This project is a collaboration between the Boris Gutkin, Sophie Denève and myself at the Group for Neural Theory (ENS, Paris).

It is widely accepted that neurons do not form a homogeneous population, but that there is large variability between neurons. Therefore, the way in which individual neurons respond to a stimulus (their encoding properties or receptive field) also varies. A classical example is the difference between ’type 1′ and ’type 2′ neurons. Apart from these intrinsic ‘encoding’ properties of neurons, the ‘decoding’ properties of neurons also show a large variability. Surprisingly however, neural coding models often treat neural networks as homogeneous populations of neurons. Here we show that networks with spiking neurons with heterogeneous encoding and decoding properties can do optimal online stimulus representation. Raimon Bullich is linking the results we found in the single neuron experiments to network effects in this framework.

 

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link to publication:	Zeldenrust, F., Gutkin, B., & Denève, S. – 2019 – Efficient and robust coding in heterogeneous recurrent networks. PLoS. Zeldenrust, F., Gutkin, B., & Denève, S. – 2019 – Efficient and robust coding in heterogeneous recurrent networks. bioRxiv. Zeldenrust, Denève, Gutkin – 2013 – Matching encoding and decoding with spiking neurons
link to code:	GitHub, Open Source Brain