

Fast osci l lat ions in the ret ina by Fabio Freitag 9

1.1 PA R A L L E L C H A N N E L S I N T H E R E T I N O G E N I C U L AT E S Y S T E M

Even a simple visual scene can be decomposed into distinct attributes such as

brightness, color, depth, shape and motion. Processing all this information in parallel

implies into representing specific features by appropriate channels, from the first

processing steps at the level of the retina to the primary visual cortex and extrastriate

areas (Livingstone and Hubel, 1988).

In the retina, evidence for parallel processing was first obtained by Christina

Enroth-Cugell and John Robson with their seminal work in the cat (Enroth-Cugell and

Robson, 1966). In response to light, retinal photoreceptors change their membrane

potential. These signals are then transmitted through the bipolar cells to the ganglion

cells, where action potentials are generated and sent to the retinogeniculate pathway.

Distinct pathways are originated from the different types of retinal ganglion cells, which

convey parallel information first to the lateral geniculate nucleus (LGN) and then to the

visual cortex (Yeh et al., 2003). In the cat, the two most important functional cell types

are the X- and Y-cells. These cell types differ primarily in their morphology and

response properties. X-cells show sustained responses to spot stimuli and have dendritic

arbors confined to a narrow area, while Y-cells respond transiently and have wide

receptive fields (RFs) (Saito, 2004). Furthermore, X-cells exhibit a linear summation of

responses for stimuli presented to different subregions of the RF. In contrast, Y-cells do

not integrate linearly the spatial information (Kratz et al., 1978).

In the cat LGN, principal cells were also classified as X- or Y-cells (Hoffmann et

al., 1972). As in the retina, X-cells are less sensitive to fast movements of the stimulus,

while Y-cells show strong modulation to motion since they are capable of responding to

stimuli of high temporal frequency. Moreover, X-cells are capable of responding to

stimuli of high spatial frequencies, while Y-cells respond vigorously to stimuli of low

spatial frequency. Thus, we can infer that the Y-channel is important to encode visual

aspects related to motion processing, while X-cells are related to processing information

such as form (Tolhurst, 1973; Stone, 1983).

The two functional classes of cells in retina also differ in their projections to the

LGN. X-cells project mainly to the contralateral layer A and Y-cells diverge to project to

layers A and C, both contralateral (Yeh et al., 2003). Commonly known as a first-order


relay, the LGN is a laminated structure localized in the thalamus, conveying visual

information from the retina to the cortex. In the cat, the LGN is composed of three main

layers: A, A1 and C, exhibiting a retinotopic organization with emphasis in the

representation of the the central region of the visual field (Sherman and Guillery, 2002,

see Figure 1). The receptive field (RF) structure of LGN neurons is concentric, with an

antagonistic center-surround organization, similar to those observed in retinal ganglion

cells, and with either ON-center or OFF-center (Hubel and Wiesel, 1962).

1.2 NE U R O N A L E N C O D I N G

Specific modalities of sensory stimuli such as pressure, sound level or visual

contrast have been thought to be encoded by the spiking rate of neurons. In the visual

system, the relay cells in the LGN transmit visual information encoded by retinal

ganglion cell responses to the primary visual cortex. The notion of feature encoding by

firing rate has its roots in the seminal studies of Hubel and Wiesel (1961) using

simplified stimuli such as light spots and oriented bars.

A simple demonstration of how the spiking responses encode stimulus

characteristics can be seen in the experiments of Hubel and Wiesel (1961) in the LGN.

As shown in Figure 2, the size of the stimulus strongly modulates the neuronal

responses. When the stimulus is relatively large but still confined to the the center of the

Figure 1. Schematic representation of a cat LGN in a coronal section showing its laminar organization in three main layers, A, A1 and C, and the lines of projection representing single points in the visual field. OT, optic tract (from Sherman and Guillery, 2002).


RF, the responses are vigorous. However, when the stimulus reach the surround

subregions of the RF, responses decreases. Thus, the concentric organization of the RFs

in the retinogeniculate pathway provides a way of encoding a simple feature of the

visual stimulus, its size.

Average firing rates, however, are not the only possibility for representing a

stimulus. It has been argued that the fine temporal structure of the responses may be

relevant for encoding. Accordingly, experimental and theoretical evidence has shown

that the relative timing structure of single cell responses (Reich et al., 1997), relative

spike latencies in populations (Gollisch and Meister, 2008), and phase-locking between

spikes and ongoing oscillatory activity (Koepsell et al., 2010) carry information about

the stimulus.

Figure 2. Stimulus size modulates spiking responses of an ON-center cell in the LGN. When a circular stimulus covered entirely the center of the RF responses reach their maximum. Notice that firing rate decreases for a very large stimulus covering the surround region of the RF. Responses are poor when there is no light in RF center (modified from Hubel and Wiesel, 1961).


1.3 TH E B I N D I N G B Y S Y N C H R O N I Z AT I O N H Y P O T H E S I S

As discussed before, information from the visual scene is decomposed and

processed in parallel pathways which ultimately converges onto higher visual areas,

where neurons respond selectively for combinations of color, motion, orientation,

texture, shape and depth. Feedforward connections from earlier processing stages to

higher areas, such as V2, V4 and MT, coexist with reciprocal feedback (Rosa and

Tweedale, 2005; Sillito et al., 2006) and lateral connections (from equivalent level of

processing) (Wunderle et al., 2013). Thus, the visual system seems to work in a

hierarchical fashion, first analyzing the various elemental features which are combined

later in higher areas. How bits of visual information are bound into coherent wholes?

Which are the mechanisms responsible for perceptual binding? This is still a largely

unresolved question and still a big challenge for neuroscience.

An important step for understanding perceptual binding mechanisms was made

by the synchronization hypothesis proposed by von der Malsburg (1981). Essentially,

Christoph von der Malsburg‘s idea was that the elementary features of perceptual

objects are linked together by the synchronous activity of distributed neuronal

assemblies. The synchronization of the spiking activity at very fine time scale (order of

milliseconds) would provide a mechanism for building flexible relationships in the

brain. This would explain how we bind information related to one object and distinguish

from the background (see the dalmatian dog in Figure 3).

Figure 3. The binding by synchronization hypothesis. Local features in a visual scene (spots of the dalmatian dog) are linked by the synchronous activity among neuronal assemblies. In accord to this hypothesis, visual objects (the whole dog) are segregated from the background because of temporal relationships created in the brain. Local field potential (LFP) and spiking activity (MUA) traces show synchronous oscillatory responses in the visual cortex in response to a single coherent bar (data from Gray and Singer, 1989).


Experimental evidence for the binding by synchronization hypothesis was first

obtained in the visual cortex of the cat (Eckhorn et al., 1988; Gray et al., 1989). In

response to simplified stimuli, such as moving bars, single cells and groups of cells

(multi-unity activity, MUA) in areas 17 and 18 exhibit strong oscillatory activity at the

gamma range, from 35 to 85 Hz (Figure 3). In these seminal studies, periodic activity in

spiking activity was found to be highly correlated with the simultaneously recorded

local field potential (LFP). Action potentials generated synchronically within gamma

cycles would have a higher probability to generate new action potentials in target cells,

allowing for a neuronal coding based on timing relationships (see review in Uhlhaas et

al., 2011). These findings suggest that columns at different regions of the cortex may

synchronize their activity, leading to synchronous ensembles representing distinct

surfaces or objects in a visual scene. Interestingly, gamma oscillatory responses were

not found in the LGN (Gray and Singer, 1989) indicating that the gamma oscillations

resulted from an intracortical mechanism.

An important finding in these early studies was that synchronization of neuronal

responses appears only for single contour objects (such as a single coherent bar, Gray et

al., 1989) but not by different contours moving in different directions (Engel et al.,

1991), revealing that synchronization is highly context-dependent (Singer, 1999). The

idea that synchronization can mediate a binding mechanism was supported later by a

series of experimental studies in cats and monkeys (Engel et al., 1991; Kreiter and

Singer, 1996). Evidence was obtained not only for simple moving bars but also for bi-

stable stimuli such as moving plaids (Castelo-Branco et al., 2000). Cells in area 18 and

another cortical area called postero-medial bank of the lateral suprasylvian sulcus

(PMLS) of the cat synchronize their responses when induced by contours of the same

surface (pattern motion of the plaids) but not by contours belonging to different surfaces

(component motion of the plaids). These results are very interesting because in principle

they could provide an explanation for perceptual dynamics.

A number of studies, mainly in behaving monkeys, however, have shown

contrary evidence for the binding by synchronization hypothesis (Thiele and Stoner,

2003; Roelfsema et al., 2004; Palanca and DeAngelis, 2005; Lima et al., 2009). These

studies are very important in trying to establish a direct correlation between neuronal

synchronization and perception, what was not possible in the early studies in the


anesthetized cat. In addition, a body of evidence has been gathered recently showing

that gamma processes are associated with attention (Engel et al., 2001; Fries et al.,

2001, see review in Fries, 2009) and temporal expectancy (Lima et al., 2011). These

latter studies suggest that gamma synchronization may be important for controlling the

flow of information across the cortical networks.

