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

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      Creators
      Zeldenrust, Fleur
      Knecht, Sicco de
      Wadman, Wytse J.
      Denève, Sophie
      Gutkin, Boris
      Date of Archiving
      2017
      Archive
      Radboud Data Repository
      Data archive handle
      https://hdl.handle.net/11633/di.dcn.DSC_626840_0001_460
      Related publications
      Estimating the information extracted by a single spiking neuron from a continuous input time series  
      Publication type
      Dataset
      Access level
      Restricted access
      Please use this identifier to cite or link to this item: https://hdl.handle.net/2066/203825   https://hdl.handle.net/2066/203825
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      Organization
      Neurophysiology
      Audience(s)
      Life sciences
      Languages used
      English
      Key words
      Bayesian neuron model; in vitro electrophysiology; neural information processing; Information theory; artificial neural network
      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 recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the 'Bayesian neuron' (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant.
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