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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)
Receptive field
Monkey V1 – Orientation Tuning
Monkey V1 – Orientation Columns
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 – Orientation Map
Orientation columns Monkey V1 – The Ice Cube Model
LGN cell
V1 simple cell
V1 complex cell
Concentric on/off
Simple cells
Complex cells
Hyper-complex
Grandmother
Hierarchy of Receptive Fields
Simple cells receptive fields
Models v0.0
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)
Monosynaptic connectivity from thalamus to layer 4
Expected response of linear RF to moving gratings
Skottun et al (1991)
Yet F1/F0 distributions are bimodal
There appears to be a continuum of responses
Priebe et al, 2004
Priebe et al, 2004
Beware of bounded indices
Laminar distribution of F1/F0
Same in cat (Peterson & Freeman; but see Martinez et al)
Standard Models v1.0
Conditional Stimulus Distributions
How are the original and conditional stimulus distributions different?
p s
P(s) P(s | spike)
Stochastic stimuli
Standard Models v1.1
Elaborating the LN model
Carandini, Heeger & Movshon (1996)
Simple-cell nonlinearities: Saturation
Carandini, Heeger & Movshon (1996)
Saturation depends on orientation
Carandini, Heeger & Movshon (1996)
Simple-cell nonlinearities: Masking
‘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?
Why is the cortical state important?
Cortical State, ( )s t
Stimulus, ( )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.
Going beyond the modeling of mean responses
The ‘vending machine’ analogy
Current State, ( )s t
Stimulus, ( )x t Response, ( )r t
Count up to 75¢ and deliver a coke (a deterministic machine)
The vending machine analogy
The ‘vending machine’ analogy
Count up to 75¢ and deliver a coke (a deterministic machine)
0¢
25¢
50¢
The vending machine analogy
The ‘vending machine’ analogy
Current State, ( )s t
Stimulus, ( )x 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 positiveresponse every 3 stimuli.
The vending machine analogy
The ‘vending machine’ analogy
Saturday 23, 2004.
The response appears very noisy.On average, we get one positiveresponse every 3 stimuli.
The source of the noise may be inthe mechanism delivering the coke,
The vending machine analogy
Arieli et al (1996)
Single trial response
Single trial prediction
Mean response
Modeling the Mean Response – Is it sufficient?
Modeling the Mean Response – Is it sufficient?
Supèr et al (2003)
Seeking invariants of the population response
8 spikes
17 spikes
3 spikes
Vertical grating
Vertical grating
Vertical grating
Stimulus Response Percept
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
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