Overall, a consensus on the role of gamma oscillations in cognitive function is

still lacking. A possible reason is that gamma oscillations may be related to more than

one elementary function in the highly diverse neural subsystems that composes the

brain (Merker, 2013).

1.4 SY N C H R O N O U S O S C I L L AT I O N S I N T H E E A R LY V I S U A L S Y S T E M

Oscillations are not restricted to the cortical networks. In fact, oscillatory

responses were observed in the optic nerve and eye as early as the first recordings of

Figure 4. Fast retinal oscillations have been described in a variety of animals, such as the frog, rabbit (not shown), cat and monkey, with intracellular (frog) and extra-cellular recording techniques. Oscillations are generally seen in MUA responses to large visual stimuli and also spontaneously, in the dark.

A N E S T H E T I Z E D M O N K E Y

A N E S T H E T I Z E D C AT

B E H A V I N G F R O G

Doty and Kimura, 1963

Laufer and Verzeano, 1967

Neuenschwander and Singer, 1996

Doty and Kimura, 1963

Ishikane et al., 2005


neuronal activity became available (Gotch, 1903; Fröhlich, 1914; Granit and Therman,

1935). Activity in sensory systems, in particular, are commonly associated with fast

oscillations, such as in the olfactory bulb (Laurent, 2002), somatosensory (Ahissar et

al., 2000) and auditory cortices (Roberts and Rutherford, 2008), and in the insect optic

lobe (Kirschfeld, 1992). In the retina, prominent oscillations have been described in

various species, including the frog (Ishikane et al., 1999; Ishikane et al., 2005), rabbit

(Ariel et al., 1983), cat (Doty and Kimura, 1963; Laufer and Verzeano, 1967; Arnett,

1975; Neuenschwander and Singer, 1996; Neuenschwander et al., 1999) and monkey

(Doty and Kimura, 1963) (Figure 4). Strong oscillations in retinal ganglion cell activity

can be observed for responses to large spots of light, covering the center and the

surround subregions of the RFs (Neuenschwander et al., 1996), as well as Ganzfeld

illumination (Laufer and Verzeano, 1967), and also spontaneously, in the dark (Bishop

et al., 1964; Neuenschwander et al., 1999). Correlation analysis of multi-electrode

recordings suggested that synchronous oscillations in the retina arises from large-scale

interactions between the ganglion cells (Laufer and Verzeano, 1967; Neuenschwander et

al., 1999, see example in Figure 5). In the cat, synchronization of oscillatory responses

has been observed for distances larger than 20 degrees of visual angle across the retina

(Neuenschwander and Singer, 1996). Moreover, fast oscillations were found in

Figure 5. Synchronous oscillations in the retina arises from population interactions. MUA traces show strong oscillatory responses to a large spot of light. Notice that correlated individual spikes from different cells exhibit missing cycles. Color arrows indicated spikes from different cells. Qualitative analysis (data from Neuenschwander and collaborators, Max-Planck Institute for Brain Research, Frankfurt).

10 ms

30 ms


responses to all functional types (ON and OFF-cells, X- and Y-cells) in the retina and

LGN (Ariel et al., 1983; Neuenschwander et al., 1999; Ito et al., 2010).

An important aspect in the organization of the retinogeniculate system is the

reliability of information transfer. Retinal afferents to the LGN consist of thick axons

involving richly branched terminal arbors with boutons densely distributed in terminal

clusters (Sherman and Guillery, 2006). This exquisite organization confers great

robustness for the retinogeniculate transmission. Analysis of retinal excitatory post-

synaptic potentials (EPSPs) associated with LGN spike waveforms (S-Potentials) shows

that most LGN neurons have one retinal ganglion cell input that accounts for nearly all

LGN spikes sent to visual cortex (> 95% of LGN spikes are associated with an EPSP

from a single retinal ganglion cell, Sincich et al., 2007). Thus, it is not surprising to see

that the retinal fast oscillations are faithfully transmitted through the retinogeniculate

pathway up to the LGN. Accordingly, it has been demonstrated by means of

simultaneous recordings from the retina and LGN that retinal oscillatory inputs survive

transmission at the retinogeniculate synapses (Arnett, 1975; Neuenschwander and

Singer, 1996). Moreover, simultaneous recordings from the LGNs in the two

hemispheres have shown that only LGN cells receiving inputs from the same eye are

Figure 6. Feedforward synchronization in the retinogeniculate system. Synchronization of oscillatory responses is seen only among LGN cells receiving inputs from the same eye (right lamina A and left lamina A1, RA - LA1), whereby it did not matter whether the cells were located in the same LGN or in the LGNs of the two hemispheres. Direct recordings form the retina and LGN have demonstrated that the synchronizing mechanism resides in the retina (from Neuenschwander and Singer, 1996).

LEFT EYE

LEFT LGNRIGHT LGN

RARA1 LA1

-80 0 80Time lag (ms)

100


capable of synchronizing their responses (Figure 6), excluding thalamic mechanisms

since they cannot account for synchronization across the two hemispheres

(Neuenschwander and Singer, 1996). In addition, recordings from the retina, LGN and

visual cortex in the cat (Castelo-Branco et al., 1998) have provided direct evidence that

oscillatory responses from the retina are transmitted to the cortex, characterizing a

feedforward synchronization mechanism in the early visual system (Neuenschwander et

al., 2002).

It is important to notice that although retinal oscillations are transmitted to the

cortex, they do not necessarily contribute to generate cortical gamma. In the study of

Castelo-Branco et al. (1998) in the cat, data from simultaneous recordings from the

retina, LGN and cortex (areas A17 and A18) indicate that gamma in the cortex arises

independently from oscillatory retinogeniculate inputs. Moreover, in the cortex, gamma

responses are very sensitive to the orientation selectivity of the cells (Gray and Singer,

1989), a feature that is irrelevant for responses in the retina. Another important finding

in the study of Castelo-Branco et al. (1998) is the noticeable differences in oscillation

frequencies. Gamma oscillations in the cortex have frequencies in the range of 30 to 60

Hz, while fast oscillations in the retina have frequencies at a much higher band, from 60

to 120 Hz. It is possible, however, that synchronized responses in the retina bias

synchronization of responses at the cortical level. Alternatively, synchronous inputs to

postsynaptic cells may improve information transmission (Dan et al., 1998). For all

these conjectures, at present it is still unclear whether fast oscillatory retinogeniculate

inputs contribute or not to cortical processing.

1.5 RE T I N A L O S C I L L AT I O N S A S A N E N C O D I N G M E C H A N I S M

If retinal oscillations indeed play any role in vision they most likely convey

stimulus information upstream to the cortex. In the anesthetized cat, Neuenschwander

and Singer (1996) showed evidence that synchronization of oscillatory responses in the

retina (and in the LGN) depends on stimulus continuity. Rectangular stimuli flashed

over the RFs elicited strong oscillatory responses, which were correlated only when the

stimulus was contiguous, linking the two RFs (Figure 7). Based on these findings, the

authors proposed that retinal oscillations serve as a mechanism for conveying

information about stimulus connectedness or continuity. This interpretation has a


parallel with the hypothesis of feature binding by gamma synchronization. Cortical

feature space is far more complex, such as the representation of stimulus orientation,

color, form and texture. In the retina, on the other hand, representational space is

restricted to simpler features, such as position, contrast and size. These ideas gained

support with a recent model of the inner retina showing that correlated oscillatory

activity in the retina convey size information (Stephens et al., 2006). It is possible that

large features in a visual scene, such as continuous surfaces, are encoded by the global

synchronous population activity in the retina. To determine if encoding of stimulus size

depended on connectedness independent of object shape, Stephens et al., (2006) ran a

simulation of responses to a random binary checkerboard stimulus, in which every pixel

changed incrementally its color from black to white (see example in Figure 11). The

sudden appearance of large connected blobs in the stimulus (at the percolation

threshold) led to a sharp phase transition in the oscillatory behavior. Interestingly,

synchronization of oscillatory responses of retinal ganglion cells in the frog is correlated

with escape behavior (Ishikane et al., 2005). These findings are relevant because they

point to a direct link between retinal oscillations and behavior.

Encoding by temporal correlations and by firing rates are not exclusive. As

proposed by Koepsell et al. (2009), a temporal code may co-exist with a rate code in the

Figure 7. Synchronous oscillations in the retina encode stimulus continuity (from Neuenschwander and Singer, 1996). Response to rectangular light stimuli that were either separated by a gap or were contiguous and covered the area between the RFs. When the stimuli were separated, they elicited responses that oscillated with similar frequency (ca. 110 Hz) at both sites but showed no synchrony (left). When the stimuli were spatially contiguous, the oscillatory responses in the LGN became highly synchronous (right).


retina. It is even possible that information about local contrast features and global size

are carried independently by multiplexed channels.

