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Fakharian, M. A.

Publications and source records attributed to Fakharian, M. A..

2 recordsLinked to original sources

Synchronous spiking of cerebellar Purkinje cells during control of movements

The information that the brain transmits from one region to another is often viewed through the lens of firing rates. However, if the output neurons could vary the timing of their spikes with respect to each other, then through synchronization they could highlight information that may be critical for control of behavior. In the cerebellum, the computations that are performed by the cerebellar cortex are conveyed to the nuclei via inhibition. Yet, synchronous activity entrains nucleus neurons, making them fire. Does the cerebellar cortex rely on spike synchrony within populations of Purkinje cells (P-cells) to convey information to the nucleus? We recorded from multiple P-cells while marmosets performed saccadic eye movements and organized them into populations that shared a complex spike response to error. Before movement onset, P-cells transmitted information via a rate code: the simple spike firing rates predicted the direction and velocity of the impending saccade. However, during the saccade, the spikes became temporally aligned within the population, signaling when to stop the movement. Thus, the cerebellar cortex relies on spike synchronization within a population of P-cells, not individual firing rates, to convey to the nucleus when to stop a movement.

neuroscience↗

P-sort: an open-source software for cerebellar neurophysiology

Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents challenges for spike detection. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. Here, we analyzed data from over 300 P-cells in marmosets, macaques, and mice, using an open-source, semi-automated software called P-sort that addresses the spike identification and attribution problems. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge seemingly disparate clusters, or split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns.

neuroscience↗