optic nerve striate cortex (v1) hubel & wiesel 1 deg

optic nerve

StriateCortex

(V1)

Hubel & Wiesel

1 deg

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

Sclar & Freeman 1982

response(spikes/s)

orientation

80

0

-25º +25º0º

40

20

60

Hallem & Carlson 2006

amines

lactones

acids

sulfur

terpenes

aldehydes

ketones

aromatics

alcohols

esters

Odorant Receptors

StriateCortex

(V1)

1 deg

IT face cell

Tsao et al. 2006

xxxx

xxxx

xxxx

Hubel & Wiesel 1962

LGN

Striate Cortex

X = excitation = inhibition

++

+

R1

Sclar & Freeman 1982

response(spikes/s)

orientation

80% contrast

40% contrast

80

0

-25º +25º0º

McAdams & Maunsell 1999

attend in

attend out

1.0

0.5

0.0-90º -60º -30º 0º 30º 60º 90º

V4response

orientation

Kohn & Movshon 2004

0

50

100

-180 1800direction of motion

spikes/s

adaptingdirection

waterfall illusion

140 spikes/s

Early: 65 to 85 ms(2 or 3 spikes)

= 45° = 90° = 135°

Late: >150 ms

140 spikes/s

Pack & Born 2001

Lorençeau et al. 1993

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


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)


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


P. Dayan & L.F. Abbott Theoretical Neuroscience MIT Press


P. Dayan & L.F. Abbott Theoretical Neuroscience MIT Press

v

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.


There is lots of evidence that activity in nearby neurons is often not independent.

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

discriminant

Linear discriminant analysis: a method for classifying samples within a data set based on drawing a linear boundary (a line or plane) which best separates different categories of samples.

optic nerve striate cortex (v1) hubel & wiesel 1 deg

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