Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.
Cereb Cortex. 2014 Mar;24(3):677-90. doi: 10.1093/cercor/bhs348. Epub 2012 Nov 11.
This paper addresses the question how generic microcircuits of neurons in different parts of the cortex can attain and maintain different computational specializations. We show that if stochastic variations in the dynamics of local microcircuits are correlated with signals related to functional improvements of the brain (e.g. in the control of behavior), the computational operation of these microcircuits can become optimized for specific tasks such as the generation of specific periodic signals and task-dependent routing of information. Furthermore, we show that working memory can autonomously emerge through reward-modulated Hebbian learning, if needed for specific tasks. Altogether, our results suggest that reward-modulated synaptic plasticity can not only optimize the network parameters for specific computational tasks, but also initiate a functional rewiring that re-programs microcircuits, thereby generating diverse computational functions in different generic cortical microcircuits. On a more general level, this work provides a new perspective for a standard model for computations in generic cortical microcircuits (liquid computing model). It shows that the arguably most problematic assumption of this model, the postulate of a teacher that trains neural readouts through supervised learning, can be eliminated. We show that generic networks of neurons can learn numerous biologically relevant computations through trial and error.
本文探讨了一个问题，即不同大脑皮层区域的神经元通用微电路如何实现并保持不同的计算专业化。我们表明，如果局部微电路动力学中的随机变化与与大脑功能改善相关的信号（例如在行为控制中）相关联，那么这些微电路的计算操作可以针对特定任务进行优化，例如生成特定的周期性信号和任务相关的信息路由。此外，我们表明，如果特定任务需要，工作记忆可以通过奖励调节的赫布学习自主出现。总的来说，我们的结果表明，奖励调节的突触可塑性不仅可以优化特定计算任务的网络参数，还可以启动功能重布线，从而在不同的通用皮质微电路中产生多样化的计算功能。在更普遍的层面上，这项工作为通用皮质微电路计算的标准模型（液体计算模型）提供了新的视角。它表明，该模型最有争议的假设之一，即通过监督学习训练神经读出器的教师假设，可以被消除。我们表明，通用神经元网络可以通过反复试验学习许多与生物学相关的计算。
