Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks

突触可塑性模型与生物启发的无监督深度学习：综述

Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey

本文综述了受生物启发的突触可塑性模型及其在深度学习场景中的应用，探讨了其与脉冲神经网络的联系，指出生物启发深度学习是推动技术和理解智能的重要研究方向。

Optimality of short-term synaptic plasticity in modelling certain dynamic environments

Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication and local synaptic plasticity. It remains unclear whether these neuronal mechanisms only offer efficiency or also underlie the superiority of biological intelligence. Here, we prove rigorously that, indeed, the Bayes-optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on short-term spike-timing-dependent plasticity, a hallmark of biological synapses…

果蝇嗅觉回路的结构特征缓解了持续学习中的稳定性-可塑性困境

Structural features of the fly olfactory circuit mitigate the stability-plasticity dilemma in continual learning

针对持续学习的稳定性-可塑性困境，提出果蝇嗅觉回路模型Fly Model作为即插即用模块，增强记忆稳定性与学习可塑性，以极低计算成本提升机器学习持续学习能力。

Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks

Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks. In this work, we present convolutional spike timing dependent plasticity based feature learning with biologically plausible leaky-integrate-and-fire neurons in Spiking Neural Networks (SNNs). We use shared weight kernels that are trained to encode representative features underlying the input patterns thereby improving the sparsity as well as the robustness of the learning model. We demonstrate…

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances in self-supervised deep learning. Learning minimizes a simple layer-specific loss function and does not need to back-propagate error signals within or between layers. Instead, weight updates follow a local, Hebbian, learning rule that only depends on pre- and post-synaptic neuronal activity, predictive dendritic input and widely broadcasted …

Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity

The liquid state machine (LSM) combines low training complexity and biological plausibility, which has made it an attractive machine learning framework for edge and neuromorphic computing paradigms. Originally proposed as a model of brain computation, the LSM tunes its internal weights without backpropagation of gradients, which results in lower performance compared to multi-layer neural networks. Recent findings in neuroscience suggest that astrocytes, a long-neglected non-neuronal brain cell, modulate synaptic plasticity and brain dynamics, tuning brain networks to the vicinity of the comput…