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1. Meta-learning biologically plausible plasticity rules with random feedback pathways

   https://doi.org/10.1038/s41467-023-37562-1  

   Shervani-Tabar, Navid; Rosenbaum, Robert (March 2023, Nature Communications)

   Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-learning to discover effective, interpretable learning rules satisfying biological constraints. 

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2. A surprising link between cognitive maps, successor-relation based reinforcement learning, and BTSP

   https://doi.org/10.1101/2025.04.22.650046  

   Yang, Yukun; Stöckl, Christoph; Maass, Wolfgang (April 2025, bioRxiv)

   Abstract Recent recordings from the hippocampus of the human brain suggest that after a few presentations of sequences of unrelated natural images, correlations between emergent neural codes encode the sequential structure of these sequences. We show that this learning process, which is consistent with experimental data on BTSP (Behavioral Time Scale Synaptic Plasticity), creates a cognitive map that enables online-generation of plans for moving to any given goal, both in spatial environments and in abstract graphs. Furthermore, the resulting neural circuits and plasticity rules provide a biologically plausible implementation of Successor Relation based Reinforcement Learning. In addition, this brain-derived approach for learning cognitive maps provides a blueprint for implementing autonomous learning through on-chip plasticity in energy-efficient neuromorphic hardware. 

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3. Synaptic balancing: A biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance

   https://doi.org/10.1371/journal.pcbi.1010418  

   Stock, Christopher H; Harvey, Sarah E; Ocko, Samuel A; Ganguli, Surya (September 2022, PLOS Computational Biology)

   Richards, Blake A (Ed.)

   We introduce a novel, biologically plausible local learning rule that provably increases the robustness of neural dynamics to noise in nonlinear recurrent neural networks with homogeneous nonlinearities. Our learning rule achieves higher noise robustness without sacrificing performance on the task and without requiring any knowledge of the particular task. The plasticity dynamics—an integrable dynamical system operating on the weights of the network—maintains a multiplicity of conserved quantities, most notably the network’s entire temporal map of input to output trajectories. The outcome of our learning rule is a synaptic balancing between the incoming and outgoing synapses of every neuron. This synaptic balancing rule is consistent with many known aspects of experimentally observed heterosynaptic plasticity, and moreover makes new experimentally testable predictions relating plasticity at the incoming and outgoing synapses of individual neurons. Overall, this work provides a novel, practical local learning rule that exactly preserves overall network function and, in doing so, provides new conceptual bridges between the disparate worlds of the neurobiology of heterosynaptic plasticity, the engineering of regularized noise-robust networks, and the mathematics of integrable Lax dynamical systems. 

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4. Approximating Back-propagation for a Biologically Plausible Local Learning Rule in Spiking Neural Networks

   https://doi.org/10.1145/3354265.3354275  

   Shrestha, Amar; Fang, Haowen; Wu, Qing; Qiu, Qinru (July 2019, International Conference on Neuromorphic Systems)

   Asynchronous event-driven computation and communication using spikes facilitate the realization of spiking neural networks (SNN) to be massively parallel, extremely energy efficient and highly robust on specialized neuromorphic hardware. However, the lack of a unified robust learning algorithm limits the SNN to shallow networks with low accuracies. Artificial neural networks (ANN), however, have the backpropagation algorithm which can utilize gradient descent to train networks which are locally robust universal function approximators. But backpropagation algorithm is neither biologically plausible nor neuromorphic implementation friendly because it requires: 1) separate backward and forward passes, 2) differentiable neurons, 3) high-precision propagated errors, 4) coherent copy of weight matrices at feedforward weights and the backward pass, and 5) non-local weight update. Thus, we propose an approximation of the backpropagation algorithm completely with spiking neurons and extend it to a local weight update rule which resembles a biologically plausible learning rule spike-timing-dependent plasticity (STDP). This will enable error propagation through spiking neurons for a more biologically plausible and neuromorphic implementation friendly backpropagation algorithm for SNNs. We test the proposed algorithm on various traditional and non-traditional benchmarks with competitive results. 

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5. Biologically inspired reinforcement learning for mobile robot collision avoidance

   Shim, Myung S; Li, Peng (May 2017, Proceedings of ... International Joint Conference on Neural Networks)

   Collision avoidance is a key technology enabling applications such as autonomous vehicles and robots. Various reinforcement learning techniques such as the popular Q-learning algorithms have emerged as a promising solution for collision avoidance in robotics. While spiking neural networks (SNNs), the third generation model of neural networks, have gained increased interest due to their closer resemblance to biological neural circuits in the brain, the application of SNNs to mobile robot navigation has not been well studied. Under the context of reinforcement learning, this paper aims to investigate the potential of biologically-motivated spiking neural networks for goal-directed collision avoidance in reasonably complex environments. Unlike the existing additive reward-modulated spike timing dependent plasticity learning rule (A-RM-STDP), for the first time, we explore a new multiplicative RM-STDP scheme (M-RM-STDP) for the targeted application. Furthermore, we propose a more biologically plausible feed-forward spiking neural network architecture with fine-grained global rewards. Finally, by combining the above two techniques we demonstrate a further improved solution to collision avoidance. Our proposed approaches not only completely outperform Q-learning for cases where Q-learning can hardly reach the target without collision, but also significantly outperform a baseline SNN with A-RMSTDP in terms of both success rate and the quality of navigation trajectories. 

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