Matthieu Ambroise

Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments.

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development.

Generation of Locomotor-Like Activity in the Isolated Rat Spinal Cord Using Intraspinal Electrical Microstimulation Driven by a Digital Neuromorphic CPG

Neural prostheses based on electrical microstimulation offer promising perspectives to restore functions following lesions of the central nervous system (CNS). They require the identification of appropriate stimulation sites and the coordination of their activation to achieve the restoration of functional activity. On the long term, a challenging perspective is to control microstimulation by artificial neural networks hybridized to the living tissue. Regarding the use of this strategy to restore locomotor activity in the spinal cord, to date, there has been no proof of principle of such hybrid approach driving intraspinal microstimulation (ISMS). Here, we address a first step toward this goal in the neonatal rat spinal cord isolated ex vivo, which can display locomotor-like activity while offering an easy access to intraspinal circuitry. Microelectrode arrays were inserted in the lumbar region to determine appropriate stimulation sites to elicit elementary bursting patterns on bilateral L2/L5 ventral roots. Two intraspinal sites were identified at L1 level, one on each side of the spinal cord laterally from the midline and approximately at a median position dorso-ventrally. An artificial CPG implemented on digital integrated circuit (FPGA) was built to generate alternating activity and was hybridized to the living spinal cord to drive electrical microstimulation on these two identified sites. Using this strategy, sustained left-right and flexor-extensor alternating activity on bilateral L2/L5 ventral roots could be generated in either whole or thoracically transected spinal cords. These results are a first step toward hybrid artificial/biological solutions based on electrical microstimulation for the restoration of lost function in the injured CNS.

Biorealistic spiking neural network on FPGA

In this paper, we present a digital hardware implementation of a biorealistic spiking neural network composed of 117 Izhikevich neurons. This digital system works in hard real-time, which means that it keeps the same biological time of simulation at the millisecond scale. The Izhikevich neuron implementation requires few resources. The neurons behavior is validated by comparing their firing rate to biological data. The interneuron connections are composed of biorealistic synapses. The architecture of the network implementation allows working on a single computation core. It is freely configurable from an independent-neuron configuration to all-to-all configuration or a mix with several independent small networks. This spiking neural network will be used for the development of a new proof-of-concept Brain Machine Interface, i.e. a neuromorphic chip for neuroprosthesis, which has to replace the functionality of a damaged part of the central nervous system.

Biomimetic neural network for modifying biological dynamics during hybrid experiments

Electrical stimulation of nerve tissue and recording of neural electrical activity are the basis of emerging prostheses and treatments for many neurological disorders. Here we present closed-loop bio-hybrid experiment using in vitro biological neuronal network (BNN) with an artificial neural network (ANN) implemented in a neuromorphic board. We adopted a neuromorphic board which is able to perform real-time event detection and trigger an electrical stimulation of the BNN. This system embeds an ANN, based on Izhikevich neurons which can be put in uni- and bi-directional communication with the BNN. The ANN used in the following experiments was made up of 20 excitatory neurons with inhibition synapse and with synaptic plasticity to design central pattern generator. Open-loop and closed-loop hybrid experiments show that the biological dynamics can be modified. This work can be seen as the first step towards the realization of an innovative neuroprosthesis.

Leech heartbeat neural network on FPGA

Most of rhythmic movements are programmed by central pattern-generating networks that comprise neural oscillators. In this article, we implement a real-time biorealistic central pattern generator (CPG) into digital hardware (FPGA) for future hybrid experiments with biological neurons. This CPG mimics the Leech heartbeat neural network system. This system is composed of a neuron core from Izhikevich model, a biorealistic synaptic core and a topology to configure the table of connectivity of the different neurons. Our implementation needs few resources and few memories. Thanks to that, we could implement network of these CPG for instance to mimic the behavior of a salamander. Our system is validated by comparing our results to biological data.