GPS in the brain: about the Nobel prizes of 2014geza.kzoo.edu/~erdi/leckek/tu2.pdf · GPS in the brain: about the Nobel prizes of 2014 P eter E rdi [email protected] Henry R. Luce Professor

GPS in the brain: about the Nobel prizesof 2014

Peter [email protected]

Henry R. Luce ProfessorCenter for Complex Systems Studies

Kalamazoo Collegehttp://people.kzoo.edu/ perdi/

andInstitue for Particle and Nuclear Physics, Wigner Research Centre, Hungarian Academy

of Sciences, Budapesthttp://cneuro.rmki.kfki.hu/

– Typeset by FoilTEX – 1

The Nobel Prize in Physiology or Medicine 2014

© 2014 The Nobel Committee for Physiology or Medicine The Nobel Prize® and the Nobel Prize® medal design mark are registered trademarks of the Nobel Foundation

Illustration and layout: Mattias Karlén

John O’Keefe discovered, in 1971, that certain nerve cells in the brain were activated when a rat assumed a particular place in the environment. Other nerve cells were activated at other places. He proposed that these “place cells” build up an inner map of the environment. Place cells are located in a part of the brain called the hippocampus.

Grid cells, together with other cells in the entorhinal cortex that recognize the direction of the head of the animal and the border of the room, form networks with the place cells in the hippocampus. This circuitry constitutes a comprehensive positioning system, an inner GPS, in the brain. The positioning system in the human brain appears to have similar components as those of the rat brain.

May-Britt och Edvard I. Moser discovered in 2005 that other nerve cells in a nearby part of the brain, the entorhinal cortex, were activated when the rat passed certain locations. Together, these locations formed a hexagonal grid, each “grid cell” reacting in a unique spatial pattern. Collectively, these grid cells form a coordinate system that allows for spatial navigation.

Fig. 1

Fig. 2

Fig. 3


Spatial information processing

• HIPPOCAMPUS

• COGNITIVE MAPS

• PLACE CELLS

• TEMPORAL CODING of LOCATION

• BOUNDARY CELLS

• HEAD DIRECTION CELLS

• DISTANCE MEASURMENT: GRID CELLS

• A HYBRID OSCILLATORY INTERFERENCE/CONTINUOUS ATTRACTOR NET-WORK MODEL OF GRID CELL FIRING

• NAVIGATION STRATEGIES: PATH INTEGRATION, MAP-BASED NAVIGATION


HIPPOCAMPUS

.

��

1��/��

�&2

��

�&3

��

��455.3555�352

�� 6555�352

��

��785�352

�� 9455�352

��

��345�352

�� 7255�352

��)�� !1��)��

��85.3551�38

�!1�8.35�352

�!1�352

-��

�� &2:�&3

�� 1/:�&2:�&3

�� -�:1/:�&2:�&3:��,

��,��

Figure 1: Hippocampal circuitry I

• cell numbers

• convergence divergence num-bers

• velocity of activity propagation


HIPPOCAMPUS

.

Figure 2: Hippocampal circuitry II.

• Microcircuit organization

• inhibitory motiffs

• circuit and transmitters

• Mueller and Remy: SynapticNeurosci., 30 September 2014


COGNITIVE MAPS

COGNITIVE MAPS INRATS AND MEN E. C.Tolman 1948 ”We believethat in the course of learn-ing, something like a fieldmap of the environmentgets established in therat’s brain . . . The stimuli. . . are usually worked over. . . . into a tentative,cognitive-like map of theenvironment. And it is thistentative map, indicatingroutes and paths and en-vironmental relationships,which finally determineswhat responses, if any, theanimal will finally release.”

.


.

Place cells and cognitive maps

Different cells become

active in different places

O’Keefe NL, p12


TEMPORAL CODING of LOCATION

�� &-�(1-��

��

��&�-�(1- ��

1��,��

��

&��,��,��,��*��

� 0�� -�� ,��)��


O'Keefe & Recce, Hippocampus 1993

��+��

��+�� *��

$�� ,��*��*�� >�)�� ?

��


TEMPORAL CODING of LOCATION

��

��

�$�� .��*��

�$��

�%��

frequency~

Ansatz:

position

speed

phase~

#�� ,��,��


BOUNDARY CELLS

Figure 3: The BVC model. A BVC responds maximally when a boundary is perceived

at a preferred distance and allocentric direction from the animal, regardless of the animal’s

heading direction. A, The receptive field of a BVC tuned to respond to a barrier at a short

distance east-northeast from the animal. B, BVCs tuned to respond to barriers farther from

the animal will have broader receptive fields. C, The firing field (firing rate as a function of

the animal’s location; top) for a BVC with a receptive field tuned to respond to a boundary

at a short distance to the east (bottom). D, Predicted firing fields in different environments

for the BVC shown in C. Insertion of a barrier causes a doubling of the field (bottom right

panel). Figures are adapted from Hartley et al. (2000) and Barry and Burgess (2007). From

C. Lever: Journal of Neuroscience, 5 August 2009, 29(31): 9771-9777)


HEAD DIRECTION CELLS

”. . . a particular neuron might

discharge whenever the animal

points its head northeast, inde-

pendent of its location. In this

way, head direction cells are sim-

ilar to a compass in that their

discharge is always tuned to a

particular direction and can fire

at any location provided the an-

imalas head is facing the appro-

priate direction. However, unlike

a compass, head direction cells

are not dependent on the Earthas

geomagnetic field, but rather on

landmarks and self-motion cues,

such as vestibular and proprio-

ceptive cues.”

