How to throw away information optimally
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1
University College London, Gatsby Computational Neuroscience Unit, United Kingdom
A wide range of computations performed by the nervous system involve throwing away information, a particular type of probabilistic inference known as marginalization. This is certainly true of sensory processing; for example, to recognize a person based on the activity of neurons in our retina, we have to throw away information about the person's pose, motion, clothes, background, light level, etc. It is also true in other computations, ones as diverse as causal reasoning, odor recognition, motor control, visual tracking, coordinate
transformations, visual search, decision making, and object recognition, to name just a few. The question we address here is: how could neural circuits implement such marginalizations? We show that when the statistics of spike trains follow a distribution which we call "Poisson-like" - a distribution that is close to what has been reported in vivo - some of the more common marginalizations can be achieved with networks that implement a quadratic nonlinearity and divisive normalization, the latter being a type of nonlinear lateral inhibition that has been widely reported in neural circuits. We illustrate this with three common examples: sensorimotor transformations, visual tracking, and olfaction.
Keywords:
Structure,
Dynamics and Function of Brains
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Keynote
Topic:
other
Citation:
Latham
P
(2011). How to throw away information optimally.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00023
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Received:
26 Sep 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Prof. Peter Latham, University College London, Gatsby Computational Neuroscience Unit, London, United Kingdom, pel@gatsby.ucl.ac.uk