Neural dynamics implement a flexible decision bound with a fixed firing rate for choice: a model-based hypothesis

Frontiers in Neuroscience

Da-Hui eWang¹  Gunnar eBlohm²  Dominic eStandage² 

[1] Beijing Normal University;Queen's University;

关键词: decision threshold;    neural dynamics;    Speed-accuracy trade-off;    bounded integration;    threshold-baseline difference;   

DOI  :  10.3389/fnins.2014.00318

来源: DOAJ

【 摘 要 】

Decisions are faster and less accurate when conditions favour speed, and are slower and more accurate when they favour accuracy. This speed-accuracy trade-off (SAT) can be explained by the principles of bounded integration, where noisy evidence is integrated until it reaches a bound. Higher bounds reduce the impact of noise by increasing integration times, supporting higher accuracy (vice versa for speed). These computations are hypothesized to be implemented by feedback inhibition between neural populations selective for the decision alternatives, each of which corresponds to an attractor in the space of network states. Since decision-correlated neural activity typically reaches a fixed rate at the time of commitment to a choice, it has been hypothesized that the neural implementation of the bound is fixed, and that the SAT is supported by a common input to the populations integrating evidence. According to this hypothesis, a stronger common input reduces the difference between a baseline firing rate and a threshold rate for enacting a choice. In simulations of a two-choice decision task, we use a reduced version of a biophysically-based network model (Wong & Wang, 2006) to show that a common input can control the SAT, but that changes to the threshold-baseline difference are epiphenomenal. Rather, the SAT is controlled by changes to network dynamics. A stronger common input decreases the model’s effective time constant of integration and changes the shape of the attractor landscape, so the initial state is in a more error-prone position. Thus, a stronger common input reduces decision time and lowers accuracy. The change in dynamics also renders firing rates higher under speed conditions at the time that an ideal observer can make a decision from network activity. The difference between this rate and the baseline rate is actually greater under speed conditions than accuracy conditions, suggesting that the bound is not implemented by firing rates per se.

【 授权许可】

Unknown