Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments - Ambroise, Matthieu; Levi, Timothée; Joucla, Sébastien; Yvert, Blaise; Saïghi, Sylvain (Frontiers in Neuroscience, 2013)
This article investigates the neural network system in the leech heartbeat and develops a real-time biomimetic digital hardware using few-resource implementation for hybrid experiments. The leech heartbeat neural network is one of the most simple central pattern generators (CPG). In biology, CPG provide for rhythmic bursts of spikes and is the basis for all muscles contractions orders (heartbeat) and locomotion (walking, running….). The leech neural network system was already investigated and this CPG has been already formalized with Hodgkin-Huxley neural model (HH) that is the most complex neuron model. However, the resources needed for a neural model is proportional to its complexity. To answer to this issue, this article describes a biomimetic implementation into FPGA (Field Programmable Gate Array) of a network of 240 CPGs using a simple model (Izhikevich model) and by proposing a new synapse model: activity dependent depression synapse. The architecture of the network implementation allows working on a single computation core. This digital system works in real-time, needs few resources and has the same bursting activity behavior than complex model. To validate our implementation of this CPG, we compare it firstly with a simulation of the complex model. Then we match its activity with the pharmacological data of the activity of the rat’s spinal cord. This digital system allows future hybrid experiments and will be a great step towards hybridation between biological tissue and artificial neural network. This network of CPG could be also useful for mimic the activity of a different animal locomotion or developing hybrid experiments for neuroprosthesis development.