Neural population dynamics of computing with synaptic modulations.
Neural population dynamics of computing with synaptic modulations.
复制标题
DOI：
10.7554/elife.83035
复制
发表时间：
2023-02-23
期刊：
影响因子：
7.7
通讯作者：
Mihalas S
中科院分区：
文献类型：
--
作者：
Aitken K;Mihalas S
除了长时间尺度的重新布线之外，大脑中的突触还受到以更快的时间尺度发生的显着调制，这赋予大脑处理信息的额外手段。尽管如此，像循环神经网络 (RNN) 这样的大脑模型在训练后通常会冻结其权重，依靠存储在神经元活动中的内部状态来保存任务相关信息。在这项工作中，我们研究了一个网络的计算潜力和由此产生的动力学，该网络在推理过程中仅依赖于突触调制来处理任务相关信息，即多可塑性网络（MPN）。由于 MPN 没有循环连接，这使我们能够研究仅由突触调制贡献的计算能力和动态行为。 MPN 的通用性使我们的结果能够应用于从短期突触可塑性 (STSP) 到较慢的调制（例如尖峰时间依赖性可塑性 (STDP)）的突触调制机制。我们彻底检查了在基于集成的任务上训练的 MPN 的神经群体动力学，并将其与已知的 RNN 动力学进行比较，发现两者具有根本不同的吸引子结构。我们发现，动态方面的差异使得 MPN 在多项神经科学相关测试中表现优于 RNN。通过一系列神经科学任务训练 MPN，我们发现它在这种设置中的计算能力与使用循环连接进行计算的网络相当。总而言之，我们相信这项工作展示了突触调制计算的计算可能性，并强调了这些计算的重要主题，以便可以在类脑系统中识别它们。
In addition to long-timescale rewiring, synapses in the brain are subject to significant modulation that occurs at faster timescales that endow the brain with additional means of processing information. Despite this, models of the brain like recurrent neural networks (RNNs) often have their weights frozen after training, relying on an internal state stored in neuronal activity to hold task-relevant information. In this work, we study the computational potential and resulting dynamics of a network that relies solely on synapse modulation during inference to process task-relevant information, the multi-plasticity network (MPN). Since the MPN has no recurrent connections, this allows us to study the computational capabilities and dynamical behavior contributed by synapses modulations alone. The generality of the MPN allows for our results to apply to synaptic modulation mechanisms ranging from short-term synaptic plasticity (STSP) to slower modulations such as spike-time dependent plasticity (STDP). We thoroughly examine the neural population dynamics of the MPN trained on integration-based tasks and compare it to known RNN dynamics, finding the two to have fundamentally different attractor structure. We find said differences in dynamics allow the MPN to outperform its RNN counterparts on several neuroscience-relevant tests. Training the MPN across a battery of neuroscience tasks, we find its computational capabilities in such settings is comparable to networks that compute with recurrent connections. Altogether, we believe this work demonstrates the computational possibilities of computing with synaptic modulations and highlights important motifs of these computations so that they can be identified in brain-like systems.