Unsupervised Spiking Neural Network Model of Prefrontal Cortex to study Task Switching with Synaptic deficiency

• URL: http://arxiv.org/abs/2305.14394v1
• Date: Tue, 23 May 2023 05:59:54 GMT
• Title: Unsupervised Spiking Neural Network Model of Prefrontal Cortex to study Task Switching with Synaptic deficiency
• Authors: Ashwin Viswanathan Kannan, Goutam Mylavarapu and Johnson P Thomas
• Abstract summary: We build a computational model of Prefrontal Cortex (PFC) using Spiking Neural Networks (SNN) In this study, we use SNN's having parameters close to biologically plausible values and train the model using unsupervised Spike Timing Dependent Plasticity (STDP) learning rule.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we build a computational model of Prefrontal Cortex (PFC) using Spiking Neural Networks (SNN) to understand how neurons adapt and respond to tasks switched under short and longer duration of stimulus changes. We also explore behavioral deficits arising out of the PFC lesions by simulating lesioned states in our Spiking architecture model. Although there are some computational models of the PFC, SNN's have not been used to model them. In this study, we use SNN's having parameters close to biologically plausible values and train the model using unsupervised Spike Timing Dependent Plasticity (STDP) learning rule. Our model is based on connectionist architectures and exhibits neural phenomena like sustained activity which helps in generating short-term or working memory. We use these features to simulate lesions by deactivating synaptic pathways and record the weight adjustments of learned patterns and capture the accuracy of learning tasks in such conditions. All our experiments are trained and recorded using a real-world Fashion MNIST (FMNIST) dataset and through this work, we bridge the gap between bio-realistic models and those that perform well in pattern recognition tasks

Related papers

• Spatio-temporal Structure of Excitation and Inhibition Emerges in Spiking Neural Networks with and without Biologically Plausible Constraints [0.06752396542927405]
  We present a Spiking Neural Network (SNN) model that incorporates learnable synaptic delays. We implement a dynamic pruning strategy that combines DEEP R for connection removal and RigL for connection. We observed that the reintroduction-temporal patterns of excitation and inhibition appeared in the more biologically plausible model as well.
  arXiv  Detail & Related papers  (2024-07-07T11:55:48Z)
• Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks [23.613277062707844]
  Spiking Neural Networks (SNNs) emulate the integrated-fire-leak mechanism found in biological neurons. Existing SNNs predominantly rely on the Integrate-and-Fire Leaky (LIF) model. This paper proposes a novel S-patioTemporal Circuit (STC) model.
  arXiv  Detail & Related papers  (2024-06-01T11:17:27Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections [10.188771327458651]
  Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. We design an adaptive synaptic filter and introduce the adaptive spiking threshold to enrich the representation ability of SNNs. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets.
  arXiv  Detail & Related papers  (2022-07-06T14:53:32Z)
• Single-phase deep learning in cortico-cortical networks [1.7249361224827535]
  We introduce a new model, bursting cortico-cortical networks (BurstCCN), which integrates bursting activity, short-term plasticity and dendrite-targeting interneurons. Our results suggest that cortical features across sub-cellular, cellular, microcircuit and systems levels jointly single-phase efficient deep learning in the brain.
  arXiv  Detail & Related papers  (2022-06-23T15:10:57Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• SpikePropamine: Differentiable Plasticity in Spiking Neural Networks [0.0]
  We introduce a framework for learning the dynamics of synaptic plasticity and neuromodulated synaptic plasticity in Spiking Neural Networks (SNNs) We show that SNNs augmented with differentiable plasticity are sufficient for solving a set of challenging temporal learning tasks. These networks are also shown to be capable of producing locomotion on a high-dimensional robotic learning task.
  arXiv  Detail & Related papers  (2021-06-04T19:29:07Z)
• Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data [77.92736596690297]
  We introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics. We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model. Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons.
  arXiv  Detail & Related papers  (2020-05-05T14:16:54Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.