Computational Neuroscience Journal Club

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• Guillaume Hennequin (University of Cambridge)
• Tuesday 25 March 2014, 16:00-17:00
• Cambridge University Engineering Department, CBL Rm #438 (http://learning.eng.cam.ac.uk/Public/Directions).

If you have a question about this talk, please contact Guillaume Hennequin.

In this session, Guillaume Hennequin will cover: Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony by M. Histed and J. Maunsell PNAS (2013) http://www.pnas.org/content/111/1/E178.abstract

ABSTRACT : Neurons are sensitive to the relative timing of inputs, both because several inputs must coincide to reach spike threshold and because active dendritic mechanisms can amplify synchronous inputs. To determine if input synchrony can influence behavior, we trained mice to report activation of excitatory neurons in visual cortex using channelrhodopsin-2. We used light pulses that varied in duration 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, channelrhodopsin-2 conductance varies linearly with intensity), and the accompanying 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 independent of temporal arrangement. Thus, animals’ detection performance reflected the linear integration of total input over 100 ms. This behavioral linearity despite neurons’ nonlinearities can be explained by a population code using noisy neurons. Ongoing background activity creates probabilistic spiking, allowing weak inputs to change spike probability linearly, with little amplification of coincident input. Summing across a population then yields a total spike count that weights inputs equally, regardless of their arrival time.

This talk is part of the Computational Neuroscience series.

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