About

SUSNOIMAC (Simulator Using Spiking Neurons Originally Intended for Modelling Auditory Cortex) is a a toolkit for simulating and analysing  spiking neural networks. 

Modelling Features

  • Izhikevich model of neuron (Izhikevich 2003; Izhikevich 2004a)
  • STDP (spike-timing dependent plasticity)
  • Spontaneous activity in a form of spontaneous synaptic release (miniature postsynaptic currents, sometimes called “minis”)
  • High flexibility in network structure definition (neurons, their types and density and synaptic connection with given weights and delays)
  • High flexibility in definition of inputs

Computational Features

  • The Simulator itself is written in Java
  • High computational efficiency (a simulation of one second of the model with 100 000 neurons (and over 21 million of synapses) requires approximately 33.73 s of computational time on a single-processor PC)
  • Parallel implementation
  • Mersenne Twister method for generating pseudorandom number (this method is considered as one of the best PRNGs in the terms of combination of high degree of randomness and efficiency)
  • Batch processing of more experiments in a serie
  • Possibility to save a simulation and load it later for further simulations

Analysis (Postprocessing) Features

  • Visualisation of small-scale simulations directly in Java
    • 3D and 2D view on the network over time of the simulation
    • Visualisation of values of main dynamic variables
  • Various analysis (for all scales) in Matlab
    • A (basic analysis)
      • Visualisation of spike trains, development of mean firing rate (overall and according neuron types)
      • Visualisation of the network
      • Computation and visualisation of global and local oscillations (waves)
    • B (synapses analysis)
      • Development of the weights
    • C (analysis dependent on the external inputs - currently only auditory related)
      • Computation of receptive fields and characteristic frequencies
      • Spatial visualisation of the characteristic frequencies
      • Computation of degree of tonotopy, various statistics and their visualisation