In the present study, we investigate the role of fast oscillations in the retina on

stimulus encoding based on recordings from the LGN in the anesthetized cat. We used a

variety of simplified stimuli to test how oscillatory behavior is related to stimulus

continuity and connectedness. In particular we tested experimentally the stimulus

proposed in the theoretical study of Stephens et al. (2006). We also used dynamical

stimuli, such as natural scene movies, to verify whether retinal oscillatory responses are

present for more naturalistic conditions.

1.6 AN E S T H E S I A E F F E C T S

Halogenated anesthetics, such as halothane and isoflurane, have been commonly

used in many studies of rhythmic activity in the brain. In the cat, cortical gamma

oscillations seem to survive the anesthesia, although efforts are generally made to have

it at low levels (lightly anesthetized animal, e.g., Gray et al., 1989). Nevertheless, it has

been demonstrated with stimulation of the mesencephalic reticular formation that high

cortical activation levels (Munk et al., 1996; Herculano-Houzel et al., 1999) facilitate

gamma activity in the cortex of anesthetized cats. Moreover, a series of recent studies in

the behaving monkey have shown that gamma responses are modulated with attention

(Womelsdorf et al., 2007; Fries 2009) and temporal expectancy (Lima et al., 2011).

Based on these reports one may conclude that low cortical activation, as during the

anesthesia, generally damps gamma responses in the cortex.

Similarly, retinal oscillations have been studied mostly during halothane or

isoflurane anesthesia (Laufer and Verzeano, 1967; Neuenschwander and Singer, 1996;

Ito et al., 2010). A few studies in the 60‘s reported retinal oscillatory responses in non-

anesthetized, paralyzed cats submitted to transpontine transection (Doty and Kimura,

1963). To clarify whether halothane has an effect on the generation of retinal

oscillations we varied systematically the concentration levels of this anesthetic during

the experiments.


2. OB J E C T I V E S

Our primary goal with this study was to test if retinal oscillations (recorded here

in the LGN) serve as an encoding mechanism for stimulus size and continuity as

proposed by a number of theoretical studies. In order to investigate stimulus

dependencies we applied a series of stationary stimuli of various sizes and luminance

values. We compared the oscillatory responses to these simplified stimuli with

responses to stimuli of increasing complexity, such as binary and gray-level natural

movies.

In addition, we have examined how oscillatory responses recorded in the LGN

are dependent on the anesthesia. We studied in particular the effects of halothane and

isoflurane, halogenate anesthetics that have been routinely used in many studies of

neuronal oscillations in cats and monkeys.

Specif ic goals

• Evaluate the dependencies of retinal synchronous oscillations on stimulus size

and contrast;

• Verify whether breaking of stimulus continuity disrupt synchronous oscillatory responses;

• Test if the sudden emergence of large connected clusters (percolation threshold in random binary checkerboards) leads to transitions in the oscillatory patterning of the responses;

• Verify whether retinal oscillations are also present in responses to spatial and temporal complex stimuli, such as gratings and natural scene movies;

• Test the dependencies of the oscillatory responses on halothane (and isoflurane) anesthesia levels.


3. ME T H O D S

Adult cats from the Brain Institute’s colony were used in this study (N= 5). All

experimental procedures were previously approved by the ethical committee for animal

experimentation of the Universidade Federal do Rio Grande do Norte (CEUA-UFRN

protocol nº 019/2012).

Recordings of LGN responses in anesthetized cats were obtained by means of up

to 5 electrodes placed at the representation of the central visual field. Our analysis

consisted in obtaining indicators for the incidence and strength of oscillatory responses

in the retina, which are known to be transmitted faithfully to the LGN (Neuenschwander

et al., 2002). Auto and cross-correlation functions were computed for spiking responses

in order to estimate the modulation amplitude and frequency of the oscillations. The

time course of the oscillatory responses was evaluated with a sliding window analysis.

In addition, the rate levels were estimated by means of average response histograms

(PSTHs). Analysis was based on the NEUROSYNC package developed by S.

Neuenschwander, at the Department of Neurophysiology, Max-Planck Institute for

Brain Research, Frankfurt.

To test the role of retinal oscillations in stimulus encoding we used a variety of

visual stimuli, including spots of light of various sizes and contrasts, checkerboards,

moving gratings and natural scene movies.

In addition in this study we examine the effects of halogenated anesthetics such

as halothane on retinal oscillations.

3.1 EX P E R I M E N TA L S E S S I O N S

Experiments were done in the anesthetized cat. Different anesthetics (e.g.,

ketamine, halothane, isoflurane, fentanyl) were used to establish whether retinal

oscillations are dependent on the agent and level of anesthesia.

3.1 .1 SU R G I C A L P R O C E D U R E S , A N E S T H E S I A A N D L I F E S U P P O RT

An experiment started with an intramuscular injection of ketamine (10 mg/kg,

i.m.) and xylazine (2 mg/kg, i.m.). The cats were then submitted to a tracheotomy to


allow the maintenance of the inhalation anesthesia by artificial ventilation with a gas

mixture of N2O (70%) and O2 (30%), supplemented with halothane (0.8 to 1.2 %,

Tanohalo - Halotano, Cristália; in some experiments isoflurane was also used at similar

concentrations). To ensure the immobilization of the eyes, a muscle relaxant

(pancuronium bromide, 0.25 mg/kg/h) was administrated i.v. continuously during the

experiment. Vital signs such as the ECG, ventilation pressure, rectal temperature

(maintained at 37°C) and expired CO2 (kept in the range of 2.6 to 3.5 %) were

continuously monitored during the experiments by means of a medical life monitoring

system (GE Dash 3000 Patient Monitor). Fluid loss was compensated by continuous i.v.

infusion of saline solution and glucose (5%).

After the animal was placed in the stereotaxic apparatus, a craniotomy was made

in the skull above the LGN (AP 6.5 and ML 9.5). Subsequently, a recording chamber

made of titanium with 14 mm of internal diameter was mounted over the exposed

region and secured into the bone with titanium screws and dental cement. A metal rod

was also secured on the skull for fixation of the head. At the end of all surgical

procedures, the stereotaxic ear and eye bars were removed and the halothane

concentration adjusted between 0.6 and 0.8%. Recordings were obtained during the

following 3–5 days.

During visual stimulation, the pupils were dilated and the nictitating membranes

retracted by means of atropine applied topically (1%). Contact lenses with an artificial

pupil of 3 mm diameter were used to protect the cornea. The representation of the area

centralis was plotted on a white board with help of a fundus camera (Zeiss, Germany)

and served as a reference for estimating the position of the receptive fields.

3.1 .2 RE C O R D I N G D E V I C E A N D E L E C T R O D E S

For the recordings we used a custom made system fitted with up to 5

independently movable electrodes. This system was designed by S. Neuenschwander at

the Max Planck Institute for Brain Research, Frankfurt.

The recording device consisted of 5 Narishige hydraulic microdrives mounted in

a movable platform made in thermoplastic (PEEK) (Figure 8). An acrylic plate secured


a 6 X 5 grid for positioning of the guiding-tubes. The position of the guiding-tubes was

decided previously based on stereotaxic atlas of the cat.

For electrode guiding two needles were mouted in each other (Braun 100

Sterican Ø 0.60 x 60 mm, 23G x 2 ⅜” and Ehrhardt Supra Einmal-Kanülen Ø 0.30 x 23

mm) to obtain a total length of 82 mm. The thicker extremity was used for fixation in

grid (the hole size was sufficient to allow the passage and hold the guiding-tubes), while

the thinner one was placed into the brain.

A glass capilar mounted at the end of the guiding-tubes served as a piston for

moving the electrodes. A piece of needle was used to connect the glass capilar to the

micromanipulators. The recording device was fixated on recording chamber with an

adapter screwed in the recording chamber. X-Y positioning of the guiding-tubes was

adjusted previously to the descent of the electrodes by means of a Narishige X-Y table

(parts of the Narishige MO-95 recording system).

In this study we used tungsten-platinum, quartz-insulated, fiber electrodes (80

µm diameter, Thomas Recording, Germany), see Figure 9. These electrodes were

Figure 8. Schematic representation of our electrode recording system based on Narishige oil-hydraulic microdrives and X-Y table (parts of the MO-95 microdrive, Narishige, Japan). Designed by S. Neuenschwander at the Max-Planck Institute for Brain Research, Frankfurt, 2005. Simulation by Heitor de Oliveira, Brain Institute - UFRN, Natal.

Macro-screw (for macro-movement of the electrodes)

Narishige MO-95 micromanipulators (fro micro-movement of the electrodes)

Electrode macro depth scale (macro-movement of the electrodes)

Guiding-tubes gridGuiding-tubes

Device X-Y table

Narishige micromanipulators sliding suport

Guiding-tubes depth scale Guiding-tubes sliding suport


chosen because they have a very good signal to noise ratio, they are strong enough to

penetrate the intact dura-mater, and have high biocompatibility. The tips were prepared

in a grinding machine (Thomas Recordings, Gießen, Germany) to expose the metal core

with a conical shape (expose tip of 20 µm). Electrodes were connected by welding thin

flexible wires to them.

