A leaky integrate and fire model for spike generation in a neuron with variable threshold and multiple‐input–single‐output configuration

Ganguly, Chittotosh; Chakrabarti, Saswat

doi:10.1002/ett.3561

Cited by 11 publications

(4 citation statements)

References 24 publications

(37 reference statements)

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“…Additionally, single-neuron calcium spikes and similar biophysical events have specialized nonlinear features that are leveraged by deconvolution methods to decode underlying voltage spikes by supervised learning of large amounts of data (18,19). Future variations of the method presented here will mimic specific signal properties relevant to biophysical processes and train over less naïve presynaptic input and signal shapes, as well as use leaky integrate and fire neurons more suitable for recreating tonic firing and bursting network activity (46,47).…”

Section: Discussionmentioning

confidence: 99%

Inference of Presynaptic Connectivity from Temporally Blurry Spike Trains by Supervised Learning

Vareberg

Eizadi

Ren

et al. 2022

Preprint

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Reconstruction of neural network connectivity is a central focus of neuroscience. The ability to use neuronal connection information to predict activity at single unit resolution and decipher its effect on whole systems can provide critical information about behavior and cognitive processing. Neuronal sensing modalities come in varying forms, but there is yet to exist a modality that can deliver readouts that sufficiently address the spatiotemporal constraints of biological nervous systems. This necessitates supplementary approaches that rely on mathematical models to mitigate physical limitations and decode network features. Here, we introduce a simple proof-of-concept model that addresses temporal constraints by reconstructing presynaptic connections from temporally blurry data. We use a variation of the perceptron algorithm to process firing rate information at multiple time constraints for a heterogenous feed-forward network of excitatory, inhibitory, and unconnected presynaptic units. We evaluate the performance of the algorithm under these conditions and determine the optimal learning rate, firing rate, and the ability to reconstruct single unit spikes for a given degree of temporal blur. We then test our method on a physiologically relevant configuration by sampling network subpopulations of leaky integrate-and-fire neuronal models displaying bursting firing patterns and find comparable learning rates for optimized reconstruction of network connectivity. Our method provides a recipe for reverse engineering neural networks based on limited data quality that can be extended to more complicated readouts and connectivity distributions relevant to multiple brain circuits.

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Section: Discussionmentioning

confidence: 99%

Inference of Presynaptic Connectivity from Temporally Blurry Spike Trains by Supervised Learning

Vareberg

Eizadi

Ren

et al. 2022

Preprint

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“…Figure 3 shows the Axon-Hillock circuit, which is considered to be the traditional artificial neuron circuit [19]. It was proposed by Mead in 1989 and has been widely used in many works [20,21]. The input current I in is commonly generated by a photodiode, in which different light intensities correspond to different magnitudes of the induced currents.…”

Section: The Principle Of Axon-hillock Circuitmentioning

confidence: 99%

An Ultra-Low Power Threshold Voltage Variable Artificial Retina Neuron

2022

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An artificial retina neuron is proposed and implemented by CMOS technology. It can be used as an image sensor in the Artificial Intelligence (AI) field with the benefit of ultra-low power consumption. The artificial neuron can generate signals in spike shape with pre-designed frequencies under different light intensities. The power consumption is reduced by removing the film capacitor. The comparator is adopted to improve the stability of the circuit, and the power consumption of the comparator is optimized. The power consumption of the proposed CMOS neuron circuit is suppressed. The ultra-low-power artificial neuron with variable threshold shows a frequency range of 0.8–80 kHz when the input current is varied from 1 pA to 150 pA. The minimum DC power is 35 pW when the input current is 5 pA. The minimum energy of the neuron is 3 fJ. The proposed ultra-low-power artificial retina neuron has wide potential applications in the field of AI.

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“…Various research showed that the increase of variability of neuron threshold is proportional to the increasing in the input spikes firing rate [45]. Also, a few neuromorphic hardware was built to achieve the relationship between threshold and firing rate [39]. An experiment that had been performed in vivo showed that threshold variability plays a crucial role in the information transfer process in the brain of electric fish [46], [47].…”

Section: Related Workmentioning

confidence: 99%

Automated Adaptive Threshold-Based Feature Extraction and Learning for Spiking Neural Networks

Amin

2021

IEEE Access

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Over the past years Spiking Neural Networks (SNNs) models became attractive as a possible bridge to enable low-power event-driven neuromorphic hardware. SNNs have a high computational power due to the implicit employment of the biologically inspired input times. SNNs employ various parameters such as neuron threshold, synaptic delays, and weights in their structures. However, SNNs applications are still limited and elementary compared with other neural network architectures such as the Convolution Neural Networks (CNNs). In this research, a new SNN-based model named Adaptive Threshold Module (ATM) and its algorithm are proposed. The proposed ATM and algorithm depend on the adaptation of the internal spiking neuron threshold level. Adapting the threshold of the neurons is employed to control the spiking neuron firing rate to uniquely extract the main features of the input pattern that is in the shape of spike trains. It is shown that this technique works as an automated feature extraction method of input patterns in an efficient and faster way than other methods. The proposed method can preserve all information of the input spike trains. Simulations of the proposed model and the algorithm, using the challenging speech TIDIGITS dataset, sound RWCP dataset, and Poisson distribution spike trains, show encouraging results. The ATM can make SNN provide an accuracy surpassing that of the current state-of-the-art SNN algorithms and conventional non-spiking learning models.

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A leaky integrate and fire model for spike generation in a neuron with variable threshold and multiple‐input–single‐output configuration

Cited by 11 publications

References 24 publications

Inference of Presynaptic Connectivity from Temporally Blurry Spike Trains by Supervised Learning

Inference of Presynaptic Connectivity from Temporally Blurry Spike Trains by Supervised Learning

An Ultra-Low Power Threshold Voltage Variable Artificial Retina Neuron

Automated Adaptive Threshold-Based Feature Extraction and Learning for Spiking Neural Networks

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