We present a reconfigurable acquisition and wavelet-based detection circuit, NeuroBetaMed, for in vitro biological signals. It is implemented on a configurable digital integrated circuit (FPGA). We consider real-time computing as a hard specification and silicon area as a price to pay. It is designed for noisy signals like those recorded from in vitro cellular preparations, by extracellular electrodes. NeuroBetaMed performs biological signal acquisition, stationary wavelet transform (SWT) and adaptive thresholding to detect action potentials (APs). Initially developed to detect pancreatic islet cells action potentials, this system is also suitable for neural signals.

Reuse methodologies are now widely used to design digital circuits. They are based on the concept of intellectual property (IP), or virtual block of computing, characterized by a behavioral model, synthesizable or not. The design reuse for analog integrated systems is much less natural and less standardized. This paper addresses the issue of an analog design flow based on reuse, focusing on three key issues: the formal content of the IP block, the design of a reusable analog IP, and the organization of a design flow centered on an IP library. After a conceptual overview, this paper presents the methodological principles and details examples with a tutorial intention. The objective is to guide the designer involved in the process of developing analog IPs and corresponding design flow. This method is inspired by platform-based design and adapted here on an original case study: the design of full-custom neuromimetic integrated circuits, built from specific analog computational blocks. The development of reusable IPs represents an additional effort, mainly for behavioral modeling and characterization. Nevertheless, the steps illustrated in this case study show that the extra time provides a definite advantage to future design projects.

We propose a new estimation method for the characterization of the Hodgkin-Huxley formalism. This method is an alternative technique to the classical estimation methods associated with voltage clamp measurements. It uses voltage clamp type recordings, but is based on the differential evolution algorithm. The parameters of an ionic channel are estimated simultaneously, such that the usual approximations of classical methods are avoided and all the parameters of the model, including the time constant, can be correctly optimized. In a second step, this new estimation technique is applied to the automated tuning of neuromimetic analog integrated circuits designed by our research group. We present a tuning example of a fast spiking neuron, which reproduces the frequency-current characteristics of the reference data, as well as the membrane voltage behavior. The final goal of this tuning is to interconnect neuromimetic chips as neural networks, with specific cellular properties, for future theoretical studies in neuroscience.

Large-scale spiking neural networks (SNN) are generally run on distributed and parallel architectures with multiple computation nodes. These architectures induce extra delays due to the node-to-node communication process. In multiboard & multichip SNNs, important delays may affect spike arrival time and, thus, can alter simulation results. In this work, we propose a method aiming to guarantee spike arrival time with arbitrary prefixed deadlines. The communication architecture is based on the token-passing access policy to grant access to shared communication channels. We show that several network parameters must be set carefully if spikes have to meet their deadlines. Parameters are chosen by taking into account the communication channel bandwidth, the arbitrary deadlines and the worst case situation that can happen in generating neural activity in SNNs. As proof of concept, we have built a system that emulates up to 120 analog Hodgkin-Huxley neurons spread across 6 boards. Experimental results show that whatever it happens (unless there is a network fault), spikes reach their destination with a maximum delay of 5 microseconds.

In this paper, we present a multi-board system based on analog neuromimetic ICs. These ICs compute in realtime conductance-based models. These models are implemented in a modular architecture based on our analog IPs. Each IC includes five neurons and analog memory cells to set and store the conductance model parameters, and eventually optimize it to compensate the analog circuit variability. The circuits are embedded in a multi-board system able to host up to 120 neurons spread across 6 boards all connected to a backplane with daisy-chain facilities. Each action potential computed by analog neuromimetic chips is time-stamped when detected by digital device (FPGA). These FPGAs are also in charge of the real-time plasticity computation and of controlling inter-boards communication. The system is designed to compute programmable models and connectivity schemes.

Neuronal variability has been thought to play an important role in the brain. As the variability mainly comes from the uncertainty in biophysical mechanisms, stochastic neuron models have been proposed for studying how neurons compute with noise. However, most papers are limited to simulating stochastic neurons in a digital computer. The speed and the efficiency are thus limited especially when a large neuronal network is of concern. This brief explores the feasibility of simulating the stochastic behavior of biological neurons in a very large scale integrated (VLSI) system, which implements a programmable and configurable Hodgkin-Huxley model. By simply injecting noise to the VLSI neuron, various stochastic behaviors observed in biological neurons are reproduced realistically in VLSI. The noise-induced variability is further shown to enhance the signal modulation of a neuron. These results point toward the development of analog VLSI systems for exploring the stochastic behaviors of biological neuronal networks in large scale.

Bioelectronic systems using multielectrodes arrays (MEAs) make possible new experiments, based on long term analysis of neural system at the network level. In this paper we present an experimental platform that processes signals from a 60 electrode MEA and runs event (spike, burst, and stimulus artifact) detection and statistic in real-time. The minimum processing task (storage and monitoring) takes 25 mus and the full one takes 60 mus. The data is available for online and offline analysis: on the computer's screen, on the computer's hard disk, and on the TCP/IP layer.

This paper presents an original method to tune a neuromimetic IC based on neuron conductance-based models (Hodgkin-Huxley formalism). This method is well known in electrophysiology as the "voltage-clamp" technique. It consists in measuring the neural conductances while holding the membrane voltage at a clamped level. The voltage- and time- dependent variables of the conductance equations are then interpolated from the measurements. We apply this technique on the neuromimetic IC to extract the exact parameters of the neuron model. This model is computed in software, and results are compared with the hardware simulation. We conclude by mentioning the potential applications of this technique in hardware simulation systems based on neuromimetic ASICs.

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.

This paper presents an analog neuromimetic integrated circuit and an associated system dedicated for experiments of parameters extraction in biological neuron models. The IC based on Hodgkin-Huxley (HH) formalism computes in real-time and continuous mode. The dedicated system is a PCI board that is able to program dynamically the neuron model parameters in the IC. The full system, which includes the IC and the PCI board, is used to build a new hardware/software technique to extract biophysics parameters from biological neuron. This technique could be helpful for the neuroscientists proposing an alternative to voltage-clamp technique. For that, the new technique will use optimization algorithms to be efficient