Desiderata for Normative Models of Synaptic Plasticity
Desiderata for Normative Models of Synaptic Plasticity
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DOI：
10.1162/neco_a_01671
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发表时间：
2024-06-07
影响因子：
2.9
通讯作者：
Savin，Cristina
中科院分区：
文献类型：
--
作者：
Bredenberg，Colin;Savin，Cristina
突触可塑性的规范模型使用计算原理来预测行为和网络水平的适应性现象。近年来，这一领域的理论工作出现了爆炸式增长，但实验证实仍然有限。在这篇综述中，我们组织工作规范的可塑性模型的一组desiderata，当满足时，旨在确保一个给定的模型表现出明确的可塑性和适应行为之间的联系，是一致的，与已知的生物学证据神经可塑性，并产生具体的可测试的预测。作为一个原型，我们包括一个详细的分析，增强算法。我们还讨论了新的模型如何开始改善所确定的标准，并提出进一步发展的途径。总的来说，我们提供了一个概念性的指导，以帮助发展神经学习理论，是精确的，强大的，和实验测试。
Normative models of synaptic plasticity use computational rationales to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work in this realm, but experimental confirmation remains limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata that, when satisfied, are designed to ensure that a given model demonstrates a clear link between plasticity and adaptive behavior, is consistent with known biological evidence about neural plasticity and yields specific testable predictions. As a prototype, we include a detailed analysis of the REINFORCE algorithm. We also discuss how new models have begun to improve on the identified criteria and suggest avenues for further development. Overall, we provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.