Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons

• URL: http://arxiv.org/abs/2506.03392v1
• Date: Tue, 03 Jun 2025 21:06:13 GMT
• Title: Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons
• Authors: Aref Ghoreishee, Abhishek Mishra, John Walsh, Anup Das, Nagarajan Kandasamy,
• Abstract summary: We show that a ternary spiking neuron model's performance is worse than that of binary models in deep Q-learning tasks.<n>We propose a novel ternary spiking neuron model to mitigate this issue by reducing the estimation bias.<n>Results show that the proposed ternary spiking neuron mitigates the drastic performance degradation of ternary neurons in Q-learning tasks.
• Score: 3.3062248565028463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning. Although a ternary neuron model has recently been introduced to overcome the limited representation capacity offered by the binary spiking neurons, we show that its performance is worse than that of binary models in deep Q-learning tasks. We hypothesize gradient estimation bias during the training process as the underlying potential cause through mathematical and empirical analysis. We propose a novel ternary spiking neuron model to mitigate this issue by reducing the estimation bias. We use the proposed ternary spiking neuron as the fundamental computing unit in a deep spiking Q-learning network (DSQN) and evaluate the network's performance in seven Atari games from the Gym environment. Results show that the proposed ternary spiking neuron mitigates the drastic performance degradation of ternary neurons in Q-learning tasks and improves the network performance compared to the existing binary neurons, making DSQN a more practical solution for on-board autonomous decision-making tasks.

Related papers

• Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework [6.446189857311325]
  We propose a local objective for neurons that align them to exhibit similarities to error-backpropagation. We examine a neural network comprising decentralized, self-interested neurons seeking to maximize their local objective. We demonstrate the learning capacity of these multi-agent neural networks through experiments on three datasets.
  arXiv  Detail & Related papers  (2023-10-15T21:07:09Z)
• Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks [10.379191500493503]
  Experimental results have demonstrated that the proposed quadratic neuron structure exhibits superior computational and storage efficiency across various tasks. This work introduces an efficient quadratic neuron architecture distinguished by its enhanced utilization of second-order computational information.
  arXiv  Detail & Related papers  (2023-06-10T11:25:31Z)
• SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks [56.35403810762512]
  Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. We study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method.
  arXiv  Detail & Related papers  (2023-02-01T04:22:59Z)
• 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)
• Deep Reinforcement Learning with Spiking Q-learning [51.386945803485084]
  spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (RL)
  arXiv  Detail & Related papers  (2022-01-21T16:42:11Z)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)
• Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks [68.8204255655161]
  Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks. We explore the diversity of the neurons within the hidden layer during the learning process. We analyze how the diversity of the neurons affects predictions of the model.
  arXiv  Detail & Related papers  (2021-09-20T15:12:16Z)
• BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons [8.956708722109415]
  Spiking neural networks (SNNs) transmit information through discrete spikes, which performs well in processing spatial-temporal information. We propose a deep spiking neural network with adaptive self-feedback and balanced excitatory and inhibitory neurons (BackEISNN) For the MNIST, FashionMNIST, and N-MNIST datasets, our model has achieved state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-27T08:38:31Z)
• And/or trade-off in artificial neurons: impact on adversarial robustness [91.3755431537592]
  Presence of sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks. We define AND-like neurons and propose measures to increase their proportion in the network. Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.
  arXiv  Detail & Related papers  (2021-02-15T08:19:05Z)
• Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks [0.9790524827475205]
  We show how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance. We calculate a $>$100x energy improvement for our SRNNs over classical RNNs on the harder tasks.
  arXiv  Detail & Related papers  (2020-05-24T01:04:53Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

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.