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Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time. 

Survival relies on the ability to flexibly choose between different actions according to varying environmental circumstances. Many lines of evidence indicate that action selection involves signaling in corticostriatal circuits, including the orbitofrontal cortex (OFC) and dorsomedial striatum (DMS). While choice-specific responses have been found in individual neurons from both areas, it is unclear whether populations of OFC or DMS neurons are better at encoding an animal’s choice. To address this, we trained head-fixed mice to perform an auditory guided two-alternative choice task, which required moving a joystick forward or backward. We then used silicon microprobes to simultaneously measure the spiking activity of OFC and DMS ensembles, allowing us to directly compare population dynamics between these areas within the same animals. Consistent with previous literature, both areas contained neurons that were selective for specific stimulus-action associations. However, analysis of concurrently recorded ensemble activity revealed that the animal’s trial-by-trial behavior could be decoded more accurately from DMS dynamics. These results reveal substantial regional differences in encoding action selection, suggesting that DMS neural dynamics are more specialized than OFC at representing an animal’s choice of action. NEW & NOTEWORTHY While previous literature shows that both orbitofrontal cortex (OFC) and dorsomedial striatum (DMS) represent information relevant to selecting specific actions, few studies have directly compared neural signals between these areas. Here we compared OFC and DMS dynamics in mice performing a two-alternative choice task. We found that the animal’s choice could be decoded more accurately from DMS population activity. This work provides among the first evidence that OFC and DMS differentially represent information about an animal’s selected action. 

The striatum plays an important role in learning, selecting, and executing actions. As a major input hub of the basal ganglia, it receives and processes a diverse array of signals related to sensory, motor, and cognitive information. Aberrant neural activity in this area is implicated in a wide variety of neurological and psychiatric disorders. It is therefore important to understand the hallmarks of disrupted striatal signal processing. This review surveys literature examining howin vivostriatal microcircuit dynamics are impacted in animal models of one of the most widely studied movement disorders, Parkinson's disease. The review identifies four major features of aberrant striatal dynamics: altered relative levels of direct and indirect pathway activity, impaired information processing by projection neurons, altered information processing by interneurons, and increased synchrony.