3.1 .3 S I G N A L C O N D I T I O N I N G A N D D ATA A C Q U I S I T I O N

After the fixation of the recording device and positioning of the guiding-tubes

we generally made a series of exploratory penetrations, usually with a single electrode,

in order to find an appropriate position of the electrodes aiming at the central

representation of the visual field.

Spiking activity from neuronal groups (MUA) were obtained after amplification

and band-pass filtering the signals (0.7 – 6.0 KHz) with a 32-channel Plexon modified

preamplifier fitted with an HST16025 headset (Plexon Inc, Dallas, TX, USA). Data

acquisition was made using the program SPASS, an acquisition system based on

National Instruments boards (M-series acquisition boards, National Instruments, Austin,

TX, USA) written in LabVIEW by S. Neuenschwander at the Max Planck Institute for

Brain Research, Frankfurt. Signals were sampled at 32 kS/s with an additional 10 X

onboard amplification. Spikes were detected with an amplitude discrimination with

threshold set to twice the noise level.

Figure 9. Quartz-insulated tungsten-platinum electrodes. Notice that electrode‘s impedance depends largely on the size of the exposed tip. Electrodes of 0.5 Ω impedance as used in this study are better for MUA recordings. Electrodes were manufactured in our laboratory from quartz electrode fibers using a a special grinding machine (Thomas Recording, Gießen, Germany).

80 µm

METAL CORETIP

1.0 MΩ

0.5 MΩ

QUARTZ


3.2 . V I S U A L S T I M U L I

Visual stimuli were presented on a 21” CRT monitor (Hitachi, CM815ET),

placed at 57 cm of distance from the cat, with a refresh rate of 100 Hz and 1024 x 768

pixels resolution. In these conditions 1° of visual field corresponded to approximately to

25 pixels. The screen was gamma-corrected in order to achieve a linear perception of

luminance (illumination range from 25 to 200 Lux). To control the stimulus presentation

we used the software ActiveStim (www.activestim.com). Protocols consisted of a series

of 60 to 300 repetitions. Generally, stimuli were centered at the mapped receptive field.

All different types of stimuli from different protocols were presented in a random order.

At the beginning of the recording sessions, the receptive field of the cells were

mapped with an automatic algorithm based on a moving bar (1000 X 5 pixels) crossing

the whole field of the monitor in 16 different directions (Pipa et al., 2012). RF maps

were obtained by compiling an average matrix, in which the responses to the moving

bar were added in 10 ms bins (corresponding to 0.2° in visual angle) for all directions

(see example in Figure 14). Depending on the eccentricity of the RFs the electrodes

were moved to a new position aiming at a region corresponding to the central

representation of the visual field.

Figure 10. Stimulus size and circle annular mask protocols. We used annular masks to test whether breaking stimulus continuity disrupts oscillatory responses to a circle. Stimuli were centered at the RF fields. Stimulus sizes are indicated in degrees of visual angle (same conventions in all other figures).

C I R C L E S I Z E

C I R C L E A N N U L A R M A S K

3° 7° 9° 9° 10° 10° 12°12°5° 7° 14°

10°2° 3° 4° 6° 7° 8° 11° 12° 13°

14°


http://www.activestim.com

http://www.activestim.com

Circle size

In order to analyze the effects of the stimulus size and contrast on firing rates

and oscillatory responses, we presented circles of 10 different sizes (2°, 3°, 4°, 6°, 7°,

8°, 10°, 11°, 12° and 13° of visual angle) with 3 different contrast levels (relative levels

of 1.0, 0.5 and 0.3, ranging from 175 to 50 lux; background illumination of 1.0 lux).

Sessions comprised 10 stimulus repetitions for a total of 300 trials.

Circle annular mask

We used annular masks to test whether breaking stimulus continuity disrupts

oscillatory responses to a circle. These stimuli had a shape of donut and were placed

centered over individual RFs, in a way that the responses to the central circle were

independent of the peripheral annulus. Annular masks and solid circles were presented

randomly with different sizes (sequence of 3° circle, 5° circle, 7° circle, 7° masked

circle, 9° circle, 9° masked circle, 10° circle, 10° masked circle, 12° circle, 12 ° masked

circle, 14° circle and 14° masked circle; Figure 10).

Percolation stimulus

Our percolation protocol consisted in presenting sequences of checkerboard

binary images, in which every pixel changed its luminance randomly and once, from

black to white (gray background, see Figure 11). This stimulus was proposed by

Stephens and colleagues in a theoretical study (Stephens et al., 2006) to test if

oscillation dynamics in the retina show a transition point in response to a change in the

spatial properties of the images, when all points get connected (percolation phase

Figure 11. Percolation stimulus. This stimulus protocol was designed to test if oscillation dynamics in the retina show a transition point in response to a change in the spatial properties of the images, when all points get connected (percolation threshold). RF position is indicated by the color circle in the center of the stimulus (same conventions in all other figures).

GRATINGS


transition). The duration of this protocol was controlled and ranged from 1 to 6 seconds

and each session had either 50 or 100 trials. Thus, the speed of the appearing white

pixels also changed. As a control, we used a solid circle (of same size) with luminance

increasing linearly (ramp). This control stimulus was presented randomly with the

percolation stimulus.

Natural movies and gratings

In general, retinal oscillations have been studied with simplified stimuli, such as

spots of lights, bars, gratings, and Ganzfeld stimuli (Neuenschwander et al., 1999;

Laufer and Verzeano, 1967). In many cases the stimuli were static and uniform, very

different from the natural vision. To test how spatial and temporal dynamics in the

stimulus change oscillation behavior we used 4 types of dynamic stimuli: gray-scale

natural scene movies (200 luminance values), binary natural scene movies (2 luminance

values), randomly flickering checkerboards (temporal frequency from 25 to 50 Hz) and

moving gratings (temporal frequency 0,33 Hz, 1,25 Hz and 3 Hz) (see examples in

Figure 18). Two different natural scene movies were presented (referred here as

LabPan and Medikament), consisting of pan images of the laboratory (the same stimuli

Figure 12. Dynamical visual stimuli. Gray-level natural scene movies had 200 luminance values (LabPan movie is depicted here). Binary natural scene movies were displayed with 2 luminance values (black and white). The movies were made with a single global motion component made with a long camera panning (same stimuli used in Haslinger et al., 2012). Random stimuli consisted of flickering checkerboards. Grating stimuli moved at temporal frequencies of 0,33 Hz, 1,25 Hz and 3 Hz.

RANDOM

GRATINGS

GRAY-LEVEL BINARY


used in the study of Haslinger et al., 2012). Trials had duration of 5 sec and each

stimulus was repeated 60 times in a pseudorandom order.

3.3 . HA L O T H A N E E F F E C T S

To test the effects of halothane anesthesia we varied systematically its

concentration levels during the recording sessions (Dräger Vaporizer, Germany).

Accurate estimation of the partial expiratory concentration of halothane (and isoflurane)

in the ventilation system was obtained from readings of the life monitoring apparatus

(GE Dash 3000 Patient Monitor). During the manipulations of halothane (and

isoflurane) levels we supplemented the anesthesia with ketamine or fentanyl (even in

case of total absence of halothane or isoflurane). These supplementary anesthetics were

applied well before the recording sessions.

For testing the effects of halothane we used various static and dynamical stimuli

(circle, linear ramp, natural scene movies and gratings) with characteristics known to

induce strong oscillatory responses in the retina (large, high contrast stimuli). For the

circle stimuli, halothane concentration was changed from 0.74% to 0.20% (at 20 trials

recording sessions for each condition), or smoothly from 1.0% to 0.0% (300 trials

recording session over about 1 hour). For the linear ramp stimuli, we decreased the

halothane levels from 0.95% to 0.20% to obtain recording sessions of 50 trials (2.0 to

3.0 sec duration). For the natural scene movies, we decreased the halothane levels from

1.0% to 0.19% in 3 steps. Finally, for the gratings stimuli, we decreased the halothane

levels from 0.94% to 0.0%. In all cases as a control the concentration of halothane was

restored to previous levels. In a few experiments we substituted halothane for

isoflurane, which is of the same family of halogenated anesthetics.

3.4 . DATA A N A LY S I S

Our analysis was mainly focused on the spiking activity of groups of neurons

(multi-unity activity, MUA). Only occasionally we have used spike sorting methods to

study single unity activity (SUA). Analysis was made using the NEUROSYNC

packaged developed in LabVIEW (Austin, Texas, USA) and in Matlab R10

(MathWorks, Natick, Massachusetts, USA). For spike sorting we used the program


Wave_clus developed by Rodrigo Quiroga (Quiroga et al., 2004). This method uses a

set of wavelet coefficients from each spike selected by a Kolmogorov-Smirnov test of

normality as an input to the clustering algorithm. An automatic clustering algorithm

(super-paramagnetic clustering) identifies different clusters of spikes based on the

selected set of wavelet coefficients.

