Department of Biomedical Engineering, Boston University, Boston, 02115, MA, USA.
Undergraduate Program in Neuroscience, Boston University, Boston, 02115, MA, USA.
Nat Commun. 2023 Oct 16;14(1):6510. doi: 10.1038/s41467-023-41752-2.
We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and fast initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial conditions combine with sensory evidence to induce decision-related dynamics in PMd.
我们采用动态系统的观点来理解与决策相关的神经活动，这是一个尚未解决的基本问题。该观点认为，时变的神经活动可以用一个带有初始条件的状态方程来描述，并通过在每个时间步长上结合递归活动和输入来随时间演变。我们假设了各种决策的动力学机制，在模型中对其进行了模拟，以得出预测，并通过检查猴子在执行感知决策任务时的背侧运动前皮层（PMd）神经元的放电率来评估这些预测。刺激前的神经活动（即初始条件）预测了刺激后的神经轨迹，与 RT 和前一个试验的结果相关，但与选择无关。刺激后的动力学既取决于感觉证据，也取决于初始条件，较容易的刺激和较快的初始条件导致与选择相关的动力学最快。总的来说，这些结果表明，初始条件与感觉证据相结合，在 PMd 中引起与决策相关的动力学。
