Dataset Information

Integrate-and-Fire Neuron Circuit Without External Bias Voltages.

ABSTRACT: In this study, we propose an integrate-and-fire (I&F) neuron circuit using a p-n-p-n diode that utilizes a latch-up phenomenon and investigate the I&F operation without external bias voltages using mixed-mode technology computer-aided design (TCAD) simulations. The neuron circuit composed of one p-n-p-n diode, three MOSFETs, and a capacitor operates with no external bias lines, and its I&F operation has an energy consumption of 0.59 fJ with an energy efficiency of 96.3% per spike. The presented neuron circuit is superior in terms of structural simplicity, number of external bias lines, and energy efficiency in comparison with that constructed with only MOSFETs. Moreover, the neuron circuit exhibits the features of controlling the firing frequency through the amplitude and time width of the synaptic pulse despite of the reduced number of the components and no external bias lines.

SUBMITTER: Park YS

PROVIDER: S-EPMC8024489 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Integrate-and-Fire Neuron Circuit Without External Bias Voltages.

Park Young-Soo YS Woo Sola S Lim Doohyeok D Cho Kyoungah K Kim Sangsig S

Frontiers in neuroscience 20210324

In this study, we propose an integrate-and-fire (I&F) neuron circuit using a p-n-p-n diode that utilizes a latch-up phenomenon and investigate the I&F operation without external bias voltages using mixed-mode technology computer-aided design (TCAD) simulations. The neuron circuit composed of one p-n-p-n diode, three MOSFETs, and a capacitor operates with no external bias lines, and its I&F operation has an energy consumption of 0.59 fJ with an energy efficiency of 96.3% per ...[more]

PMID: 33841084

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Project description:Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the "Functional subnetwork approach" (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network's intended function, without the use of global optimization or machine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuron model have fundamental similarities to those of a non-spiking leaky integrator neuron model. We derive analytical expressions that show functional parallels between: (1) A spiking neuron's steady-state spiking frequency and a non-spiking neuron's steady-state voltage in response to an applied current; (2) a spiking neuron's transient spiking frequency and a non-spiking neuron's transient voltage in response to an applied current; and (3) a spiking synapse's average conductance during steady spiking and a non-spiking synapse's conductance. The models become more similar as additional spiking neurons are added to each population "node" in the network. We apply the FSA to model a neuromuscular reflex pathway two different ways: Via non-spiking components and then via spiking components. These results provide a concrete example of how a single non-spiking neuron may model the average spiking frequency of a population of spiking neurons. The resulting model also demonstrates that by using the FSA, models can be constructed that incorporate both spiking and non-spiking units. This work facilitates the construction of large networks of spiking neurons and synapses that perform specific functions, for example, those implemented with neuromorphic computing hardware, by providing an analytical method for directly tuning their parameters without time-consuming optimization or learning.

Project description:Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin's electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike's Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA.

Dataset Information

Integrate-and-Fire Neuron Circuit Without External Bias Voltages.

Publications

Integrate-and-Fire Neuron Circuit Without External Bias Voltages.

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