Spontaneous activity in V1:a probabilistic framework

Gergő OrbánVolen Center for Complex Systems

Brandeis University

Sloan Swartz Centers Annual Meeting, 2007

Normative account for visual representations

Optimization criterion for the emergence of simple-cell receptive fields: independent ‘filters’ + sparseness (Bell & Sejnowski, 1995; Olshausen & Field, 1996)

Activity in V1

Spontaneous activity Response variabilty Temporal dynamics

The spectrum of V1 physiology is much richer

Can we devise a framework that

Gives a functional description of visual processing Uses normative principles in probabilistic learning Gives a more complete interpretation of V1 activity?

Computational paradigm

Density estimation

Useful representation Biologically plausible

: retinal image/ RGC output; : neural activity

Statistically well founded principle Allows the representation of uncertainty

Efficient for making predictions

Internal representation:

Spontaneous activity

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Evoked Spontaneous

(Tsodyks et al, 1999)

Patterns of neural activities are similar in stimulus evoked condition and closed eye condition

In the awake brain there is patterned neural activity not directly related to the stimulus

Long-range correlations in neural activity

(Fiser et al, 2004)

Receptive fields

Probabilistic model: Field of experts

Filters are componenets in a Boltzmann energy function (Osindero, Welling & Hinton, 2006)

Sparse prior (Student-t distribution)

Image model assuming translational invariance (Black & Roth, 2005)

Learning: standard contrastive divergence & Hybrid MC (Hinton 2002)

Spontaneous activity asprior sampling

Evoked activity:

Intuitive link between evoked and spontaneous activities

ANSATZ:

Spontaneous activity:

Evoked activity

Natural imagestatistics

Images generated by the model

Images generated from prior have long-range structure

Prior over activities

Neural activities

Dreamed image

Sampling

Filters

Evoked and spontaneous neural activity

Evoked and spontaneous activities have similarcorrelational structure

Correlation between hidden units

Experiment

(Fiser et al, 2004)

Spontaneous neural activity before learning

Correlational patterns in the activity of neuronsis a result of learning in the probabilistic model

Experiment

(Fiser et al, 2004)

Conclusions

The probabilistic framework provides a viable explanation for spontaneous activity in V1

Spontaneous activity as sampling from prior

Long range correlations are present both in evoked and spontaneous activities

The tendency of changes in spatial correlations with training match experimental results

Bottom line

In the probabilistic framework:

Spontaneous activity prior sampling

Response variablity posterior variance

Temporal dynamics top-down/lateral interactions

Special thanks to

Pietro Berkes (Gatsby)

Collaborators:

Máté Lengyel (Gatsby)

József Fiser (Brandeis)

– prior sampling

– posterior variance– top-down/ lateral interactions

Are there sensible interpretations that assign

functional roles for the spontaeous activity?

High-level computational principles + physiology

• Computational paradigm:Normative probabilistic model

• Experimental paradigm:Spontaneous activity in V1