INFN started tackling neural simulations with its own engine, the Distributed and Plastic Spiking Neural Network (DPSNN), which is a scalable C++/MPI code for HPC platforms at extreme scales simulating the spiking dynamics of a brain cortex modeled as a grid of cortical columns populated with neurons and their interconnecting synapses.
It has been used to first model brain cortex behaviour — with a special focus on sleep-like states — and to gauge compute and power efficiency on different architectures.
INFN has now transitioned to another, more versatile tool, the NEST Simulator; this is a C++/MPI/OpenMP code by the NEST Initiative (https://nest-initiative.org) that empowers a user with a domain-specific language to design a virtual neurophysiology experiment, from the equations driving the dynamics of the components of interest in the cortex (with a rich library of many types of either neurons and synapses ready to be used) to the topology of their interconnections — the so-called connectome — all the way to the necessary supporting tools, like probing or stimulating electrodes that read or inject electrical currents into the simulated cortex.
NEST offers to the experimenter an intuitive Python interface to easily setup a detailed and complex protocol of interaction between a simulated cortex and a set of external stimuli.
Coupled with the huge set of tools for analysis, visualization and data transformation available to the Python user, NEST allows for a compact yet expressive way to perform even very involved neural simulations. INFN has used NEST to implement a biologically-inspired thalamo-cortical model which is able to be trained in classification of handwritten digits from the MNIST dataset and then mimick the wake-sleep cycle, in order to test the enhancing effects of sleep on the quality of learning and recognition, even in noisy environments.
NEST does not currently support running on GPUs, therefore INFN is closely following and collaborating in the development of NeuronGPU (by B. Golosio), a CUDA/C++/MPI code that replicates many features of NEST with a similar Python interface while targeted at high performances on NVIDIA GPUs and which is poised to be integrated in the near future in some capacity into NEST as a GPU-enabling component called NEST-GPU.
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