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SUMMARY:Computational Neuroscience Journal Club - Yan Wu (University of Ca
 mbridge)
DTSTART:20151013T150000Z
DTEND:20151013T160000Z
UID:TALK61473@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:Yan Wu will cover:\n\n* Critical and maximally informative enc
 oding between neural populations in the retina\n* D. B. Kastner\, S. A. Ba
 ccus\, and T. O. Sharpee\n* PNAS (2015)\n* http://www.pnas.org/content/112
 /8/2533.full.html\n\nComputation in the brain involves multiple types of n
 eurons\, yet the organizing principles for how these neurons work together
  remain unclear. Information theory has offered explanations for how diffe
 rent types of neurons can maximize the transmitted information by encoding
  different stimulus features. However\, recent experiments indicate that s
 eparate neuronal types exist that encode the same filtered version of the 
 stimulus\, but then the different cell types signal the presence of that s
 timulus feature with different thresholds. Here we show that the emergence
  of these neuronal types can be quantitatively described by the theory of 
 transitions between different phases of matter. The two key parameters tha
 t control the separation of neurons into subclasses are the mean and stand
 ard deviation (SD) of noise affecting neural responses. The average noise 
 across the neural population plays the role of temperature in the classic 
 theory of phase transitions\, whereas the SD is equivalent to pressure or 
 magnetic field\, in the case of liquid–gas and magnetic transitions\, re
 spectively. Our results account for properties of two recently discovered 
 types of salamander Off retinal ganglion cells\, as well as the absence of
  multiple types of On cells. We further show that\, across visual stimulus
  contrasts\, retinal circuits continued to operate near the critical point
  whose quantitative characteristics matched those expected near a liquid
 –gas critical point and described by the nearest-neighbor Ising model in
  three dimensions. By operating near a critical point\, neural circuits ca
 n maximize information transmission in a given environment while retaining
  the ability to quickly adapt to a new environment.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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