TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling

• URL: http://arxiv.org/abs/2308.13250v3
• Date: Sat, 17 Feb 2024 13:30:46 GMT
• Title: TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
• Authors: Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan
• Abstract summary: 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.
• Score: 54.97005925277638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. To address this challenge, we propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF. The proposed model incorporates carefully designed somatic and dendritic compartments that are tailored to facilitate learning long-term temporal dependencies. Furthermore, a theoretical analysis is provided to validate the effectiveness of TC-LIF in propagating error gradients over an extended temporal duration. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, and high energy efficiency of the proposed TC-LIF model. Therefore, this work opens up a myriad of opportunities for solving challenging temporal processing tasks on emerging neuromorphic computing systems. Our code is publicly available at https://github.com/ZhangShimin1/TC-LIF.

Related papers

• 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)
• ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks [1.5931140598271163]
  We build a model for efficient learning sequences using only local always-on and phase-free plasticity. We showcase the capabilities of ELiSe in a mock-up of birdsong learning, and demonstrate its flexibility with respect to parametrization.
  arXiv  Detail & Related papers  (2024-02-26T17:30:34Z)
• Efficient Online Learning for Networks of Two-Compartment Spiking Neurons [23.720523101102593]
  We present a novel online learning method specifically tailored for networks of TC-LIF neurons. We also propose a refined TC-LIF neuron model called Adaptive TC-LIF, which is carefully designed to enhance temporal information integration. Our approach successfully preserves the superior sequential modeling capabilities of the TC-LIF neuron while incorporating the training efficiency and hardware friendliness of online learning.
  arXiv  Detail & Related papers  (2024-02-25T03:15:12Z)
• 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)
• 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)
• Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay [67.50637511633212]
  A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day.
  arXiv  Detail & Related papers  (2021-12-09T07:11:14Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Learn to cycle: Time-consistent feature discovery for action recognition [83.43682368129072]
  Generalizing over temporal variations is a prerequisite for effective action recognition in videos. We introduce Squeeze Re Temporal Gates (SRTG), an approach that favors temporal activations with potential variations. We show consistent improvement when using SRTPG blocks, with only a minimal increase in the number of GFLOs.
  arXiv  Detail & Related papers  (2020-06-15T09:36:28Z)

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.