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Karimimehr, S.

Publications and source records attributed to Karimimehr, S..

2 recordsLinked to original sources

Distinct signatures of reward value and variance on spiking activity and functional interactions in monkey fronto-parietal areas

Theories of executive function propose that controlled information processing is costly and is allocated according to the behavioral benefits it brings. Computational theories predict that the benefits of new information depend on prior uncertainty, but the cellular effects of uncertainty on the executive network are incompletely understood. Using simultaneous recordings in monkeys, we reveal several mechanisms by which the fronto-parietal network reacts to uncertainty independently of average reward gains. We show that the variance of expected rewards, independently of the value of the rewards, was represented in single neuron and population spiking activity and local field potential (LFP) oscillations. Moreover, uncertainty asymmetrically affected the coherence between spikes and LFPs, selectively suppressing information transmission from the frontal to the parietal lobe but enhancing transmission from the parietal to the frontal lobe, consistent with Bayesian principles of optimal inference under uncertainty.

neuroscience↗

A fully automated spike sorting algorithm using t-distributed neighbor embedding and density based clustering

In this study, a new spike sorting method was developed based on a combination of two methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Parameters of both methods were simultaneously optimized using a Genetic Algorithm (GA) using a simulated dataset containing 2 to 20 simultaneously recorded neurons. The performance of this method was evaluated using both the simulated dataset as well as real multichannel electrophysiological data. The results indicated that our fully automated algorithm using t-SNE-DBSCAN outperforms other state-of-the-art algorithms and human experts in spike sorting especially when there are a large number of simultaneously recorded units. Our algorithm also determines the noise waveforms and has an overall high sensitivity, precision and accuracy for correctly classifying waveforms belonging to each neuron (all >90%) without the need for manual corrections afterwards. Our method can be a crucial part of the analysis pipeline in particular when manual sorting of units is becoming prohibitive due to the sheer number of recorded neurons per session.

neuroscience↗