In order to access the rate and timing of neuronal spike activities induced by

visual stimuli, response histograms were computed with a resolution of 10 ms. We

decided to use small bin sizes because retinal oscillations are known to be very fast (up

to 100 Hz). A 500 to 1000 ms window was used for estimating average firing rates over

different response epochs.

Our analysis of neuronal oscillations was made in the time domain. Correlation

functions for spiking responses were computed for 500 to 1000 ms analysis windows.

For a quantitative analysis a Gabor function was fitted to the correlograms and its

parameters were used to infer the strength and frequency of the synchronous

oscillations. The Levenberg-Marquardt algorithm was used (described in Press et al.,

1986) for the fitting procedure. To estimate correlation strength, a modulation amplitude

index was computed as the ratio between the amplitude of the central peak of the fitted

function and its offset. In cases when an oscillatory patterning of the responses was

detected, we used two metrics to evaluate oscillations strength: the oscillation amplitude

(correlograms were normalized by the geometric mean of firing rates) and the

modulation amplitude of the first satellite peak (MAS) (König, 1994).

A sliding window analysis was applied to follow the oscillatory behavior of the

responses over time. This analysis consists of sampling the response using a 100-ms

analysis window in 50-ms steps. Correlograms obtained for each overlapping window

were then plotted as a two-dimensional array, where the y-axis denotes time lag of the

correlation and the x-axis denotes the time course of the responses. The amplitude of the

correlograms was normalized by the geometric mean of firing rates and displayed with a

color code.

Comparisons between stimulus conditions group data was subject to the analysis

of variance (ANOVA, after testing for normal distribution) and post-hoc Bonferroni for

testing differences in pairs of mean values. Confidence interval was chosen with 95% (p

< 0.05).


4. RE S U LT S

4.1 . DE P E N D E N C I E S O N S T I M U L U S S I Z E

To test how stimulus size modulates the strengh of retinal oscillations (seen here

in LGN responses) we used a simple circular light stimulus, which was flashed over the

RFs of the recorded neurons. The sliding analysis presented in Figure 13 gives an

example of how strong oscillations in the retinogeniculate system can be in response to

a large stimulus (in this case, 10°). We used 10 different sizes of the simulus at 3

luminance levels (175, 80 and 50 Lux, corresponding to contrast ratios of 1.0, 0.5 and

0.3) (N= 14 recording sessions, comprising 3 different experiments, 10 repetitions for

10 different sizes). As a metric to characterize the oscillation strength in MUA

responses, we have taken the amplitude of the oscillation from the fitted function in the

correlograms (oscillation amplitude, see Methods). Comparisons across conditions have

shown significant differences for stimulus size for all contrast levels (p < 0.05 for the

stimulus sizes of 4°, 7°, 8°, 10°, 11° and 12°). Mean values and standard deviations for

oscillation amplitude and firing rates are given in Table 1.

To analyze how oscillation strength varied as a function of stimulus size we

compiled plots separately for the 3 luminance values studied (Figure 13). Oscillation

strength increased as a function of stimulus size non linearly, without reaching a

plateau. The level of responses, on the other hand, reached a maximum already for a

stimulus of 4°. Thus, in our data, oscillation strength was clearly independent of the

spike rate.

Synchronization in the LGN was always accompanied by oscillations. Figure 14

shows an example of a cross-correlation computed for MUA pairs with RFs separated

by about 3°. Recordings were obtained from different electrodes. Observe that the

modulation of the correlogram is very strong (oscillation frequency, 87 Hz; MAS, 0.76).

The shift-predictor control is flat indicating that oscillatory responses do not arise from


Figure 13. Stimulus size and luminance modulate fast oscillations in the retinogeniculate system. MUA recordings were obtained from the LGN of the anesthetized cat. (Left panels) Autocorrelation sliding window analysis show strong oscillatory responses to a high contrast circle of 10° diameter flashed over the RF (oscillation frequency at 78 Hz), while a 8° stimulus evoked weak oscillations for the same contrast. Sliding window size, 200 ms; step, 100 ms. (Right plots) Population data, traces show response levels (firing rate, upper plot) and oscillation amplitude (lower plot) as function of stimulus size. Notice that rates and oscillation strength have different trends. Plots show data obtained for 3 different stimulus luminance levels (175, 80, 50 Lux). Analysis window of 1000 ms, as indicated by the dashed line at the bottom of the sliding window plots. In this study correlograms were normalized by firing rates (normalized coincidences). Arrows indicate the points corresponding to the data used for the sliding window analysis (corresponding respectively to stimulus sizes of 8° and of 10°).

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Figure 14. Synchronous oscillatory responses to a circular light stimulus for a pair of MUA ON-cells with corresponding RFs separated by approximately 3°. Panels show crosscorrelograms computed with a bin of 1 ms. Oscillation frequency, 87 Hz; Modulation amplitude (MAS), 0.76. Shuffling the correlation by 1-trial (shift-predictor) gives a flat function, indicating that correlation arises from interactions in the retinal networks. Fitted functions are displayed as black thin lines over the correlograms. Analysis window, 1000 ms. Stimulus was flashed over the RFs (size, 14°). (Upper inset) RF mappings obtained by means of a moving bar showed at 16 different directions (Pipa et al., 2012). Data from two independent electrode channels (RF maps were merged for visualization). Stimulus borders are indicated by the circle line on the RF mapping plots.

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stimulus coordination.

4.2 . BR E A K I N G S T I M U L U S C O N T I N U I T Y

To test whether stimulus continuity is relevant for supporting long-range

oscillatory interactions in the retina, we designed a stimulus that consisted of an annular

mask superimposed on circular stimuli of various sizes (center part of the stimulus had

5°, see Figure 10). The presence of a mask outside the borders of the RFs should hinder

oscillatory responses for the center part of the stimulus, because the vicinity being not

activated should not mediate synchronization. As shown in the plots of Figure 15,

annular masks had a profound effect on oscillation amplitude, even though the rates

remained the same (rates tend to decrease with stimulus size in both unmasked and

masked conditions; N= 24, 3 different experiments). Notably, in these experiments, the

effect of stimulus size on oscillation strength seemed to reach a plateau for a stimulus of

10°.

Figure 15. Breaking stimulus continuity disrupts oscillatory responses to a circle stimulus. (Left panels) Strong oscillatory responses to a high contrast circle of 9° (upper panel) are disrupted by a dark mask cutting off the central region of the stimulus over the RF (middle panel). Oscillation frequency, 99 Hz. These effects cannot be explained by a simple decrease in luminance level because oscillations are also disrupted for a larger stimulus (12°, lower panel). Sliding window size, 200 ms; step, 100 ms. (Right plots) Population data. Notice that oscillation amplitude is always lower for annular masked stimuli. Analysis window, 1000 ms (dashed line).

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Notice, however, that although oscillation strength was much diminished for the

masked stimuli, they did not vanish completely. In the traces of Figure 15 one sees a

clear trend for an increase in oscillation amplitude (without reaching a plateau) as a

function of the size of the peripheral part of the stimulus (the center part remains

unchanged). A possible confounding factor could be the stray-light of the CRT monitor.

It would be interesting to use different types o display (e.g.., fast LCD monitor) to

dispel these interferences. Results for annular masked stimuli are given in Table 2.

4.3 . PE R C O L AT I O N S T I M U L U S

To further study whether oscillation dynamics are related to stimulus

connectedness (Neuenschwander and Singer, 1996) we used the percolation stimulus

proposed by Stephens et al. (2006) (see Methods). The sliding window analysis

presented in Figure 16 shows an example of how oscillatory behavior changes as a

function of adding pixels in the image. Although one can see a sharp appearance of an

Figure 16. Percolation threshold does not lead to a phase transition in the oscillatory responses. (Left panels) Sliding window analysis show that oscillatory responses appear much later for the percolation stimulus (see Methods) as compared to the linear ramp circular stimulus (linear change in luminance, symbolized by a ramp over a circle). Arrows indicate the expected time of percolation threshold (at 60 % of elapsed time, see Stephens et al., 2006). Notice that in this example a transition in oscillatory behavior happens just before the point in time of percolation threshold (Right plot). Population data for oscillation onset from the start of the stimulus. Although oscillatory responses took longer to appear for the percolation stimulus as compared to the linear ramp stimulus, oscillation onsets were clearly below the expected time of percolation threshold (depicted as a dashed line in the plot).

1800

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oscillatory pattern at 1200 ms, at this point in time the stimulus has not yet reached

percolation threshold (1800 ms). Population data confirmed these results. In all cases

studied oscillations appeared before the expected time of percolation (mean of 640 ms

after stimulus onset). Thus, our population results do not support the predictions by

Stephens et al. (2006). In their model, a clear phase transition in oscillation should

occur just before the point of percolation. These was simply not the case in most of our

experiments.

