V1 Physiology
Questions
Hierarchies of RFs and visual areas
Is prediction equal to understanding?
Is predicting the mean responses enough?
General versus structural models?
What should a theory of V1 look like?
How is information represented in V1?
The cortex
Visual Areas in the Nonhuman Primate
Felleman & van Essen
Visual Areas in the Nonhuman Primate
Monkey LGN
Monkey LGN
Monkey V1 – Laminar organization
Monkey V1 – Inputs
Monkey V1 – Outputs
Monkey V1 – Oculodominance Columns
Monkey V1 – Oculodominance Columns
Monkey V1 – CO patches (or blobs)
Monkey V1 – Orientation Tuning
Receptive field
Monkey V1 – Orientation Columns
Monkey
V1 – Orientation
Map
Orientation
map
What generates the map?
How does it develop? What is the role of experience?
What is its functional significance (if any)?
How are receptive field properties distributed with
respect to the map features (such as pinwheels)?
What is the relationship to other maps (retinotopy)?
Monkey
V1 – The
Ice Cube Model
Orientation
columns
LGN cell
V1 simple cell
V1 complex cell
Hierarchy of Receptive Fields
Concentric on/off
Simple cells
Complex cells
Hyper-complex
Grandmother
Simple cells receptive fields
Models v0.0
Monosynaptic
connectivity from
thalamus to layer 4
Analysis of monosynaptic
connections
Alonso, Usrey & Reid (2001)
Monosynaptic
connectivity
from thalamus
to layer 4
The “sign rule”
of thalamo-cortical
connectivity
Reid & Alonso (1995)
Alonso, Usrey & Reid (2001)
Expected response of linear RF to moving gratings
Yet F1/F0 distributions are bimodal
Skottun et al (1991)
There appears to be a continuum of responses
Priebe et al, 2004
Beware of bounded indices
Priebe et al, 2004
Laminar distribution of F1/F0
Same in cat (Peterson & Freeman; but see Martinez et al)
Standard Models v1.0
Conditional Stimulus
Stochastic
stimuliDistributions
p s
P(s)
P(s | spike)
How are the original and conditional stimulus distributions different?
Standard Models v1.1
Elaborating the LN model
Simple-cell nonlinearities: Saturation
Carandini, Heeger & Movshon (1996)
Saturation depends on orientation
Carandini, Heeger & Movshon (1996)
Simple-cell nonlinearities: Masking
Carandini, Heeger & Movshon (1996)
‘Non-specific’ gain control can shape tuning selectivity
Prediction = Understanding?
The linear-nonlinear model
Simple cell receptive fields in V1
Simple cell receptive fields in V1
Simple cell receptive fields in V1
Simple cell receptive fields in V1
Simple cell receptive fields in V1
Why this particular set of filters?
Going
the modeling
of mean responses
Why beyond
is the cortical
state important?
Cortical State,
Stimulus,
s (t )
x (t )
Response,
r (t )
The response to sensory stimulation at any one time is a function of
both the recent history of the stimulus and the cortical state.
If the ongoing cortical activity is noise then:
• Measure the mean response to sensory stimulus
• Measure how the mean response varies with stimulus parameters.
The
vending
machine
analogy
The
‘vending
machine’
analogy
Current State,
Stimulus,
x (t )
s (t )
Response,
r (t )
Count up to 75¢ and deliver a coke (a deterministic machine)
The
vending
machine
analogy
The
‘vending
machine’
analogy
50¢
25¢
0¢
Count up to 75¢ and deliver a coke (a deterministic machine)
The
vending
machine
analogy
The
‘vending
machine’
analogy
Current State,
Stimulus,
x (t )
s (t )
Response,
r (t )
Count up to 75¢ and deliver a coke (a deterministic machine)
The
vending
machine
analogy
The
‘vending
machine’
analogy
Saturday 23, 2004.
The response appears very noisy.
On average, we get one positive
response every 3 stimuli.
The
vending
machine
analogy
The
‘vending
machine’
analogy
Saturday 23, 2004.
The response appears very noisy.
On average, we get one positive
response every 3 stimuli.
The source of the noise may be in
the mechanism delivering the coke,
the one accepting the quarter, or both.
Modeling the Mean Response – Is it sufficient?
Arieli et al (1996)
Mean response
Single trial prediction
Single trial response
Modeling the Mean Response – Is it sufficient?
Supèr et al (2003)
Seeking invariants of the population response
Stimulus
Response
Percept
8 spikes
Vertical grating
17 spikes
Vertical grating
3 spikes
Vertical grating
There must be some invariant feature in the population responses.
Asking about the ‘neural code’ is equivalent to asking what is this
invariant (‘best clustering’ approach of Victor et al).
Theory of Visual Area X
Representation: Area X is about representing natural signals
optimally.
Estimation/Bayes: Area X is all about estimating the most likely
stimulus (motion/contours/etc) given the statistics of natural
signals.
Processing: Area X is doing some interesting image processing
(for example, face detection)
Behavior: Area X is about using visual information for visually
guided behavior (‘active vision’)
Half-Time