Low-dimensional dynamics for working memory and time encoding

Significance A fundamental challenge in studying neural activity that evolves over time is understanding what computational capabilities can be supported by the activity and when these dynamics change to support different computational demands. We develop analyses to parcellate neural activity into computationally distinct dynamical regimes. The regimes we consider each have different computational capabilities, including the ability to keep track of time or preserve information robustly against the flow of time in working memory. We apply our analyses to neural activity and find that low-dimensional trajectories provide a mechanism for the brain to solve the problem of storing information across time while simultaneously retaining the timing information necessary for anticipating events and coordinating behavior. Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.