Department of Psychology, University of Sheffield, Sheffield S10 2TP, UK.
Neural Comput. 2012 Nov;24(11):2924-45. doi: 10.1162/NECO_a_00360. Epub 2012 Aug 24.
The basal ganglia are a subcortical group of interconnected nuclei involved in mediating action selection within cortex. A recent proposal is that this selection leads to optimal decision making over multiple alternatives because the basal ganglia anatomy maps onto a network implementation of an optimal statistical method for hypothesis testing, assuming that cortical activity encodes evidence for constrained gaussian-distributed alternatives. This letter demonstrates that this model of the basal ganglia extends naturally to encompass general Bayesian sequential analysis over arbitrary probability distributions, which raises the proposal to a practically realizable theory over generic perceptual hypotheses. We also show that the evidence in this model can represent either log likelihoods, log-likelihood ratios, or log odds, all leading proposals for the cortical processing of sensory data. For these reasons, we claim that the basal ganglia optimize decision making over general perceptual hypotheses represented in cortex. The relation of this theory to cortical encoding, cortico-basal ganglia anatomy, and reinforcement learning is discussed.
基底神经节是一组位于皮质下的相互连接的核团，参与调节皮质内的动作选择。最近的一项建议是，这种选择导致了对多个替代方案的最佳决策，因为基底神经节的解剖结构映射到一个最优统计方法的网络实现，用于假设检验，假设皮质活动编码了受约束的高斯分布替代方案的证据。这封信表明，基底神经节的这个模型自然地扩展到了对任意概率分布的通用贝叶斯序贯分析，这将该假说提升为一种针对通用感知假说的实际可实现理论。我们还表明，该模型中的证据可以表示对数似然、对数似然比或对数几率，所有这些都是对皮质处理感觉数据的建议。出于这些原因，我们认为基底神经节在皮质中对一般感知假设进行了优化决策。本文还讨论了该理论与皮质编码、皮质基底神经节解剖和强化学习的关系。
