Neural Computations in a Dynamical System with Multiple Time Scales - Mi, Yuanyuan; Lin, Xiaohan; Wu, Si (Frontiers in Computational Neuroscience, 2016)
Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at single neurons, and short-term facilitation (STF) and depression (STD) at neuronal synapses. These dynamical features typically covers a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what the computational benefit for the brain to have such variability in short-term dynamics is. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in their dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.