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Butts et al. 2010
Spikes from an LGN Neuron: 62 Repeats of each stimulus
S1 S2 S3
Firi
ng R
ate
(H
z) time
trial #1
. . . .
62
Hallem & Carlson 2006
amines
lactones
acids
sulfur
terpenes
aldehydes
ketones
aromatics
alcohols
esters
Odorant Receptors
McAdams & Maunsell 1999
attend in
attend out
1.0
0.5
0.0-90º -60º -30º 0º 30º 60º 90º
V4response
orientation
140 spikes/s
Early: 65 to 85 ms(2 or 3 spikes)
= 45° = 90° = 135°
Late: >150 ms
140 spikes/s
Pack & Born 2001
Shadlen & Newsome 1994
trial #
sp/sec
time (ms)
Spikes from an MT Neuron: Identical Stimulus, 210 Repeats
Outline: neural coding lecture, pt 2
Population coding: a case study
Problems in understanding decoding
A cheat sheet for your homework assignment
Population coding: a case study
the cricket wind direction sensing system (first-order neurons)
Bacon & Murphey J. Physiol. 1984 352:601-623
Bacon & Murphey J. Physiol. 1984 352:601-623see http://www.biol.sc.edu/~vogt/courses/neuro/neurolabs.html
the cricket wind direction sensing system (second-order neurons)
Population coding: a case study
First-order neuron projections to the terminal ganglion are organized according to preferred wind direction.
There are four second-order neurons, and their dendrites are organized along the same divisions.
cell 1 cell 2 cell 3 cell 4
wind direction (degrees)
r / rmax
v
Population coding: a case study
P. Dayan & L.F. Abbott Theoretical Neuroscience MIT Press
Outline: neural coding lecture, pt 2
Population coding: a case study
Problems in understanding decoding
A cheat sheet for your homework assignment
Problems in understanding decodingWhich spike trains are being decoded to produce a percept?
Stimuli that produce different percepts should produce discernable changes in the spiking of the candidate neurons.
Differences in the spiking of candidate neurons should be sufficiently reliable to account for the acuity of the percept.
Noise in the activity of the candidate neurons should predict noise in the percept.
Artificially stimulating the candidate neurons should affect the percept.
Silencing or removing the candidate neurons should affect the percept.
Some criteria:
adapted from Parker & Newsome, Annu. Rev. Neurosci. 1998. 21:227–77.
Problems in understanding decodingIs information encoded in spike timing or spike rate?
adapted from Gollisch & Meister Science 2008 319:1108-11
In principle, either spike timing or spike rate can carry information about a stimulus.
Problems in understanding decodingHow much of a spike train should we consider?
Cury & Uchida Neuron 2010 68:570-585
Behavioral performance can help tell us what portion of a spike train we should consider.
Problems in understanding decodingIs the optimal decoding algorithm always used by the organism?
Johansson & Vallbo, J. Physiol. 1979 297:405-422
rapidly adapting
slowly adapting
rapidly adaptingtype 2
rapidly adaptingtype 1
psychophysical
The “lower envelope model”: Sensory thresholds are specified by the neuron that has the lowest threshold for stimulus in question.
Problems in understanding decodingIs the optimal decoding algorithm always used by the organism?
Johansson & Vallbo, J. Physiol. 1979 297:405-422
… but single neurons can exhibit better acuity than the organism as a whole!
rapidly adapting
slowly adapting
Problems in understanding decodingDoes each neuron provide independent information to the decoder?
The “pooling model”: Sensory thresholds can be improved by pooling independent information from many neurons.
Problems in understanding decodingDoes each neuron provide independent information to the decoder?
There is lots of evidence that activity in nearby neurons is often not independent.
Outline: neural coding lecture, pt 2
Population coding: a case study
Problems in understanding decoding
A cheat sheet for your homework assignment
principal component 1accounts for a largepart of the variance
(“body size”)
Principal component analysis: a method for reducing the dimensionality of a data set by defining a reduced set of axes which account for much of the variance in the data.
principal component 2accounts for a smaller
part of the variance