Taube, J: Scholarpedia

Figure 4: HD cell firing: Firing ratevs. HD plot depicting various pa-rameters used to characterize thefiring properties of HD cells.


DISTANCE MEASURMENT: GRID CELLS

The fields formed a grid that covered the entire space available to the animal. We called them grid cells

22

0 c

m w

ide

bo

x

Hafting et al. (2005).

Nature 436:801-806

Entorhinal cells had spatial fields with a periodic hexagonal structure

Stensola et al. Nature, 492, 72-78 (2012)

T. Hafting, M. Fyhn, S. Molden

Figure 5: E Moser Nobel lecture. p 7


DISTANCE MEASURMENT: GRID CELLS

Phase, scale and orientation may vary between grid cells. How are these variations organized in anatomical space?

Scale

Grid cells have at least three dimensions of variation

Figure 6: E Moser Nobel lecture. p 8


A HYBRID OSCILLATORY INTERFERENCE/CONTINUOUSATTRACTOR NETWORK MODEL OF GRID CELL FIRING

Figure 7:

Figure 8:

Figure 9: Toroidal CANN ofgrid cells (McNaughton etal. 2006)

• Continuous attractor network:A. Samsonovich: http://www.

scholarpedia.org/article/

Continuous_attractor_network

• HD cell is active when the rat’s head isoriented in a specific absolute directionin the environment,

• a ”bump”, called an activity packet,

• each trajectory of the system attractedby a continuous attractor reaches onepoint of the continuous attractor in afinite time and stays there forever if noperturbation is applied;

• billiard ball in a pool

• active cognitive map has a single locusof activity that can be positioned virtu-ally at any its point, like a billiard ballin a pool.


http://www.scholarpedia.org/article/Continuous_attractor_network




Figure 10:

”Activity packet on a chart observedin the rat hippocampus (from Sam-sonovich and McNaughton 1997).Units on horizontal axes are cen-timeters. The animal is locatedat the center of the square and ismoving to the left and toward theviewer. The same activity of thesame units distributed on a differ-ent chart would show a ”white noise”pattern.”



Figure 11: Grid cell networkconnectivity, recurrent inhibitoryweight profile, and excitatory inputprofiles. from Bush D & Burgess



”A hybrid OI and CAN model. a, In the absence of rhythmicinput from VCOs: a stable network state that is character-ized by a single activity bump across the topographically or-ganized sheet of cells. b, The power spectrum: the lack ofrhythmicity in the spike trains during formation and mainte-nance of the activity bump in the absence of VCO inputs.c, Spatially tuned rhythmic input from VCOs breaks theinput symmetry of uniform feedforward excitation to thegrid cell network so that the single, stable activity bump ismore rapidly generated in a location dictated by the inter-ference pattern. d, The power spectrum of the spike trainautocorrelogram averaged across all active cells illustrat-ing theta rhythmicity in the spike trains during formationand maintenance of the activity bump in the presence ofVCO inputs. e, Simulations of the hybrid OI/CAN modelin a 2D arena. Input from VCOs determine the locationof the activity bump and integrate movement over time,thereby shifting its location according to self-motion. ei,Path taken by the animal (gray) and the location of spikesfired by a typical grid cell (red). eii, Smoothed firing ratemap. eiii, Smoothed spatial autocorrelation. eiv, Meantemporal autocorrelation, illustrating that burst-firing fre-quency is higher than baseline theta frequency (marked byred lines), which suggests that phase precession is presentin these simulations. f: grid cell activities. g. Group datain 1D arena”


NAVIGATION STRATEGIES: PATH INTEGRATION,MAP-BASED NAVIGATION

Figure 12: Memory, navigationand theta rhythm in the hippocam-pal-entorhinal system Buzsaki andMoser, E

• Navigation and memory

• a. Path integration is based onself-referenced information

• b. Map-based navigation: rela-tionship among landmarks

• c. Episodic memory: mentaltravel in time and space : pathintegration?

• d. Semantic memory: land markbased navigation?


Documents

GPS in the brain: about the Nobel prizes of 2014geza.kzoo.edu/~erdi/leckek/tu2.pdf · GPS in the brain: about the Nobel prizes of 2014 P eter E rdi [email protected] Henry R. Luce Professor