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V1 Physiology

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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?

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The cortex

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Visual Areas in the Nonhuman Primate Felleman & van Essen

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Visual Areas in the Nonhuman Primate

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Monkey LGN

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Monkey V1 – Laminar organization

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Monkey V1 – Inputs

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Monkey V1 – Outputs

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Monkey V1 – Oculodominance Columns

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Monkey V1 – CO patches (or blobs)

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Receptive field Monkey V1 – Orientation Tuning

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Monkey V1 – Orientation Columns

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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

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Orientation columns Monkey V1 – The Ice Cube Model

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LGN cell

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V1 simple cell

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V1 complex cell

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Concentric on/off Simple cells Complex cells Hyper-complex Grandmother Hierarchy of Receptive Fields

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Simple cells receptive fields

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Models v0.0

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Analysis of monosynaptic connections Alonso, Usrey & Reid (2001) Monosynaptic connectivity from thalamus to layer 4

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The “sign rule” of thalamo-cortical connectivity Reid & Alonso (1995)Alonso, Usrey & Reid (2001) Monosynaptic connectivity from thalamus to layer 4

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Expected response of linear RF to moving gratings

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Skottun et al (1991) Yet F1/F0 distributions are bimodal

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There appears to be a continuum of responses Priebe et al, 2004

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Beware of bounded indices

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Laminar distribution of F1/F0 Same in cat (Peterson & Freeman; but see Martinez et al)

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Standard Models v1.0

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Conditional Stimulus Distributions How are the original and conditional stimulus distributions different? P(s)P(s | spike) Stochastic stimuli

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Standard Models v1.1

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Elaborating the LN model

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Carandini, Heeger & Movshon (1996) Simple-cell nonlinearities: Saturation

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Carandini, Heeger & Movshon (1996) Saturation depends on orientation

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Carandini, Heeger & Movshon (1996) Simple-cell nonlinearities: Masking

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‘Non-specific’ gain control can shape tuning selectivity

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Prediction = Understanding?

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The linear-nonlinear model

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Simple cell receptive fields in V1

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Why this particular set of filters?

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Why is the cortical state important? Cortical State, Stimulus, Response, 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

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The ‘vending machine’ analogy Current State, Stimulus, Response, Count up to 75¢ and deliver a coke (a deterministic machine) The vending machine analogy

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The ‘vending machine’ analogy Count up to 75¢ and deliver a coke (a deterministic machine) 0¢ 25¢ 50¢ The vending machine analogy

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The ‘vending machine’ analogy Current State, Stimulus, Response, Count up to 75¢ and deliver a coke (a deterministic machine) The vending machine analogy

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The ‘vending machine’ analogy The vending machine analogy

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The ‘vending machine’ analogy The vending machine analogy

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Arieli et al (1996) Single trial response Single trial prediction Mean response Modeling the Mean Response – Is it sufficient?

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Supèr et al (2003)

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Seeking invariants of the population response 8 spikes 17 spikes 3 spikes Vertical grating StimulusResponsePercept 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).

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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’)

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Half-Time

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