Computational Neuroscience Journal Club

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• Dr. David Barrett (University of Cambridge)
• Tuesday 09 June 2015, 16:00-17:00
• Cambridge University Engineering Department, CBL, BE-438 (http://learning.eng.cam.ac.uk/Public/Directions).

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

David Barrett will cover:

• Diverse coupling of neurons to populations in sensory cortex
• M Okun, N A Steinmetz, L Cossell, M F Iacaruso, H Ko,P Barthó, T Moore, S B Hofer, T D Mrsic-Flogel, M Carandini & K D Harris
• Nature (2015)
• http://www.nature.com/nature/journal/v521/n7553/full/nature14273.html

A large population of neurons can, in principle, produce an astronomical number of distinct firing patterns. In cortex, however, these patterns lie in a space of lower dimension as if individual neurons were “obedient members of a huge orchestra”. Here we use recordings from the visual cortex of mouse (Mus musculus) and monkey (Macaca mulatta) to investigate the relationship between individual neurons and the population, and to establish the underlying circuit mechanisms. We show that neighbouring neurons can differ in their coupling to the overall firing of the population, ranging from strongly coupled ‘choristers’ to weakly coupled ‘soloists’. Population coupling is largely independent of sensory preferences, and it is a fixed cellular attribute, invariant to stimulus conditions. Neurons with high population coupling are more strongly affected by non-sensory behavioural variables such as motor intention. Population coupling reflects a causal relationship, predicting the response of a neuron to optogenetically driven increases in local activity. Moreover, population coupling indicates synaptic connectivity; the population coupling of a neuron, measured in vivo, predicted subsequent in vitro estimates of the number of synapses received from its neighbours. Finally, population coupling provides a compact summary of population activity; knowledge of the population couplings of n neurons predicts a substantial portion of their n2 pairwise correlations. Population coupling therefore represents a novel, simple measure that characterizes the relationship of each neuron to a larger population, explaining seemingly complex network firing patterns in terms of basic circuit variables.

This talk is part of the Computational Neuroscience series.

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