Piu Pietro, Fargnoli Francesco, Innocenti Alessandro, Rufa Alessandra
Department of Medicine, Surgery & Neurosciences, University of Siena, Viale Bracci 2, 53100 Siena, Italy ; Eye-Tracking & Visual Application Lab, University of Siena, Viale Bracci 2, 53100 Siena, Italy.
Department of Social, Political and Cognitive Sciences, University of Siena, Via Roma 56, 53100 Siena, Italy.
Comput Intell Neurosci. 2014;2014:383790. doi: 10.1155/2014/383790. Epub 2014 Aug 31.
A circuit of evaluation and selection of the alternatives is considered a reliable model in neurobiology. The prominent contributions of the literature to this topic are reported. In this study, valuation and choice of a decisional process during Two-Alternative Forced-Choice (TAFC) task are represented as a two-layered network of computational cells, where information accrual and processing progress in nonlinear diffusion dynamics. The evolution of the response-to-stimulus map is thus modeled by two linked diffusive modules (2LDM) representing the neuronal populations involved in the valuation-and-decision circuit of decision making. Diffusion models are naturally appropriate for describing accumulation of evidence over the time. This allows the computation of the response times (RTs) in valuation and choice, under the hypothesis of ex-Wald distribution. A nonlinear transfer function integrates the activities of the two layers. The input-output map based on the infomax principle makes the 2LDM consistent with the reinforcement learning approach. Results from simulated likelihood time series indicate that 2LDM may account for the activity-dependent modulatory component of effective connectivity between the neuronal populations. Rhythmic fluctuations of the estimate gain functions in the delta-beta bands also support the compatibility of 2LDM with the neurobiology of DM.
在神经生物学中，评估和选择备选方案的回路被认为是一种可靠的模型。报告了文献对该主题的突出贡献。在本研究中，二选一强制选择（TAFC）任务中决策过程的评估和选择被表示为一个由计算单元组成的两层网络，其中信息积累和处理以非线性扩散动力学进行。因此，响应-刺激映射的演变由两个相连的扩散模块（2LDM）建模，这两个模块代表参与决策评估和决策回路的神经元群体。扩散模型自然适合描述证据随时间的积累。在假设服从前沃尔德分布的情况下，这允许计算评估和选择中的反应时间（RT）。一个非线性传递函数整合了两层的活动。基于信息最大化原则的输入-输出映射使2LDM与强化学习方法一致。模拟似然时间序列的结果表明，2LDM可能解释神经元群体之间有效连接的活动依赖性调节成分。δ-β频段估计增益函数的节律性波动也支持2LDM与决策神经生物学的兼容性。
