Hagai Lalazar

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Using machine learning algorithms to decode intended behavior from neural activity serves a dual purpose. First, these tools allow patients to interact with their environment through a Brain-Machine Interface (BMI). Second, analyzing the characteristics of such methods can reveal the relative significance of various features of neural activity, task(More)
The neural basis of the internal models used in sensorimotor transformations is beginning to be uncovered. Sensorimotor learning involves the modification of such models. Different stages of sensory-motor processing have been explored with a continuum of experimental tasks, from learning arbitrary associations of sensory cues to movements, to adapting to(More)
Neuronal responses characterized by regular tuning curves are typically assumed to arise from structured synaptic connectivity. However, many responses exhibit both regular and irregular components. To address the relationship between tuning curve properties and underlying circuitry, we analyzed neuronal activity recorded from primary motor cortex (M1) of(More)
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