Neuromimetic ICs and system for parameters extraction in biological neuron models (original) (raw)
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Neural Computation, 2011
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 futur...
Tunable neuromimetic integrated system for emulating cortical neuron models
Frontiers in Neuroscience, 2011
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.
An Implementation of a Biological Neural Model using Analog-Digital Integrated Circuits
2008 IEEE International Behavioral Modeling and Simulation Workshop, 2008
Given the trends in reconfigurable hardware systems inspired by biology, we present a hardware implementation of a closed-loop neural system. The hardware implementation focuses on modeling the behavior of two-cells, PD-LP system of a Pyloric Network from a lobster's stomach. This two-cell network emulates, in real-time, a digital representation of interacting neurons whose biological behavior is known. We evaluated the circuit design by varying the circuit values to determine the appropriateness and range of operation of the model. Future development of hardware models will be used to evaluate the feasibility of creating a platform of specialized circuits or an FPNA of biological neural characteristics.
Hardware computation of conductance-based neuron models
Neurocomputing, 2004
We review di erent 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 exible. These circuits provide valuable tools for real-time simulations, including hybrid applications where single-neuron or network models are interfaced with biological cells.
Digital realization of the proposed linear model of the Hodgkin‐Huxley neuron
International Journal of Circuit Theory and Applications, 2019
SummaryIt seems that computing systems that imitate the brain can be achieved by integrating of electronics and neuroscience. In recent years, neuromorphic systems have been developed by the fusion of electronics and neuroscience. Since neurons are the basis of neural systems, constructing the optimized digital neuron plays a critical role in neuromorphic applications. Furthermore, the dynamic of ionic channels, which are the causative agents of synaptic plasticity is the main characteristic of biological neurons. The Hodgkin‐Huxley neuron model is a mathematical description of biological neuron that is widely used in neuroscience to explore the relation of action potential propagation and information transmission. This model consists of nonlinear differential equations, which approximates the electrical characteristics of excitable cells such as neurons and cardiac muscle. In this paper, a simplified version of the Hodgkin‐Huxley neuron model was proposed by substituting its comple...
Real-time simulations of networks of Hodgkin–Huxley neurons using analog circuits
Neurocomputing, 2006
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. r
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IEEE Journal of Solid-State Circuits, 1991
A complete derivation of neuron model is presented, starting with the description of the fundamental biological mechanisms involved in the living neural cell, followed by the mathematical model formulation extracted from these mechanisms, and a circuit theory technique to obtain a physical IC suitable circuit that emulates the derived mathematical equations culminating with the presentation of the experimental results of a chip fabricated in a 2-pm double-metal, double-poly CMOS process. It is emphasized that the FitzHugh-Nagumo model is very adequate for emulation of small biological systems. A reduced complexity oscillatory model suitable for implementation of relatively large neural network architectures is also introduced with several corresponding CMOS realizations and measured results.
Integrated circuit implementation of a cortical neuron
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A new circuit for neuron implementation
2000
In this paper a new cell for complex systems is presented, an analog design of an Inferior Olive neuron model was developed. The circuit output exhibits both the behaviours, sub-threshold oscillations and spike oscillations (beating) that characterises the Inferior Olive neurons. Moreover, stochastic resonance in the circuit was demonstrated. * The authors are with Dipartimento Elettrico, Elettronico e Sistemistico Università degli studi di Catania V.le A. Doria, 6, 95125 Catania -Italy except A.
Comparative Research of Neuron Circuits
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Spiking neurons can be implemented in hardware, for example, to model large neural systems, simulate real-time behaviour, and interface bi-directionally between brains and machines. Circuit solutions used to implement silicon neuron circuits depend on the application requirements. Various neuron circuits are presented in this thesis, including spike-event generators (Axon Hillock neuron circuits), above-threshold neuron circuits (Quadratic Integrate and Fire neuron circuits), and differential pair integrator circuits. Cadence's tool simulates these circuits using 180nm technology. Comparing these circuits is based on their working properties and simulation results, and their features are demonstrated with experiments.