Spontaneous activity in V1: a probabilistic framework Gergő Orbán Volen 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 The spectrum of V1 physiology is much richer Spontaneous activity Response variabilty Temporal dynamics 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 Statistically well founded principle Allows the representation of uncertainty Efficient for making predictions Internal representation: Useful representation Biologically plausible : retinal image/ RGC output; : neural activity

Spontaneous activity In the awake brain there is patterned neural activity not directly related to the stimulus Evoked Spontaneous (Tsodyks et al, 1999) Patterns of neural activities are similar in stimulus evoked condition and closed eye condition Long-range correlations in neural activity (Fiser et al, 2004)

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) Receptive fields

Spontaneous activity as prior sampling Evoked activity: ANSATZ: Spontaneous activity: Evoked activity Natural image statistics Intuitive link between evoked and spontaneous activities

Images generated by the model Prior over activities Sampling Neural activities Filters Dreamed image Images generated from prior have long-range structure

Evoked and spontaneous neural activity Correlation between hidden units Experiment (Fiser et al, 2004) Evoked and spontaneous activities have similar correlational structure

Spontaneous neural activity before learning Experiment (Fiser et al, 2004) Correlational patterns in the activity of neurons is a result of learning in the probabilistic model

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 Sleep-wake

Bottom line In the probabilistic framework: Temporal dynamics top-down/ lateral interactions Spontaneous activity prior sampling Response variablity posterior variance

Special thanks to Pietro Berkes (Gatsby) Collaborators: Máté Lengyel (Gatsby) József Fiser (Brandeis)

High-level computational principles + physiology Computational paradigm: Normative probabilistic model Experimental paradigm: Spontaneous activity in V1 – prior sampling – posterior variance – top-down/ lateral interactions Are there sensible interpretations that assign functional roles for the spontaeous activity?