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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)

   Abstract 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. 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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3. 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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4. 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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5. Identifying Learning Rules From Neural Network Observables

   Nayebi, A; Srivastava, S; Ganguli, S; Yamins, D. (December 2020, Advances in neural information processing systems)

   null (Ed.)

   The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem. Simulating idealized neuroscience experiments with artificial neural networks, we generate a large-scale dataset of learning trajectories of aggregate statistics measured in a variety of neural network architectures, loss functions, learning rule hyperparameters, and parameter initializations. We then take a discriminative approach, training linear and simple non-linear classifiers to identify learning rules from features based on these observables. We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes, and that these results generalize to limited access to the trajectory and held-out architectures and learning curricula. We identify the statistics of each observable that are most relevant for rule identification, finding that statistics from network activities across training are more robust to unit undersampling and measurement noise than those obtained from the synaptic strengths. Our results suggest that activation patterns, available from electrophysiological recordings of post-synaptic activities on the order of several hundred units, frequently measured at wider intervals over the course of learning, may provide a good basis on which to identify learning rules. 

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