P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks

• URL: http://arxiv.org/abs/2406.02923v2
• Date: Thu, 03 Oct 2024 18:55:14 GMT
• Title: P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks
• Authors: Malyaban Bal, Abhronil Sengupta,
• Abstract summary: Spiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures. We develop a scalable probabilistic spiking learning framework for long-range dependency tasks. Our models attain state-of-the-art performance among SNN models across diverse long-range dependency tasks.
• Score: 1.9775291915550175
• License:
• Abstract: Spiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures, with their core computational framework primarily using the leaky integrate-and-fire (LIF) neuron model. However, the limited hidden state representation of LIF neurons, characterized by a scalar membrane potential, and sequential spike generation process, poses challenges for effectively developing scalable spiking models to address long-range dependencies in sequence learning tasks. In this study, we develop a scalable probabilistic spiking learning framework for long-range dependency tasks leveraging the fundamentals of state space models. Unlike LIF neurons that rely on the determinitic Heaviside function for a sequential process of spike generation, we introduce a SpikeSampler layer that samples spikes stochastically based on an SSM-based neuronal model while allowing parallel computations. To address non-differentiability of the spiking operation and enable effective training, we also propose a surrogate function tailored for the stochastic nature of the SpikeSampler layer. To enhance inter-neuron communication, we introduce the SpikeMixer block, which integrates spikes from neuron populations in each layer. This is followed by a ClampFuse layer, incorporating a residual connection to capture complex dependencies, enabling scalability of the model. Our models attain state-of-the-art performance among SNN models across diverse long-range dependency tasks, encompassing the Long Range Arena benchmark, permuted sequential MNIST, and the Speech Command dataset and demonstrate sparse spiking pattern highlighting its computational efficiency.

Related papers

• SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning [21.37966285950504]
  Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Recent emergence of state space models (SSMs) offer superior computational efficiency and modeling capability. We propose a sparse, precise and efficient spiking SSM framework, termed SPikE-SSM.
  arXiv  Detail & Related papers  (2024-10-07T12:20:38Z)
• SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models [19.04709216497077]
  We develop spiking state space models (SpikingSSMs) for long sequence learning. Inspired by dendritic neuron structure, we hierarchically integrate neuronal dynamics with the original SSM block. We propose a light-weight surrogate dynamic network which accurately predicts the after-reset membrane potential and compatible to learnable thresholds.
  arXiv  Detail & Related papers  (2024-08-27T09:35:49Z)
• EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks [14.046487518350792]
  Spiking Neural Networks (SNNs) operate on an event-driven through sparse spike communication. We introduce Residual Potential Dropout (RPD) and Spike-Aware Training (SAT) to regulate potential distribution. Our method yields a 4.4% mAP improvement on the Gen1 dataset, while requiring 38% fewer parameters and only three time steps.
  arXiv  Detail & Related papers  (2024-03-19T09:34:11Z)
• Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding [32.25788551849627]
  Brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. This paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model.
  arXiv  Detail & Related papers  (2023-08-29T09:33:10Z)
• TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling [54.97005925277638]
  The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. It remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. We propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF.
  arXiv  Detail & Related papers  (2023-08-25T08:54:41Z)
• Long Short-term Memory with Two-Compartment Spiking Neuron [64.02161577259426]
  We propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.
  arXiv  Detail & Related papers  (2023-07-14T08:51:03Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning [9.88508686848173]
  Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain. Despite recent tremendous progress in spiking neural networks (SNNs) for handling Euclidean-space tasks, it still remains challenging to exploit SNNs in processing non-Euclidean-space data. Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph learning.
  arXiv  Detail & Related papers  (2021-06-30T11:20:16Z)
• 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)
• Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems [83.98774574197613]
  We take one of the simplest inference methods, a truncated max-product Belief propagation, and add what is necessary to make it a proper component of a deep learning model. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs) The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, optical flow and semantic segmentation.
  arXiv  Detail & Related papers  (2020-03-13T13:11:35Z)
• Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
  We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
  arXiv  Detail & Related papers  (2020-02-21T05:00:01Z)

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.