4.4 . OS C I L L AT O RY R E S P O N S E S T O DY N A M I C A L S T I M U L I

An important goal in this study was to examine whether retinal oscillations

appear also for responses to naturalistic stimulus (Figure 17). We have used two

different gray-scale natural scene movies (200 gray values, referred as PanLab and

Medikament movies, same stimuli used in the study of Haslinger et al., 2012). These

movies were chosen because they contain scenes with relatively large uniform surfaces

(scenes of the laboratory with high spatial and temporal correlation). In addition we

built a simplified, binary version of these two films (2 luminance values, black and

white). Sliding window analysis revealed epochs of strong oscillatory responses (ON-

responses) probably coinciding with the appearance of large uniform segments or blobs

in the image (quantitative analysis is missing). Oscillations were much stronger for the

binary than for the gray-level movies. Notice that oscillatory responses were not graded,

but seemed to appear in an all-or-none fashion. This probably happened because a given

segment in the image needed to reach a certain size threshold to trigger an oscillatory

response. Interestingly, during the stimulus presentation, oscillation frequencies

remained constant across the oscillatory episodes.

Responses to flickering stimuli (random checkerboards), on the contrary, always

evoked non-oscillatory responses (Figure 17).


Figure 17. Fast oscillations appear only briefly for homogeneous image segments or blobs in natural scene movies. (Left panels) LabPan movie. Oscillation frequency for gray-level movie, 92 Hz; for binary movie, 86 Hz. (Right panels) Medikament movie. Oscillation frequency for gray-level movie, 92 Hz; for binary movie, 81 Hz. Note that oscillation frequencies are fairly constant along the trial both for the gray-level and for the binary stimulus (responses to the Medikament movie). Random flickering checkerboards evoked no oscillations. Sliding window, 200 ms; step, 100 ms.

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Figure 18. Oscillatory responses to gratings as function of stimulus temporal frequency (0.3 Hz, 1.25 Hz, 3.0 Hz; same spatial frequency at 1.0 cycle per degree, different velocities). (Left panels) Sliding window analysis. Oscillation frequencies were of 80 Hz, 87 Hz and 89 Hz (analysis window placed at 2000 ms). Observe the phase-reseting of the oscillatory responses for the 3.0 Hz gratings. Increase of oscillation frequency as a function of stimulus speed confirm data obtained for moving bars (Neuenschwander et al., 1999). Interestingly, the gamma oscillations in monkey V1 exhibit a similar behavior (Lima et al., 2010). Sliding window, 200 ms; step, 100 ms. (Right panels) Response histograms were computed with a resolution of 10 ms, MUA data. The transient components of the responses increase for the faster stimulus, although overall the mean firing rates are similar for all the three temporal frequencies studied.

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Analysis of the data obtained for grating stimuli revealed that retinal oscillations

are sensitive to the temporal frequency of the stimulus (see examples in Figure 18).

Faster stimuli led to faster oscillation frequencies in the responses. Observe the

systematic decrease of oscillation frequency locked to the response onset (when the

individual bars of the gratings crossed the RF).

4.5 . DE P E N D E N C I E S O N H A L O T H A N E L E V E L S

An unexpected discovery in our study was that retinal oscillations are very

dependent on the halothane anesthesia.

Figure 19 documents this observation for responses to a large circular stimulus.

A decrease in the concentration of halothane (expired partial concentration) led to a

rapid fading of the oscillatory responses. This effects seemed to be non linear.

Decreasing the halothane level from 1.0% to 0.5% caused nearly complete vanishing of

the modulation in the correlograms. It is important to mention that before removing the

halothane from the ventilation mixture (70% N2O and 30% O2), we had supplemented

Figure 19. Halothane anesthesia greatly affects fast oscillations in the retina. ON-responses obtained for for circular light stimuli (sizes of 14° and 10°). (Left panels) In the absence of halothane (concentration of 0.0%), the oscillatory patterning in the responses completely vanished, without significant changes in rates. Sliding window, 200 ms; step, 100 ms (Right plots) Autocorrelation analysis for different epochs after turning the halothane vaporizer to 0.0 % (wash out recording session). Correlograms were normalized by the firing rates. Oscillation frequency, 75 Hz; MAS, 1.51. Analysis window, 1000 ms (indicated by a dashed line at the bottom of the sliding window plots). Notice that oscillation strength does not seem to be linearly correlated with halothane concentration. Data from the same recording sites. Halothane concentration levels were obtained from readings of the life monitoring system (expiratory gas mixture).

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the anesthesia with ketamine (10 mg/kg body weight, i.m.) or fentanyl (0.005 mg/kg

body weight, i.v.). This was carried out to assure that the cats suffered no discomfort

(vital parameters were continuously monitored during the recording sessions). None of

these supplementary agents changed the oscillatory behavior of the responses (data not

shown).

The effects of withdraw of halothane for responses to a linear ramp stimulus are

shown in Figure 20. Lowering the concentration from 1.0% to 0.4% to 0.2% led to a

rapid decrease in oscillation modulation, despite a pronounced increase in the overall

responsiveness of the cells. Interestingly, in this case there was a clear change in the

oscillation dynamics, with the appearance of weak slow rhythmic components (about 20

Hz) at 0.2% of halothane.

Figure 20. Effects of halothane on LGN responses to a linear ramp stimulus. (Left panels) Sliding window analysis. Oscillation frequency of 72 Hz. Notice that oscillation frequency remains unchanged for the different concentration levels of halothane. Sliding window, 200 ms; step, 100 ms. (Right panels) MUA response histograms. Bin, 10 ms. In this particular example halothane anesthesia diminished substantially the overall response rates (49% decrease in firing rates). Even though populations in the retina fire less during halothane anesthesia, they oscillate strongly. Data from a different cat.

12°

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Plots of oscillation strength as a function of halothane levels for responses to a

linear ramp stimulus are presented in Figure 21. Oscillation amplitude decreases

remarkably as a function of halothane concentration levels, despite a slight increase in

firing rates. Thus, removal of halothane lessens inhibition in overall activity, resulting in

a new non-oscillatory dynamics.

Similarly, retinogeniculate oscillatory responses to natural scene movies vanish

in absence of halothane (Figures 22 and 23). As in the data presented in Figures 19 and

21 the dependencies on the halothane concentration seemed to be non linear. At 0.6 %

halothane concentration the oscillatory components in the responses to the movie

(Medikament) were nearly absent.

Finally, we document in Figures 24 and 25 the halothane-withdrawal effects for

grating stimuli.

In all cases studied the restoring halothane concentration to the original levels

(from 0.8 to 1.2 %) brought back the oscillatory responses in the LGN.

Figure 21. Oscillation strength as a function of halothane levels for responses to a linear ramp stimulus. Population data. Two slopes were used for luminance ramping, one fast with duration of 2000 ms (gray traces) and another slower with duration of 3000 ms (red traces). (Upper plot) Firing rates increase slightly for low values of halothane. (Lower plot) Oscillation amplitude decreases remarkably as a function of halothane concentration levels. This trend does not seem to be linear. Recovery points are shown to the right (after increasing the halothane to previous levels).

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Figure 22. Retinal oscillations in LGN responses to natural scene movies vanish in absence of halothane. Observe that the impact on oscillation strength does not correlate linearly with concentration levels of halothane (already at 0.6% oscillations ceased almost entirely). Firing rates are slightly increased upon halothane removal, indicating that halothane causes a slight depression in the general activity of the retinogeniculate system. Oscillation frequency, 75 Hz. MAS, 1.03. Sliding window, 200 ms; step, 100 ms. Analysis window for computation of the correlograms, 500 ms. Data obtained for the Medikament movie.

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Figure 23. Oscillation strength as a function of halothane levels for responses to natural scene movies (gray-level and binary stimuli). Population data. Oscillation amplitude decreases steadily as a function of halothane levels. Recovery points are shown to the right (after increasing the halothane to previous levels). Data obtained for the LabPan movie.

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Figure 24. Retinal oscillations in LGN responses to grating stimuli (temporal frequency of 3.0 Hz). Notice the gradual disappearance of the oscillatory modulation in the responses as halothane concentration levels decrease. Mean average rates remain unchanged. Oscillation frequency, 89 Hz. Sliding window, 200 ms; step, 100 ms. Response histograms bin, 10 ms.

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Figure 25. Oscillation strength as a function of halothane levels for responses to grating stimuli (temporal frequencies of 0.3 Hz, 1.25 Hz and 3.0 Hz). Population data. Oscillation amplitude decreases as a function of halothane levels. Recovery points are shown to the right.

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Finally, we found evidence that isoflurane, an anesthetic agent with similar

pharmacological properties to halothane, is also capable of inducing strong oscillations

in the retinogeniculate pathways (Figure 26).

Taken together, these findings indicate that halogenated anesthetics have a

profound effect in the temporal patterning of the responses. As discussed below these

results question whether fast oscillations in the retina contribute to encode visual

information.

