spontaneous activity in v1: a probabilistic framework
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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. - PowerPoint PPT PresentationTRANSCRIPT
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