Spontaneous activity in V1: a probabilistic framework

1. Spontaneous activity in V1:a probabilistic framework Gergő OrbánVolen Center for Complex Systems Brandeis University Sloan Swartz Centers Annual Meeting, 2007

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

3. 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? Activity in V1 The spectrum of V1 physiology is much richer • Spontaneous activity • Response variabilty • Temporal dynamics

4. Internal representation: : retinal image/ RGC output; : neural activity Computational paradigm Density estimation • Statistically well founded principle • Allows the representation of uncertainty • Efficient for making predictions • Useful representation • Biologically plausible

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

6. 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)

7. ANSATZ: Spontaneous activity: Evoked activity Natural image statistics Spontaneous activity asprior sampling Evoked activity: Intuitive link between evoked and spontaneous activities

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

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

10. 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

11. 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

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

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

16. 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?