Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators

• URL: http://arxiv.org/abs/2206.01338v1
• Date: Thu, 2 Jun 2022 23:38:10 GMT
• Title: Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
• Abstract summary: We propose that extra-synaptic diffusion of local neuroulatormods may afford an effective mode of backpropagation lying within the bounds of biological plausibility. We show that our approximate gradient-based update rule, ModProp, propagates credit information through arbitrary time steps.
• Score: 1.1744028458220428
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The spectacular successes of recurrent neural network models where key parameters are adjusted via backpropagation-based gradient descent have inspired much thought as to how biological neuronal networks might solve the corresponding synaptic credit assignment problem. There is so far little agreement, however, as to how biological networks could implement the necessary backpropagation through time, given widely recognized constraints of biological synaptic network signaling architectures. Here, we propose that extra-synaptic diffusion of local neuromodulators such as neuropeptides may afford an effective mode of backpropagation lying within the bounds of biological plausibility. Going beyond existing temporal truncation-based gradient approximations, our approximate gradient-based update rule, ModProp, propagates credit information through arbitrary time steps. ModProp suggests that modulatory signals can act on receiving cells by convolving their eligibility traces via causal, time-invariant and synapse-type-specific filter taps. Our mathematical analysis of ModProp learning, together with simulation results on benchmark temporal tasks, demonstrate the advantage of ModProp over existing biologically-plausible temporal credit assignment rules. These results suggest a potential neuronal mechanism for signaling credit information related to recurrent interactions over a longer time horizon. Finally, we derive an in-silico implementation of ModProp that could serve as a low-complexity and causal alternative to backpropagation through time.

Related papers

• Unleashing Temporal Capacity of Spiking Neural Networks through Spatiotemporal Separation [67.69345363409835]
  Spiking Neural Networks (SNNs) are considered naturally suited for temporal processing, with membrane potential propagation widely regarded as the core temporal modeling mechanism.<n>We design Non-Stateful (NS) models progressively removing membrane propagation to its stage-wise role. Experiments reveal a counterintuitive phenomenon: moderate removal in shallow layers improves performance, while excessive removal causes collapse.
  arXiv  Detail & Related papers  (2025-12-05T07:05:53Z)
• Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays [50.45313162890861]
  We introduce a novel learning rule for simultaneously learning synaptic connection strengths and delays.<n>We validate our approach by extending a widely-used SNN model for classification trained with unsupervised learning.<n>Results demonstrate that our proposed method consistently achieves superior performance across a variety of test scenarios.
  arXiv  Detail & Related papers  (2025-06-17T21:24:58Z)
• Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation [79.16635054977068]
  We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations. In this paper, we focus on an application in numerical cognition, where a bump of activity in an attractor network is used as a spatial numerical representation.
  arXiv  Detail & Related papers  (2025-03-20T12:28:08Z)
• Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning [91.29876772547348]
  Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation. This paper reveals that SNNs, when amalgamated with synaptic delay and temporal coding, are proficient in executing (knowledge) graph reasoning.
  arXiv  Detail & Related papers  (2024-05-27T05:53:30Z)
• Contribute to balance, wire in accordance: Emergence of backpropagation from a simple, bio-plausible neuroplasticity rule [0.0]
  We introduce a novel neuroplasticity rule that offers a potential mechanism for implementing BP in the brain. We demonstrate mathematically that our learning rule precisely replicates BP in layered neural networks without any approximations.
  arXiv  Detail & Related papers  (2024-05-23T03:28:52Z)
• Backpropagation through space, time, and the brain [2.10686639478348]
  We introduce General Latent Equilibrium, a computational framework for fully local-temporal credit assignment in physical, dynamical networks of neurons. In particular, GLE exploits the morphology of dendritic trees to enable more complex information storage and processing in single neurons.
  arXiv  Detail & Related papers  (2024-03-25T16:57:02Z)
• 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)
• Unbalanced Diffusion Schr\"odinger Bridge [71.31485908125435]
  We introduce unbalanced DSBs which model the temporal evolution of marginals with arbitrary finite mass. This is achieved by deriving the time reversal of differential equations with killing and birth terms. We present two novel algorithmic schemes that comprise a scalable objective function for training unbalanced DSBs.
  arXiv  Detail & Related papers  (2023-06-15T12:51:56Z)
• 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)
• 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)
• 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)
• Convergence and Alignment of Gradient Descentwith Random Back propagation Weights [6.338178373376447]
  gradient descent with backpropagation is the workhorse of artificial neural networks. Lillicrap et al. propose a more biologically plausible "feedback alignment" algorithm that uses random and fixed backpropagation weights.
  arXiv  Detail & Related papers  (2021-06-10T20:58:05Z)
• Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks [0.548253258922555]
  plasticity of the conduction delay between neurons plays a fundamental role in learning. Understanding the precise adjustment of synaptic delays could help us in developing effective brain-inspired computational models.
  arXiv  Detail & Related papers  (2020-11-18T16:25:32Z)
• Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain [62.997667081978825]
  Activation Relaxation (AR) is motivated by constructing the backpropagation gradient as the equilibrium point of a dynamical system. Our algorithm converges rapidly and robustly to the correct backpropagation gradients, requires only a single type of computational unit, and can operate on arbitrary computation graphs.
  arXiv  Detail & Related papers  (2020-09-11T11:56:34Z)

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.