FNS spiking neural simulator; LIFL neuron model, event-driven simulation (Susi et al 2021)

FNS is an event-driven Spiking Neural Network simulator, oriented to data-driven simulations. FNS combines spiking/synaptic level description with the event-driven approach, allowing the user to define heterogeneous modules and multi-scale connectivity with delayed connections and plastic synapses, providing fast simulations at the same time. A novel parallelization strategy is also implemented in order to further speed up simulations. FNS is based on the Leaky-Integrate and Fire with Latency (LIFL) spiking neuron model, that combines some realistic neurocomputational features to low computational complexity. FNS is written in Java, distributed as open source and protected by the GPL license.

Model Type: Synapse; Realistic Network; Spiking neural network; Neuron or other electrically excitable cell

Region(s) or Organism(s): Human

Model Concept(s): Connectivity matrix; Brain Rhythms; Action Potentials; STDP

Simulation Environment: Java; FNS Neural Simulator


Susi G et al. (2021). FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency Scientific reports. 11 [PubMed]

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