Bell David, Duffy Alison, Fairhall Adrienne
Department of Physics, University of Washington, Seattle, WA 98195.
Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195.
bioRxiv. 2024 Nov 18:2024.11.18.623688. doi: 10.1101/2024.11.18.623688.
Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Oja's rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.
大脑内部的动力学可以通过为与任务目标一致的动力学提供先验支架来加速学习。面对波动的输入和生物噪声，包括突触更新和细胞死亡，这种内在动力学应该能够自我组织和自我维持。这种动力学的一个例子是序列的形成，这是神经活动中普遍存在的模式。斑胸草雀HVC中的序列生成电路为鸣叫中的运动输出提供了可靠的时间支架，并展示了在受到扰动后进行无监督恢复的显著能力。受HVC的启发，我们寻求一种局部可塑性规则，该规则能够在网络持续受到扰动的情况下组织和维持序列生成动力学。我们采用了Confavreux等人提出的元学习方法，该方法使用由突触前和突触后活动以及突触大小构建的基函数对学习规则进行参数化，并具有可调的时间常数。候选规则在初始随机网络中进行模拟，其适应性根据一个损失函数进行评估，该损失函数测量所得动力学对时间编码的保真度。我们使用这种方法引入生物噪声，迫使元学习找到稳健的解决方案。我们首先表明，在没有扰动的情况下，元学习识别出奥贾规则的时间不对称推广，该推广能够可靠地组织稀疏的序列活动。当引入突触更新时，学习到的规则包含了一种额外的稳态形式，相对于其他先前提出的规则，能更好地维持序列动力学。此外，受最近表明HVC中抑制性中间神经元突触具有可塑性的研究结果的启发，我们探索了抑制性可塑性在序列生成电路中的作用。我们发现，学习到的可塑性会根据操作调整兴奋和抑制，优于仅应用于兴奋性连接的规则。我们展示了作用于兴奋性和抑制性突触的可塑性如何能够更好地塑造兴奋性细胞动力学，以构建时间表征的支架。
