Biologically-Informed Excitatory and Inhibitory Balance for Robust Spiking Neural Network Training

• URL: http://arxiv.org/abs/2404.15627v1
• Date: Wed, 24 Apr 2024 03:29:45 GMT
• Title: Biologically-Informed Excitatory and Inhibitory Balance for Robust Spiking Neural Network Training
• Authors: Joseph A. Kilgore, Jeffrey D. Kopsick, Giorgio A. Ascoli, Gina C. Adam,
• Abstract summary: Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the ability to train spiking networks. The results indicate networks with the biologically realistic 80:20 excitatory:inhibitory balance can reliably train at low activity levels and in noisy environments.
• Score: 0.40498500266986387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train spiking networks with various ratios of excitatory to inhibitory neurons on AI-relevant datasets. The results indicate networks with the biologically realistic 80:20 excitatory:inhibitory balance can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.

Related papers

• Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
  Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
  arXiv  Detail & Related papers  (2024-09-17T04:48:45Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• Expanding memory in recurrent spiking networks [2.8237889121096034]
  Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes. We present a novel spiking neural network that circumvents these limitations.
  arXiv  Detail & Related papers  (2023-10-29T16:46:26Z)
• Expressivity of Spiking Neural Networks [15.181458163440634]
  We study the capabilities of spiking neural networks where information is encoded in the firing time of neurons. In contrast to ReLU networks, we prove that spiking neural networks can realize both continuous and discontinuous functions.
  arXiv  Detail & Related papers  (2023-08-16T08:45:53Z)
• Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks [5.955727366271805]
  We put forth a new framework for spike-based excitatory-inhibitory spiking networks. Our work proposes a new perspective on spiking networks that may serve as a starting point for a mechanistic understanding of biological spike-based computation.
  arXiv  Detail & Related papers  (2023-07-18T15:17:00Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)
• Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons [0.7340017786387767]
  We introduce Latent Equilibrium, a new framework for inference and learning in networks of slow components. We derive disentangled neuron and synapse dynamics from a prospective energy function. We show how our principle can be applied to detailed models of cortical microcircuitry.
  arXiv  Detail & Related papers  (2021-10-27T16:15:55Z)
• The distribution of inhibitory neurons in the C. elegans connectome facilitates self-optimization of coordinated neural activity [78.15296214629433]
  The nervous system of the nematode Caenorhabditis elegans exhibits remarkable complexity despite the worm's small size. A general challenge is to better understand the relationship between neural organization and neural activity at the system level. We implemented an abstract simulation model of the C. elegans connectome that approximates the neurotransmitter identity of each neuron.
  arXiv  Detail & Related papers  (2020-10-28T23:11:37Z)

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.