Figure 26. Isoflurane, an halogenated anesthetic similar to halothane, is also responsible for the appearance of fast oscillations in the retinogeniculate system. Stimulus consisted of a high contrast light circle flashed over the RFs (stimulus size, 12°). Oscillation frequency, 76 Hz. MAS, 0.38. Data was obtained after decreasing halothane level to 0.0 % and subsequently, after approximately 30 min washing period, delivering isoflurane at 1.0% (with another vaporizer).

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1. DI S C U S S I O N

The primary goal of the present study was to investigate the role of fast retinal

oscillations in encoding visual information. Our study bears on the LGN for various

reasons. First, it is much simpler to record from the thalamus. To record directly from

the retina implies positioning the electrodes through the sclera, a complex and difficult

procedure. Moreover, data from the LGN will allow us to easily draw comparisons

between anesthetized and behaving cats in future experiments, as recording directly

from the eye in non-anesthetized animals is nearly impossible. Second, it is well

established that the temporal pattern of neuronal activity originated in the retina is

transmitted to the LGN (Doty and Kimura, 1963; Laufer and Verzeano, 1967; Arnett,

1975; Neuenschwander et al., 1996; Castelo-Branco et al., 1998). In these studies in the

cat, synchronization of oscillatory responses was observed only for LGN cells receiving

inputs from the same eye, independent whether the cells were located in the same LGN

or in the LGNs of the two hemispheres (Figure 6). Accordingly, simultaneous

recordings from the retina, LGN and cortex (Castelo-Branco et al., 1988) have shown

that correlated activity from the retina propagates to the primary visual cortex (areas

A17 and A18). Since the cat retina does not receive centrifugal projections and there are

no thalamic interactions across the midline we can conclude that the synchronization

mechanism of oscillatory responses in the LGN resides in the retina (see discussion in

Neuenschwander et al., 2002). Moreover, as shown recently, most LGN neurons have

one retinal ganglion cell input that accounts for nearly all LGN spikes sent to the cortex

(Sincich et al., 2007). The retina and the LGN have approximately the same number of

cells and most LGN principal neurons receive inputs from a single retinal ganglion cell.

Thus, spiking activity in the LGN faithfully follows the driving retinogeniculate inputs.

For these reasons, in this study we attribute our main conclusions to the retina,

since we know that the fast oscillations we describe here in the LGN are actually

originated in the retina.


5.1 ST I M U L U S D E P E N D E N C I E S

Since Kuffler (1953) it has been known that the size of simple stimuli modulates

the responses of a given neuron both in the retina and the LGN (Barlow et al., 1954;

Hubel and Wiesel, 1961). Small spots of light flashed over the center of the RFs evoke

strong responses. For larger stimuli reaching the surround regions of the RFs, however,

the responses diminish. Thus, response rates of single retinal ganglion cells are unlikely

to be appropriate for encoding stimulus size.

The discovery of stimulus-dependent oscillatory responses in the retina raised an

interesting alternative hypothesis. Stimulus global characteristics such as size and

continuity could be represented by a temporal code. In the cat, it has been demonstrated

that Ganzfeld illumination induces strong oscillatory responses in the retina and LGN

(Doty and Kimura, 1963; Laufer and Verzeano, 1967). Later on, Neuenschwander and

collaborators have shown that oscillation strength is correlated with stimulus size, using

light rectangles flashed over the RFs (Neuenschwander et al., 1999; Stephens et al.,

2006). Our study confirm these findings. As shown in Figure 13, only circle sizes more

than 5° degrees (approximately 3X the size of the RFs) were able to induce oscillatory

patterns in the responses. Thus, a critical mass of activated cells must be reached for the

emergence of oscillatory responses. It has been suggested that fast oscillations in the

retina arises from local interactions among retinal ganglion cells (Laufer and Verzeano,

1967; Neuenschwander et al., 1999). This may occur by the continuous phase-locking

across neighboring cells over large extensions in the retinal mosaic (see example of

correlated activity in Figure 5).

To test this hypothesis, we used an annular mask to break continuity in space

(Figure 14). We expected to see no oscillations for the cells responding to the central

part of the stimulus (stimuli smaller than 5 degrees induced only weak oscillations, see

also Figure 13), as they should be functionally disconnected from the large interacting

populations responding to the peripheral part. The population data presented in Figure

14 show, however, that oscillations may persist for annular stimuli with large surrounds,

although in general there was a significant decrease in the oscillatory behavior of the

responses for the masked stimuli as compared to the controls. As mentioned before, a


possible confounding factor is the stray-light coming from the monitor, and controls

should be made to verify this problem.

Nevertheless, it is possible to conceive that synchronization of oscillatory

responses in the retina have a different mechanism than local interactions of contiguous

cells. Amacrine cells with large dendritic arbors are a likely candidate to mediate these

interactions. In principle, they could functionally bridge regions across considerable

distances in the retinal surface, working as hubs in a small-world network (Figure 27).

Besides the masked circular stimuli, we used the percolation stimulus proposed

by Stephens et al. (2006) to test whether continuity in space is critical for generation of

coherent oscillations in the retina. The central idea here is that a phase transition exists

when local interactions among neighboring cells entrain the whole network to a global

synchronous dynamics. This point should correspond to the percolation threshold of the

stimulus (60% of the stimulus duration). A this point the clusters of light pixels in the

image are randomly connected throughout the image (connectedness is independent of

shape). The population data presented in Figure 16, however, do not seem to support

this model since the onset of oscillatory responses does not coincided with the expected

timing of percolation threshold. A confounding factor in these experiments is the

increase of global luminance as more pixels are added. New experiments are needed to

exclude these problems.

Figure 27. Example of a small-world network (National Institute of Organization Dynamics in Australia). A similar organization in the retina would explain saltatory long-range horizontal interactions. Amacrine cells with their large dendritic fields could work as hubs in the network.


Finally, we have applied a variety of complex stimuli to follow the dynamics of

the retinal oscillatory responses in more naturalistic conditions. As shown in the Figure

17, oscillations in response to natural scene stimuli appeared only for short epochs,

likely when large connected segments in the image (e.g., surfaces or the faces of a

geometrical object) covered the RFs. Accordingly, oscillations were stronger for the

binary images, which had larger uniform segments than the gray-scale images. It would

be interesting to match the precise position of the RFs over the image sequences in

order to follow the oscillatory behavior as function of blob size. Qualitatively, we can

see that the critical blob size for triggering oscillations is very large. In this sense,

encoding of size would be in place only after a certain threshold is exceeded.

An open issue in this study is how the timing of the stimulus affects the retinal

oscillations. We could observe that oscillatory responses vanished for dynamical

flickering stimuli (Figure 17). Moreover, in additional experiments with large flickering

stimuli of various temporal frequencies we could verify that oscillatory responses are

disrupted for stimuli faster than 20 Hz (data not shown). Thus, oscillatory responses in

the retina are in general limited to relatively large and slow stimuli. From a theoretical

perspective, this would represent a severe limitation for the encoding bandwidth of the

system.

At the present we do not know whether retinal oscillations are meaningful for

cortical processing. The precise timing of synchronous spikes in the LGN are important

for effectively driving cortical targets (Alonso et al., 1996; Stanley et al., 2012). It is

possible that oscillatory inputs to the cortical neurons convey information about

-π 0 π

Figure 28. Visual processing using coupled oscillators (modified from Koepsell et al., 2010). (Left) Sample image (from the Berkeley segmentation database). (Middle) Phase representation of the image in a network of coupled phase oscillators with oscillation frequency, coupling parameters depending on similarity of pixel luminance value (model proposed by Koepsell et al., 2010). (Right) Information represented by cells spiking at a specific oscillation phase. Phase-synchronization of oscillatory responses may encode image contours relevant for further cortical processing.


homogeneous image segments and contribute to scene segmentation, as hypothesized in

a few theoretical studies (Stephens et al., 2006; Koepsell et al., 2010; Figure 28). It will

be important to further extend these conclusions, which were drawn from experiments

in the anesthetized and paralyzed cat, to the behaving monkey.

5.2 EF F E C T S O F H A L O T H A N E A N E S T H E S I A

We found abundant evidence, with a variety of visual stimuli, that retinal

oscillations in the cat are dependent on halothane anesthesia (Figures 19 to 24). Several

seminal studies on temporal coding in the retinogeniculate system have used

anesthetized animals as experimental model, applying as anesthetic agent either sodium

thiopental (Doty and Kimura, 1963; Laufer and Verzeano, 1967; Reinagel and Reid,

2000; Butts et al., 2007; Desbordes et al., 2008), halothane (Neuenschwander et al.,

1996; Castelo-Branco et al., 1998) or isoflurane (Ito et al., 2010). Halogenated

anesthetics, such as halothane, are known to act directly on GABAA receptors through

an agonist effect by prolonging the decay of inhibitory postsynaptic currents and

increasing IPSP amplitudes (Li et al., 2000; Nishikawa and Maclver, 2000). Halothane

is known to enhance receptor affinity (Krasowski and Harrison, 1999) and also inhibits

GABA uptake (Kotani and Akaike, 2013). Generally, inhibition mediated by GABAA

receptors plays an important role in the generation of high frequency oscillations in the

hippocampal (Wang and Buzsáki, 1996; see review in Buzsáki and Wang, 2012) and the

cortical (Hasenstaub et al., 2005) networks. Similarly, in the thalamus, inhibitory cells

are key players in providing inhibition to thalamocortical cells and therefore are directly

involved in the generation of thalamic oscillations (Steriade et al., 1996). Halothane was

also shown to facilitate evoked gamma in the rat visual cortex (Imas et al., 2004).

