A Brief Review on Spiking Neural Network - A Biological Inspiration&lt;b&gt; &lt;/b&gt;

Th, Rafi

doi:10.20944/preprints202104.0202.v1

Cited by 3 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By this, the learning algorithm can deduce a pattern by identifying a relationship between the target variable and the rest of the dataset based on the information it already has. On the other hand, with unsupervised learning the training set contains no 'labels' in the data and the algorithm learns without needing to supervise the model (Rafi, 2021).…”

Section: Introduction To Brain-inspired Neural Network For This Studymentioning

confidence: 99%

Machine learning with a snapshot of data: Spiking neural network ‘predicts’ reinforcement histories of pigeons' choice behavior

Plessas

Espinosa-Ramos

Parry

et al. 2022

J Exper Analysis Behavior

View full text Add to dashboard Cite

An accumulated body of choice research has demonstrated that choice behavior can be understood within the context of its history of reinforcement by measuring response patterns. Traditionally, work on predicting choice behaviors has been based on the relationship between the history of reinforcement—the reinforcer arrangement used in training conditions—and choice behavior. We suggest an alternative method that treats the reinforcement history as unknown and focuses only on operant choices to accurately predict (more precisely, retrodict) reinforcement histories. We trained machine learning models known as artificial spiking neural networks (SNNs) on previously published pigeon datasets to detect patterns in choices with specific reinforcement histories—seven arranged concurrent variable‐interval schedules in effect for nine reinforcers. Notably, SNN extracted information from a small ‘window’ of observational data to predict reinforcer arrangements. The models' generalization ability was then tested with new choices of the same pigeons to predict the type of schedule used in training. We examined whether the amount of the data provided affected the prediction accuracy and our results demonstrated that choices made by the pigeons immediately after the delivery of reinforcers provided sufficient information for the model to determine the reinforcement history. These results support the idea that SNNs can process small sets of behavioral data for pattern detection, when the reinforcement history is unknown. This novel approach can influence our decisions to determine appropriate interventions; it can be a valuable addition to our toolbox, for both therapy design and research.

show abstract

Section: Introduction To Brain-inspired Neural Network For This Studymentioning

confidence: 99%

Machine learning with a snapshot of data: Spiking neural network ‘predicts’ reinforcement histories of pigeons' choice behavior

Plessas

Espinosa-Ramos

Parry

et al. 2022

J Exper Analysis Behavior

View full text Add to dashboard Cite

show abstract

“…Each dynamic spiking neuron fires a spike when the internal state of the neuron (i.e., the membrane potential) reaches a certain threshold. Evidence reveals that in the biological neuron, actual film channels control the movements of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals (Rafi, 2021 ). The human brain disposes of information between neurons through electrical motivations (Taherkhani et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

STNet: A novel spiking neural network combining its own time signal with the spatial signal of an artificial neural network

et al. 2023

View full text Add to dashboard Cite

IntroductionThis article proposes a novel hybrid network that combines the temporal signal of a spiking neural network (SNN) with the spatial signal of an artificial neural network (ANN), namely the Spatio-Temporal Combined Network (STNet).MethodsInspired by the way the visual cortex in the human brain processes visual information, two versions of STNet are designed: a concatenated one (C-STNet) and a parallel one (P-STNet). In the C-STNet, the ANN, simulating the primary visual cortex, extracts the simple spatial information of objects first, and then the obtained spatial information is encoded as spiking time signals for transmission to the rear SNN which simulates the extrastriate visual cortex to process and classify the spikes. With the view that information from the primary visual cortex reaches the extrastriate visual cortex via ventral and dorsal streams, in P-STNet, the parallel combination of the ANN and the SNN is employed to extract the original spatio-temporal information from samples, and the extracted information is transferred to a posterior SNN for classification.ResultsThe experimental results of the two STNets obtained on six small and two large benchmark datasets were compared with eight commonly used approaches, demonstrating that the two STNets can achieve improved performance in terms of accuracy, generalization, stability, and convergence.DiscussionThese prove that the idea of combining ANN and SNN is feasible and can greatly improve the performance of SNN.

show abstract

Improvement of pattern recognition in spiking neural networks by modifying threshold parameter and using image inversion

Aghabarar,

Kiani,

Keshavarzi

2023

Multimed Tools Appl

View full text Add to dashboard Cite

A Brief Review on Spiking Neural Network - A Biological Inspiration<b> </b>

Cited by 3 publications

References 23 publications

Machine learning with a snapshot of data: Spiking neural network ‘predicts’ reinforcement histories of pigeons' choice behavior

Machine learning with a snapshot of data: Spiking neural network ‘predicts’ reinforcement histories of pigeons' choice behavior

STNet: A novel spiking neural network combining its own time signal with the spatial signal of an artificial neural network

Improvement of pattern recognition in spiking neural networks by modifying threshold parameter and using image inversion

Contact Info

Product

Resources

About