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Connecting Biological Detail With Neural Computation: Application to the Cerebellar Granule–Golgi Microcircuit
Topics in Cognitive Science ( IF 2.9 ) Pub Date : 2021-06-19 , DOI: 10.1111/tops.12536
Andreas Stöckel 1 , Terrence C Stewart 2 , Chris Eliasmith 1
Affiliation  

Neurophysiology and neuroanatomy constrain the set of possible computations that can be performed in a brain circuit. While detailed data on brain microcircuits is sometimes available, cognitive modelers are seldom in a position to take these constraints into account. One reason for this is the intrinsic complexity of accounting for biological mechanisms when describing cognitive function. In this paper, we present multiple extensions to the neural engineering framework (NEF), which simplify the integration of low-level constraints such as Dale's principle and spatially constrained connectivity into high-level, functional models. We focus on a model of eyeblink conditioning in the cerebellum, and, in particular, on systematically constructing temporal representations in the recurrent granule–Golgi microcircuit. We analyze how biological constraints impact these representations and demonstrate that our overall model is capable of reproducing key properties of eyeblink conditioning. Furthermore, since our techniques facilitate variation of neurophysiological parameters, we gain insights into why certain neurophysiological parameters may be as observed in nature. While eyeblink conditioning is a somewhat primitive form of learning, we argue that the same methods apply for more cognitive models as well. We implemented our extensions to the NEF in an open-source software library named “NengoBio” and hope that this work inspires similar attempts to bridge low-level biological detail and high-level function.

中文翻译:

将生物细节与神经计算联系起来:在小脑颗粒-高尔基体微电路中的应用

神经生理学和神经解剖学限制了可以在大脑回路中执行的一组可能的计算。虽然有时可以获得有关大脑微电路的详细数据,但认知建模者很少能够将这些限制因素考虑在内。原因之一是在描述认知功能时考虑生物机制的内在复杂性。在本文中,我们提出了神经工程框架 (NEF) 的多个扩展,它简化了将低级约束(例如 Dale 原理和空间约束连接)集成到高级功能模型中的过程。我们专注于小脑中的眨眼调节模型,特别是系统地构建循环颗粒 - 高尔基体微电路中的时间表征。我们分析了生物约束如何影响这些表示,并证明我们的整体模型能够再现眨眼调节的关键特性。此外,由于我们的技术促进了神经生理学参数的变化,我们深入了解了为什么某些神经生理学参数可能与自然界中观察到的一样。虽然眨眼调节是一种比较原始的学习形式,但我们认为相同的方法也适用于更多的认知模型。我们在名为“NengoBio”的开源软件库中实现了对 NEF 的扩展,并希望这项工作能激发类似的尝试,以连接低级生物细节和高级功能。由于我们的技术促进了神经生理学参数的变化,我们深入了解了为什么某些神经生理学参数可能与自然界中观察到的一样。虽然眨眼调节是一种比较原始的学习形式,但我们认为相同的方法也适用于更多的认知模型。我们在名为“NengoBio”的开源软件库中实现了对 NEF 的扩展,并希望这项工作能激发类似的尝试,以连接低级生物细节和高级功能。由于我们的技术促进了神经生理学参数的变化,我们深入了解了为什么某些神经生理学参数可能与自然界中观察到的一样。虽然眨眼调节是一种比较原始的学习形式,但我们认为相同的方法也适用于更多的认知模型。我们在名为“NengoBio”的开源软件库中实现了对 NEF 的扩展,并希望这项工作能激发类似的尝试,以连接低级生物细节和高级功能。
更新日期:2021-07-13
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