Filippo Grassia

Automated parameter estimation of the hodgkin-huxley model using the differential evolution algorithm: Application to neuromimetic analog integrated circuits

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.

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.

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.

Digital hardware implementation of a stochastic two-dimensional neuron model

This study explores the feasibility of stochastic neuron simulation in digital systems (FPGA), which realizes an implementation of a two-dimensional neuron model. The stochasticity is added by a source of current noise in the silicon neuron using an Ornstein-Uhlenbeck process. This approach uses digital computation to emulate individual neuron behavior using fixed point arithmetic operation. The neuron models computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. The experimental results confirmed the validity of the developed stochastic FPGA implementation, which makes the implementation of the silicon neuron more biologically plausible for future hybrid experiments.

Bifurcation analysis in a silicon neuron

In this paper, we describe an analysis of the nonlinear dynamical phenomenon associated with a silicon neuron. Our silicon neuron in Very Large Scale Integration (VLSI) integrates Hodgkin–Huxley (HH) model formalism, including the membrane voltage dependency of temporal dynamics. Analysis of the bifurcation conditions allow us to identify different regimes in the parameter space that are desirable for biasing our silicon neuron. This approach of studying bifurcations is useful because it is believed that computational properties of neurons are based on the bifurcations exhibited by these dynamical systems in response to some changing stimulus. We describe numerical simulations of the Hopf bifurcation which is characteristic of class 2 excitability in the HH model. We also show experimental measurements of an observed phenomenon in biological neurons and termed excitation block, firing rate and effect of current impulses. Hence, by showing that this silicon neuron has similar bifurcations to a certain class of biological neurons, we can claim that the silicon neuron can also perform similar computations.

A neuromimetic spiking neural network for simulating cortical circuits

In this paper, we present an hardware implementation of spiking neural networks based on analog integrated circuits. These ICs compute in real-time biologically realistic cortical neuron models. Each integrated circuit includes five neurons and analog memory cells to set and store the conductance model parameters. The system allows switching on-line the model of cortical neuron. Circuits are embedded in a multi-board system 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 implemented neural plasticity is also biological relevant thanks to its time dependent computation. The whole system is designed to compute programmable models and connectivity schemes in biological real-time. It will allow extending the hybrid technique (connection between biological and artificial neurons) to Micro Electrode Array.

A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model

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A parameter optimization method for Digital Spiking Silicon Neuron model

DSSN model is a qualitative neuronal model designed for efficient implementation in a digital arithmetic circuit. In our previous studies, we extended this model to support a wide variety of neuronal classes. Parameters of the DSSN model were hand-fitted to reproduce neuronal activity precisely. In this work, we studied automatic parameter fitting procedure for the DSSN model. We optimized parameters of the model by a GPU-based implementation of the differential evolution algorithm in order to reproduce waveforms of the ionic-conductance models and reduce necessary circuit resources for the implementation.