Jean Tomas

Plasticity in memristive devices for spiking neural networks.

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Neuromimetic ICs with analog cores: an alternative for simulating spiking neural networks

This paper aims at discussing the implementation of simulation systems for SNN based on analog computation cores (neuromimetic ICs). Such systems are an alternative to completely digital solutions for the simulation of spiking neurons or neural networks. Design principles for the neuromimetic ICs and the hosting systems are presented together with their features and performances. The authors summarize the existing architectures and neuron models used in such systems, when configured as stand-alone tools for simulating ANN or together with a neurophysiology set-up to study hybrid living artificial neural networks. As a primary illustration, the authors present results from one of the platforms: hardware simulations of single neurons and adaptive neural networks modeled using the Hodgkin-Huxley formalism for point neurons and spike-timing dependent plasticity algorithms for the network adaptation. Additional examples are detailed in the other papers of the session.

A Library of Analog Operators Based on the Hodgkin-Huxley Formalism for the Design of Tunable, Real-Time, Silicon Neurons

In this paper, we present a library of analog operators used for the analog real-time computation of the Hodgkin-Huxley formalism. These operators make it possible to design a silicon (Si) neuron that is dynamically tunable, and that reproduces different kinds of neurons. We used an original method in neuromorphic engineering to characterize this Si neuron. In electrophysiology, this method is well known as the “voltage-clamp” technique. We also compare the features of an application-specific integrated circuit built with this library with results obtained from software simulations. We then present the complex behavior of neural membrane voltages and the potential applications of this Si neuron.

Learning through ferroelectric domain dynamics in solid-state synapses

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

Towards an embodied in vitro electrophysiology : the neurobit project

Note: 316 Reference SAMLAB-ARTICLE-2004-002 Record created on 2009-05-12, modified on 2016-08-08

Real-time simulations of networks of Hodgkin-Huxley neurons using analog circuits

The traditional dilemma for performing network simulations with analog circuits is the great difficulty of handling the connectivity in hardware. The main problem is that hardware-based connectivity must be built following predefined plasticity and connectivity rules, and that once the hardware is built, it is usually not possible to change its configuration. We show here an alternative system in which the membrane equations are solved in analog ASIC circuits, but the connectivity remains controlled by a digital computer. We illustrate the behavior of this system by comparing the analog simulations with traditional computer simulations of the same models.

Hardware computation of conductance-based neuron models

We review different applications of silicon conductance-based neuron models implemented on analog circuits. At the single-cell level, we describe a circuit in which conductances are programmed to simulate various Hodgkin-Huxley type models; integrated in a hardware/software system, they provide a simulation tool; an illustrative example is the simulation of bursting neurons of the thalamus. At the network level, we present a mixed analog-digital architecture, where the connectivity and the plasticity rules are implemented digitally and are therefore very flexible. These circuits provide valuable tools for real-time simulations, including hybrid applications where single-neuron or network models are interfaced with biological cells.

A Conductance-Based Silicon Neuron with Dynamically Tunable Model Parameters

This paper presents an analog neuromimetic ASIC. It integrates Hodgkin-Huxley (HH) model types, computed in real-time and in analog continuous mode. We developed a library of sub-circuits calculating the elementary mathematical functions encountered in the HH models. Those sub-circuits are organized to form the model set of equations, in which all numerical parameters are dynamically tunable via a mixed analog-digital interface. Neural activity examples are presented to validate the library elements and illustrate the diversity of models simulated by a single ASIC

A Real-Time Closed-Loop Setup for Hybrid Neural Networks

Hybrid living-artificial neural networks are an efficient and adaptable experimental support to explore the dynamics and the adaptation process of biological neural systems. We present in this paper an innovative platform performing a real-time closed-loop between a cultured neural network and an artificial processing unit like a robotic interface. The system gathers bioware, hardware, and software components and ensures the closed-loop data processing in less than 50 mus. We detail here the system components and compare its performances to a recent commercial platform.

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.