Modulation of Neural Spiking in Motor Cortex-Cerebellar Networks during Sleep Spindles.

Sleep spindles appear to play an important role in learning new motor skills. Motor skill learning engages several brain regions with two important areas being the motor cortex (M1) and the cerebellum (CB). However, the neurophysiological processes in these areas during sleep, especially how spindle oscillations affect local and cross-region spiking, are not fully understood. We recorded an activity from the M1 and cerebellar cortex in eight rats during spontaneous activity to investigate how sleep spindles in these regions are related to local spiking as well as cross-region spiking. We found that M1 firing was significantly changed during both M1 and CB spindles, and this spiking occurred at a preferred phase of the spindle. On average, M1 and CB neurons showed most spiking at the M1 or CB spindle peaks. These neurons also developed a preferential phase locking to local or cross-area spindles with the greatest phase-locking value at spindle peaks; however, this preferential phase locking was not significant for cerebellar neurons when compared with CB spindles. Additionally, we found that the percentage of task-modulated cells in the M1 and CB that fired with nonuniform spike phase distribution during M1/CB spindle peaks were greater in the rats that learned a reach-to-grasp motor task robustly. Finally, we found that spindle band LFP coherence (for M1 and CB LFPs) showed a positive correlation with success rate in the motor task. These findings support the idea that sleep spindles in both the M1 and CB recruit neurons that participate in the awake task to support motor memory consolidation.

Emergent Low-Frequency Activity in Cortico-Cerebellar Networks with Motor Skill Learning.

The motor cortex controls skilled arm movement by recruiting a variety of targets in the nervous system, and it is important to understand the emergent activity in these regions as refinement of a motor skill occurs. One fundamental projection of the motor cortex (M1) is to the cerebellum. However, the emergent activity in the motor cortex and the cerebellum that appears as a dexterous motor skill is consolidated is incompletely understood. Here, we report on low-frequency oscillatory (LFO) activity that emerges in cortico-cerebellar networks with learning the reach-to-grasp motor skill. We chronically recorded the motor and the cerebellar cortices in rats, which revealed the emergence of coordinated movement-related activity in the local-field potentials as the reaching skill consolidated. Interestingly, we found this emergent activity only in the rats that gained expertise in the task. We found that the local and cross-area spiking activity was coordinated with LFOs in proficient rats. Finally, we also found that these neural dynamics were more prominently expressed during accurate behavior in the M1. This work furthers our understanding on emergent dynamics in the cortico-cerebellar loop that underlie learning and execution of precise skilled movement.

A unified model of rule-set learning and selection.

The ability to focus on relevant information and ignore irrelevant information is a fundamental part of intelligent behavior. It not only allows faster acquisition of new tasks by reducing the size of the problem space but also allows for generalizations to novel stimuli. Task-switching, task-sets, and rule-set learning are all intertwined with this ability. There are many models that attempt to individually describe these cognitive abilities. However, there are few models that try to capture the breadth of these topics in a unified model and fewer still that do it while adhering to the biological constraints imposed by the findings from the field of neuroscience. Presented here is a comprehensive model of rule-set learning and selection that can capture the learning curve results, error-type data, and transfer effects found in rule-learning studies while also replicating the reaction time data and various related effects of task-set and task-switching experiments. The model also factors in many disparate neurological findings, several of which are often disregarded by similar models.

Simulating the Effect of Reinforcement Learning on Neuronal Synchrony and Periodicity in the Striatum.

The study of rhythms and oscillations in the brain is gaining attention. While it is unclear exactly what the role of oscillation, synchrony, and rhythm is, it appears increasingly likely that synchrony is related to normal and abnormal brain states and possibly cognition. In this article, we explore the relationship between basal ganglia (BG) synchrony and reinforcement learning. We simulate a biologically-realistic model of the striatum initially proposed by Ponzi and Wickens (2010) and enhance the model by adding plastic cortico-BG synapses that can be modified using reinforcement learning. The effect of reinforcement learning on striatal rhythmic activity is then explored, and disrupted using simulated deep brain stimulation (DBS). The stimulator injects current in the brain structure to which it is attached, which affects neuronal synchrony. The results show that training the model without DBS yields a high accuracy in the learning task and reduced the number of active neurons in the striatum, along with an increased firing periodicity and a decreased firing synchrony between neurons in the same assembly. In addition, a spectral decomposition shows a stronger signal for correct trials than incorrect trials in high frequency bands. If the DBS is ON during the training phase, but not the test phase, the amount of learning in the model is reduced, along with firing periodicity. Similar to when the DBS is OFF, spectral decomposition shows a stronger signal for correct trials than for incorrect trials in high frequency domains, but this phenoemenon happens in higher frequency bands than when the DBS is OFF. Synchrony between the neurons is not affected. Finally, the results show that turning the DBS ON at test increases both firing periodicity and striatal synchrony, and spectral decomposition of the signal show that neural activity synchronizes with the DBS fundamental frequency (and its harmonics). Turning the DBS ON during the test phase results in chance performance regardless of whether the DBS was ON or OFF during training. We conclude that reinforcement learning is related to firing periodicity, and a stronger signal for correct trials when compared to incorrect trials in high frequency bands.