Timothée Levi

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.

In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses.

Brain-machine interfaces (BMI) were born to control “actions from thoughts” in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI—a neuromorphic chip for brain repair—to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary “bottom-up” approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of “finite size networks” which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.

Tunable neuromimetic integrated system for emulating cortical neuron models

Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin–Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called “dynamic-clamp,” that consists of connecting artificial and biological neurons to study the function of neuronal circuits.

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.

Design of a modular and mixed neuromimetic ASIC

This paper presents a new specific integrated circuit (ASIC) that emulates neurons electrical activity using a biophysical model (neuromimetic ASIC). Such ASICs form the computation core of a complete simulation system dedicated to the investigation of the dynamics of biomimetic neural networks. The circuits were designed using a modular approach. Simulations were realized by mixing the behavioral and the transistor-level description of modules. We present in this paper the ASICs specifications and architecture, together with simulation results.

Silicon neuron: digital hardware implementation of the quartic model

AbstractThis paper presents an FPGA implementation of the quartic neuron model. This approach uses digital computation to emulate individual neuron behavior. We implemented the neuron model using fixed-point arithmetic operation. The neuron model’s computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. We show that the proposed FPGA implementation of the quartic neuron model can emulate the electrophysiological activities in various types of cortical neurons and is capable of producing a variety of different behaviors, with diversity similar to that of neuronal cells. The neuron family of this digital neuron can be modified by appropriately adjusting the neuron model’s parameters.

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.

A behavioral and temperature measurements-based modeling of an operational amplifier using VHDL-AMS

High temperature has a direct impact on the behavior of an integrated circuit (IC). Instrumentation and measurement circuits and systems are one of the most sensitive ICs to such working condition. Modeling the temperature impact on these systems could be achieved by many approaches. In this paper, we present an attractive method to characterize the temperature effect on an elementary circuit: the operational amplifier (op-amp). We develop a behavioral model for a commercial operational amplifier by using a set of temperature measurements of common performance parameters. As it presents several advantages, VHDL-AMS language was chosen to develop the model.

Real-time adaptive discrimination threshold estimation for embedded neural signals detection

Multi-electrode array systems used in neurological applications produce large amount of data because of the simultaneous continuous high-rate sampling on a large number of channels. This data flow must be reduced to envision compact data acquisition systems with wireless transmission for body implantation. In spike-related applications, the useful data is sparse due to the relative low neurons firing rate combined to the high sampling rate. High compression ratio can be achieved by detecting, extracting and storing only the relevant spike occurrences. The first step is to provide a simple yet robust discrimination threshold based on the characteristics of the noise distribution. This article presents both a method and its hardware implementation for adaptive spike detection.

Modeling Methodology for Analog Front-End Circuits Dedicated to High-Temperature Instrumentation and Measurement Applications

High-temperature (HT) applications have witnessed real growth during the last years. Several instrumentation and measurement applications require electronic circuits functioning reliably in HT. The analog-to-digital converter (ADC) and the operational amplifier (op-amp) are essential parts of measurement systems and circuits. They are the major parts of the analog front end of a conditioning signal circuit. Modeling HT electronic (HTE) components is a challenging task. The motivation of this paper is to describe an appropriate choice of the ADC and op-amp modeling approaches for HT ranges. The context of HTE systems is briefly described here and allows us to understand the challenges of such modeling. Comparing most known techniques enables us to make a suitable modeling choice for the HT. An appropriate methodology for modeling is presented. Some temperature-dependent models of ideal and nonideal ADC and op-amp are developed. The comparison of the simulation and experimental results is described to validate our approach choice.