Knowing that GABAA receptors are abundant in the outer plexiform layer

(OPL) of the retina (Hughes et al., 1991 ; Vardi et al., 1992), it is natural to think that

these agents interfere with the delicate balance of excitation/inhibition in the networks.

In fact, in the isolated retina of the frog, applying bicuculline (a GABAA-receptor

antagonist) suppresses synchronous oscillations of the dimming detectors (Ishikane et

al, 1999; Arai et al., 2004). However, the mechanism for the generation of the fast

oscillations in the retina is still largely unknown. Why augmenting inhibition leads to


strong oscillations in the retinal circuits? Are the retinal oscillations generated in the

outer plexiform layer (OPL)? These are important questions that should be addressed by

in-vivo pharmacological studies.

Even if halothane is not directly responsible for the retinal oscillation it and

other anesthetics like it clearly have a facilitatory effect, which must be factored in to

any model using data derived using an anesthetized preparation. Interestingly, the

dependency of oscillation amplitude on halothane concentration seemed to be non-

linear. For example in Figure 19 oscillatory responses are already absent for a

concentration level of 0.5%. Similarly, data from a different cat show that at a

concentration of 0.6% the oscillations are nearly vanished for responses to natural scene

stimuli (Figure 22). Possibly a phase transition point must be reached for the emergence

of oscillatory pattens in the retinal networks. Importantly, the oscillatory dynamics

cannot be explained by firing rates (ringing of maximal responses). Actually, in our

experiments, halothane generally caused a slight decrease in response rates.

In this study, we show that isoflurane also evoked oscillatory responses with the

same temporal characteristics as halothane. Thus, our findings can be generalized to

halogenated anesthetics. This is important since these two agents have been routinely

used in many studies of temporal coding at various levels in the sensory systems (e.g.,

Neuenschwander and Singer, 1996; Ito et al. 2010).

Finally, a few studies have reported that retinal oscillations occur also for non-

anesthetized and paralyzed cats and monkeys (after transpontine transection) (Doty and

Kimura, 1963), which is at odds with our results. In these early experiments, however,

the stimulus conditions were very different from ours. Visual stimulation was generally

made by illuminating the whole visual field using light flashes (Doty and Kimura, 1963;

Laufer and Verzeano, 1967). It remains to be investigated whether halothane is

responsible to the generation of oscillations in the retina, or if it simply amplifies them.

These unresolved issues should be clarified by recordings in awake behaving animals.


2. CO N C L U S I O N

In this study we recorded MUA responses in the LGN in order to investigate the

role of fast retinal oscillations in visual encoding. In accord with previous studies,

stimulus size and luminance have a strong effect on the modulation of oscillation

strength. Moreover, oscillations are disrupted by changes in stimulus uniformity in

space and time. Images made of connected clusters of pixels with random shapes are

capable of inducing strong oscillatory responses. In our experiments, however, we were

not able to demonstrate the appearance of a phase transition in the oscillatory behavior

of the responses at stimulus percolation threshold, as predicted by Stephens et al., 2006.

Stimuli with complex temporal and spatial dynamics, such as natural scene movies, are

also able to elicit oscillatory responses, but only for short epochs. Overall, these results

support the idea that retinal oscillations serve for encoding of large, continuous and

uniform segments in the visual image.

A surprising finding in this study was that retinal oscillations are highly

dependent on halothane anesthesia levels. In the absence of halothane, oscillatory

activity was rarely induced by the visual stimuli even when they were optimal. The

same findings were obtained for isoflurane, which has similar pharmacological

properties. Taking into consideration these new and unexpected results we question

whether feedfoward oscillations in the early visual system are simply due to an

imbalance between excitation and inhibition in the retinal circuits created by the use of

halogenated anesthetics. Are retinal oscillations (at least in the cat) an artifact from the

halothane anesthesia? Further studies in awake behaving animals are necessary to

extend these conclusions.


3. RE F E R E N C E S

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4. TA B L E S

Table 1 . Effects of stimulus size on oscillation amplitude of LGN responses. Means and standard deviations for oscillation amplitude and firing rate for circles and its related annular mask stimuli for 5 different sizes and the first two circles.

Size (degrees) Mean SD Mean SDHigh luminance Osc. Amplitude Osc. Amplitude Rate Rate

2° 0,0025 0,0071 114,91 80,863° 0,0025 0,0071 150,85 100,974° 0,0213 0,0196 174,47 103,016° 0,0350 0,0273 179,37 104,657° 0,0263 0,0297 184,24 100,548° 0,0388 0,0340 175,71 95,0510° 0,0588 0,0309 172,91 94,6711° 0,0788 0,0432 167,79 87,6612° 0,1075 0,0528 171,87 87,513° 0,2300 0,2986 176,12 90,39

Medium luminance2° 0,0038 0,0106 90,81 74,643° 0,0025 0,0071 132,26 93,224° 0,0088 0,0181 160,81 103,026° 0,0188 0,0210 168,60 100,947° 0,0325 0,0212 170,81 98,098° 0,0400 0,0262 169,06 96,1810° 0,0375 0,0328 167,85 94,6811° 0,0538 0,0333 170,72 87,9312° 0,0725 0,0373 167,99 88,3513° 0,1063 0,0478 173,56 93,01

Low luminance2° 0,0000 0,0000 61,50 38,663° 0,0025 0,0071 93,01 55,744° 0,0013 0,0035 118,06 64,056° 0,0113 0,0181 127,89 70,077° 0,0050 0,0141 127,60 71,898° 0,0100 0,0185 132,40 77,4510° 0,0100 0,0214 134,59 80,2211° 0,0338 0,0292 130,34 81,4312° 0,0488 0,0295 130,5 78,5113° 0,0638 0,0478 127,16 79,05


Table 2 . Results for the annular mask protocol. Comparisons between masked and unmasked stimuli (circle). Mean values and standard deviation for oscillation amplitude and firing rate of the 10 different sizes at 3 different levels of luminance.

Protocol Mean SD Mean SDStimulus size Osc. Amplitude Osc. Amplitude Rate Rate

3° - Circle 0,0050 0,0117 212,97 79,465° - Circle 0,0167 0,0235 212,83 80,837 °- Annular mask 0,0092 0,0138 200,29 75,217 ° - Circle 0,0467 0,0323 190,47 78,529 ° - Annular mask 0,0133 0,0156 177,00 70,169 ° - Circle 0,0608 0,0425 169,52 71,7010 ° - Annular mask 0,0292 0,0300 159,10 69,9410 ° - Circle 0,0767 0,0352 153,65 67,8612 ° - Annular mask 0,0383 0,0346 143,84 63,9612 ° - Circle 0,0758 0,0408 142,79 65,6214 ° - Annular mask 0,0533 0,0274 141,75 63,0614 °- Circle 0,0758 0,0337 134,48 60,94


5. AC K N O W L E D G M E N T S

Many thanks to my advisor Sergio Neuenschwander, who is a special scientist

and person with skills in many fields. A “key figure” on my formation as a scientist

during this process and an inspiring reference. I learned with him the beauty of making

figures for a manuscript.

I also wish to thank Kerstin E. Schmidt for gently providing the space and all

conditions for the experiments performed in this study.

Thanks to Edward J. Tehovnik for valuable discussions about science, and

Sergio Conde and Luiz Lana for data analysis support. Special thanks to Jailson, Monik

and Wittyla for animal care and assistance in the laboratory. I would like specially to

thank my colleagues in the Instituto do Cérebro - UFRN, where I had the opportunity to

learn a lot from professors and students in a very warm atmosphere. Thanks to Maria

Salete B. Freitag for her continuous support and assistance, and for being such a strong

personal reference, and also to my close friends for support.

Many thanks to Prof. Jerome Baron and Prof. Adriano Tort for kindly accepting

to evaluate my thesis work, and Akaline Araújo for her secretary work at the PG-Neuro,

UFRN.

During this work I was supported by CAPES (Master‘s Degree Scholarship),

and the laboratory of Sergio Neuenschwander was supported by CNPq and a

collaboration between the Brain Institute-UFRN and the Max-Planck Institute for Brain

Research, Frankfurt.


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FABIO FREITAG - Federal University of Rio Grande do Norte · fabio freitag natal, 2013. universidade federal do rio grande do norte programa de pÓs-graduaÇÃo em neurociÊncias