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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Digital hardware implementation of a stochastic two-dimensional neuron model.

F Grassia¹, T Kohno², T Levi³

¹LTI Lab., University of Picardie Jules Verne, France; IMS Lab., University of Bordeaux, France.

Journal of Physiology, Paris

|February 27, 2017

Summary

This summary is machine-generated.

This study demonstrates a feasible stochastic neuron simulation on Field-Programmable Gate Arrays (FPGAs). This digital implementation enhances biological plausibility for future hybrid neuroscience experiments.

Keywords:

FPGA silicon neuron Neuromorphic engineering Noise Spiking neuron model Stochastic neuron

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Area of Science:

Computational Neuroscience
Digital Systems Engineering
Hardware Acceleration

Background:

Stochasticity is crucial for biological neuron function.
Simulating complex neuron models in digital systems presents challenges.
Field-Programmable Gate Arrays (FPGAs) offer potential for efficient neural simulations.

Purpose of the Study:

To investigate the feasibility of implementing a stochastic neuron model on an FPGA.
To develop a digital emulation of a two-dimensional neuron model with current noise.
To assess the biological plausibility of the FPGA-based neuron simulation.

Main Methods:

Implementation of a two-dimensional neuron model using fixed-point arithmetic on an FPGA.
Introduction of stochasticity via an Ornstein-Uhlenbeck process for current noise.
Design and simulation in VHDL prior to FPGA mapping.
Utilizing arithmetic pipelines for neuron model computations.

Main Results:

Successful validation of the stochastic neuron simulation on an FPGA.
Demonstrated feasibility of digital emulation for individual neuron behavior.
Confirmation of the model's ability to incorporate biologically relevant noise.

Conclusions:

The developed stochastic FPGA implementation is valid and feasible.
This approach enhances the biological plausibility of silicon neuron models.
The work paves the way for advanced hybrid experiments in neuroscience.