Directed Information between Connected Leaky Integrate-and-Fire Neurons

Nima Soltani, Andrea J. Goldsmith

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The connectivity structure between neurons is useful for determining how groups of neurons perform tasks. Directed information is a measure that can be used to infer connectivity between neurons using their recorded time series. In this paper, we develop a method of calculating the directed information rate from one neuron to another neuron it is connected to, given a particular neuronal topology. We assume a leaky integrate-and-fire (LIF) neuron model with independent and identically distributed random spike train inputs, which governs how the membrane potential of the output neuron evolves. We use this neuron model to find the dynamics of the resulting output spike train from its membrane potential dynamics, both for when the past of the input neuron is observed and when it is not. We show that an action potential in the LIF model causes a conditional independence of the activity before and after it, and we capture this conditional independence via a Markov model. We use these spike train dynamics to then calculate the directed information between the spike train of the input neuron to the spike train generated by the LIF model. In addition, we show how changing the refractory period of the LIF model affects the directed information, and also how the spike train dynamics are affected by memory constraints, which are commonly imposed in estimators of directed information.

Original languageEnglish (US)
Article number7931602
Pages (from-to)5954-5967
Number of pages14
JournalIEEE Transactions on Information Theory
Issue number9
StatePublished - Sep 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Analytical models
  • Markov processes
  • biological neural networks
  • biological system modeling
  • stochastic processes


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