Building a Dynamical Network Model from Neural Spiking Data: Application of Poisson Likelihood

Research showed that, the information transmitted in biological neurons is encoded in the instants of successive action potentials or their firing rate. In addition to that, in-vivo operation of the neuron makes measurement difficult and thus continuous data collection is restricted. Due to those reasons, classical mean square estimation techniques that are frequently used in neural network training is very difficult to apply. In such situations, point processes and related likelihood methods may be beneficial. In this study, we will present how one can apply certain methods to use the stimulus-response data obtained from a neural process in the mathematical modeling of a neuron. The study is theoretical in nature and it will be supported by simulations. In addition it will be compared to a similar study performed on the same network model.

• Publication year

  2017

• Venue

  arXiv: Neurons and Cognition

• Publication date

  2017-10-09

• Fields of study

  Biology, Mathematics, Computer Science

• Identifiers
  arXiv 1710.03071
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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