Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series.

ABSTRACT: Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., "the neural code") lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing "sensory stimulus": the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the

SUBMITTER: Zeldenrust F 

PROVIDER: S-EPMC5471316 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature