Studies of neural oscillations in the beta band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) have demonstrated modulations in beta-band power associated with sensory and motor events on time scales of 1 s or more, and have shown that these are exaggerated in Parkinson's disease. However, even early reports of beta activity noted extremely fleeting episodes of beta-band oscillation lasting <150 ms. Because the interpretation of possible functions for beta-band oscillations depends strongly on the time scale over which they occur, and because of these oscillations' potential importance in Parkinson's disease and related disorders, we analyzed in detail the distributions of duration and power for beta-band activity in a large dataset recorded in the striatum and motor-premotor cortex of macaque monkeys performing reaching tasks. Both regions exhibited typical beta-band suppression during movement and postmovement rebounds of up to 3 s as viewed in data averaged across trials, but single-trial analysis showed that most beta oscillations occurred in brief bursts, commonly 90-115 ms long. In the motor cortex, the burst probabilities peaked following the last movement, but in the striatum, the burst probabilities peaked at task end, after reward, and continued through the postperformance period. Thus, what appear to be extended periods of postperformance beta-band synchronization reflect primarily the modulated densities of short bursts of synchrony occurring in region-specific and task-time-specific patterns. We suggest that these short-time-scale events likely underlie the functions of most beta-band activity, so that prolongation of these beta episodes, as observed in Parkinson's disease, could produce deleterious network-level signaling.basal ganglia | local field potentials | beta band | sequential movement | synchronization O scillations of brain activity in the beta band (13-30 Hz) have been implicated in sensorimotor control and integration (1-5) and are pathologically synchronized and exaggerated in Parkinson's disease (6-11). Although reports have published examples of very brief (<150 ms) bursts of beta-band oscillation (12-15), the analysis of beta-band activity has focused primarily on data averaged over trials, which show variations in average beta-band power occurring on a time scale of seconds.To address the apparent discrepancy in time scales between the single-trial results and the trial-averaged results, we analyzed the relationship of brief beta bursts as viewed at a single-trial level to the substantially slower variations in trial-averaged power that are conventionally referred to as periods of "synchronization" or "desynchronization" of beta-band activity. We examined betaband activity recorded in two regions of prime clinical interest, the motor-premotor regions of the neocortex and the striatum, in macaque monkeys performing well-learned movement sequences. Our findings suggest that these regions exhibit different peak times of synchronization of beta bursting during...

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Working memory is critical to cognition, decoupling behavior from the immediate world. Yet, it is imperfect; internal noise introduces errors into memory representations. Such errors have been shown to accumulate over time and increase with the number of items simultaneously held in working memory. Here, we show that discrete attractor dynamics mitigate the impact of noise on working memory. These dynamics pull memories towards a few stable representations in mnemonic space, inducing a bias in memory representations but reducing the effect of random diffusion. Model-based and model-free analyses of human and monkey behavior show that discrete attractor dynamics account for the distribution, bias, and precision of working memory reports. Furthermore, attractor dynamics are adaptive. They increase in strength as noise increases with memory load and experiments in humans show these dynamics adapt to the statistics of the environment, such that memories drift towards contextually-predicted values. Together, our results suggest attractor dynamics mitigate errors in working memory by counteracting noise and integrating contextual information into memories.

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.