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Demo scripts for running spiking network simulations and Hidden Markov Model analyses
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Tutorial for Contamineuro Summer School 2019

Demo script for running spiking network simulations and analyses

by Luca Mazzucato 2019

Please cite: L. Mazzucato, G. La Camera, A. Fontanini Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli, Nat. Neuro. 22, 787-796 (2019).

The tutorial demo1_simulation.m runs simulations of LIF clustered networks with excitatory (E) and inhibitory (I) spiking neurons. You may run 2 different network architectures:

  1. A network with E clusters only (ClustersOption='E') [This part reproduces results from L. Mazzucato et al., 2019]
  2. A network with E and I clusters (ClustersOption='EI') [This part generates unpublished results (manuscript in preparation)]

The tutorial demo2_HMM_simple.m fits a Hidden Markov Model (HMM) with a fixed number of states to the network simulations from the previous scripts. This script is used to familiarize with HMM analyses.

The tutorial demo3_HMM_Full.m performs model selection for the number of HMM states, runs a full HMM fit and plots the results. It can be readily used on ensemble recordings from ephys data.

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