Brief epochs of beta oscillations have been implicated in sensorimotor control in the basal ganglia of task-performing healthy animals. However, which neural processes underlie their generation and how they are affected by sensorimotor processing remains unclear. To determine the mechanisms underlying transient beta oscillations in the LFP, we combined computational modeling of the subthalamo-pallidal network for the generation of beta oscillations with realistic stimulation patterns derived from single-unit data recorded from different basal ganglia subregions in rats performing a cued choice task. In the recordings, we found distinct firing patterns in the striatum, globus pallidus, and subthalamic nucleus related to sensory and motor events during the behavioral task. Using these firing patterns to generate realistic inputs to our network model led to transient beta oscillations with the same time course as the rat LFP data. In addition, our model can account for further nonintuitive aspects of beta modulation, including beta phase resets after sensory cues and correlations with reaction time. Overall, our model can explain how the combination of temporally regulated sensory responses of the subthalamic nucleus, ramping activity of the subthalamic nucleus, and movement-related activity of the globus pallidus leads to transient beta oscillations during behavior. Transient beta oscillations emerge in the normal functioning cortico-basal ganglia loop during behavior. Here, we used a unique approach connecting a computational model closely with experimental data. In this way, we achieved a simulation environment for our model that mimics natural input patterns in awake, behaving animals. We demonstrate that a computational model for beta oscillations in Parkinson's disease (PD) can also account for complex patterns of transient beta oscillations in healthy animals. Therefore, we propose that transient beta oscillations in healthy animals share the same mechanism with pathological beta oscillations in PD. This important result connects functional and pathological roles of beta oscillations in the basal ganglia.

The human visual system is developed by viewing natural scenes. In controlled experiments, natural stimuli therefore provide a realistic framework with which to study the underlying information processing steps involved in human vision. Studying the properties of natural images and their effects on the visual processing can help us to understand underlying mechanisms of visual system. In this study, we used a rapid animal vs. non-animal categorization task to assess the relationship between the reaction times of human subjects and the statistical properties of images. We demonstrated that statistical measures, such as the beta and gamma parameters of a Weibull, fitted to the edge histogram of an image, and the image entropy, are effective predictors of subject reaction times. Using these three parameters, we proposed a computational model capable of predicting the reaction times of human subjects.

Brief epochs of beta oscillations have been implicated in sensorimotor control in the basal ganglia of task-performing healthy animals. However, which neural processes underlie their generation and how they are affected by sensorimotor processing remains unclear. To determine the mechanisms underlying transient beta oscillations in the local field potential (LFP), we combined computational modeling of the subthalamo-pallidal network for the generation of beta oscillations with realistic stimulation patterns derived from single unit data. The single unit data were recorded from different basal ganglia subregions in rats performing a cued choice task. In the recordings we found distinct firing patterns in the striatum, globus pallidus and subthalamic nucleus related to sensory and motor events during the behavioral task. Using these firing patterns to generate realistic inputs to our network model lead to transient beta oscillations with the same time course as the rat LFP data. In addition, our model can account for further non-intuitive aspects of beta modulation, including beta phase resets following sensory cues and correlations with reaction time. Overall, our model can explain how the combination of temporally regulated sensory responses of the subthalamic nucleus, ramping activity of the subthalamic nucleus, and movement-related activity of the globus pallidus, leads to transient beta oscillations during behavior.Significance StatementTransient beta oscillations emerge in the normal functioning cortico-basal ganglia loop during behavior. In this work we employ a unique approach connecting a computational model closely with experimental data. In this way we achieve a simulation environment for our model that mimics natural input patterns in awake behaving animals. Using this approach we demonstrate that a computational model for beta oscillations in Parkinson’s disease can also account for complex patterns of transient beta oscillations in healthy animals. Therefore, we propose that transient beta oscillations in healthy animals share the same mechanism with pathological beta oscillations in Parkinson’s disease. This important result connects functional and pathological roles of beta oscillations in the basal ganglia.

Principles of efficient coding suggest that the peripheral units of any sensory processing system are designed for efficient coding. The function of the lateral geniculate nucleus (LGN) as an early stage in the visual system is not well understood. Some findings indicate that similar to the retina that decorrelates input signals spatially, the LGN tends to perform a temporal decorrelation. There is evidence suggesting that corticogeniculate connections may account for this decorrelation in the LGN. In this study, we propose a computational model based on biological evidence reported by Wang et al. (2006), who demonstrated that the influence pattern of V1 feedback is phase-reversed. The output of our model shows how corticogeniculate connections decorrelate LGN responses and make an efficient representation. We evaluated our model using criteria that have previously been tested on LGN neurons through cell recording experiments, including sparseness, entropy, power spectra, and information transfer. We also considered the role of the LGN in higher-order visual object processing, comparing the categorization performance of human subjects with a cortical object recognition model in the presence and absence of our LGN input-stage model. Our results show that the new model that considers the role of the LGN, more closely follows the categorization performance of human subjects.