Department of Neurobiology, Weizmann Institute of Science, Rehovot 76000, Israel.
Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA.
Neuron. 2017 Jun 7;94(5):1027-1032. doi: 10.1016/j.neuron.2017.03.048.
The dilemma that neurotheorists face is that (1) detailed biophysical models that can be constrained by direct measurements, while being of great importance, offer no immediate insights into cognitive processes in the brain, and (2) high-level abstract cognitive models, on the other hand, while relevant for understanding behavior, are largely detached from neuronal processes and typically have many free, experimentally unconstrained parameters that have to be tuned to a particular data set and, hence, cannot be readily generalized to other experimental paradigms. In this contribution, we propose a set of "first principles" for neurally inspired cognitive modeling of memory retrieval that has no biologically unconstrained parameters and can be analyzed mathematically both at neuronal and cognitive levels. We apply this framework to the classical cognitive paradigm of free recall. We show that the resulting model accounts well for puzzling behavioral data on human participants and makes predictions that could potentially be tested with neurophysiological recording techniques.
神经理论家面临的困境是，（1）虽然详细的生物物理模型可以通过直接测量来约束，但它们对大脑中的认知过程没有即时的洞察力，（2）另一方面，高层次的抽象认知模型虽然与理解行为有关，但与神经元过程基本脱节，通常有许多自由的、实验上不受约束的参数，这些参数必须针对特定的数据集进行调整，因此不能轻易推广到其他实验范式。在本研究中，我们提出了一套“第一原理”，用于记忆检索的神经启发式认知建模，该模型没有生物学上不受约束的参数，可以在神经元和认知层面上进行数学分析。我们将该框架应用于经典的自由回忆认知范式。我们表明，由此产生的模型很好地解释了人类参与者令人困惑的行为数据，并做出了预测，这些预测可能可以通过神经生理学记录技术进行检验。
