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SUMMARY:Computational Neuroscience Journal Club - Guillaume Hennequin (Uni
 versity of Cambridge)
DTSTART:20140325T160000Z
DTEND:20140325T170000Z
UID:TALK51663@talks.cam.ac.uk
CONTACT:Guillaume Hennequin
DESCRIPTION:In this session\, Guillaume Hennequin will cover:\nCortical ne
 ural populations can guide behavior by integrating inputs linearly\, indep
 endent of synchrony\nby M. Histed and J. Maunsell\nPNAS (2013)\nhttp://www
 .pnas.org/content/111/1/E178.abstract\n\nABSTRACT:\nNeurons are sensitive 
 to the relative timing of inputs\, both because several inputs must coinci
 de to reach spike threshold and because active dendritic mechanisms can am
 plify synchronous inputs. To determine if input synchrony can influence be
 havior\, we trained mice to report activation of excitatory neurons in vis
 ual cortex using channelrhodopsin-2. We used light pulses that varied in d
 uration from a few milliseconds to 100 ms and measured neuronal responses 
 and animals’ detection ability. We found detection performance was well 
 predicted by the total amount of light delivered. Short pulses provided no
  behavioral advantage\, even when they concentrated evoked spikes into an 
 interval a few milliseconds long. Arranging pulses into trains of varying 
 frequency from beta to gamma also produced no behavioral advantage. Light 
 intensities required to drive behavior were low (at low intensities\, chan
 nelrhodopsin-2 conductance varies linearly with intensity)\, and the accom
 panying changes in firing rate were small (over 100 ms\, average change: 1
 .1 spikes per s). Firing rate changes varied linearly with pulse intensity
  and duration\, and behavior was predicted by total spike count independen
 t of temporal arrangement. Thus\, animals’ detection performance reflect
 ed the linear integration of total input over 100 ms. This behavioral line
 arity despite neurons’ nonlinearities can be explained by a population c
 ode using noisy neurons. Ongoing background activity creates probabilistic
  spiking\, allowing weak inputs to change spike probability linearly\, wit
 h little amplification of coincident input. Summing across a population th
 en yields a total spike count that weights inputs equally\, regardless of 
 their arrival time.
LOCATION:Cambridge University Engineering Department\, CBL Rm #438 (http:/
 /learning.eng.cam.ac.uk/Public/Directions)
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