RESUMO
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.
RESUMO
Many hardware-based solutions now exist for the simulation of bio-like neural networks. Less conventional than software-based systems, these types of simulators generally combine digital and analog forms of computation. In this paper we present a mixed hardware-software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with dynamic adaptation rules (Spike-Timing-Dependent Plasticity). The neurons and networks are configurable, and are computed in 'biological real time' by which we mean that the difference between simulated time and simulation time is guaranteed lower than 50 mus. After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform. We then explain the organization and computation principles of the modules within the platform, and present experimental results which validate the system. Designed as a tool for computational neuroscience, the platform is exploited in collaborative research projects together with neurobiology and computer science partners.
