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Euston, D. R.

Publications and source records attributed to Euston, D. R..

2 recordsLinked to original sources

Amphetamine reduces utility encoding and stabilizes neural dynamics in rat anterior cingulate cortex

The anterior cingulate cortex (ACC) appears to support decisions by encoding the effort-reward utility of choice options. We show here that d-amphetamine (AMPH) has dose-dependent effects on this encoding and on neural dynamics in rat ACC that are concordant with its behavioral effects. Low-dose AMPH increased task engagement and had mild effects on neural encoding, whereas high doses disrupted utility signaling and decreased task engagement. The disruption involved reduced reward signaling and compressed effort-reward encoding of utility cells, which corresponded with reduced reward consumption behaviors. Furthermore, low-dose AMPH stabilized and accelerated trajectories of neural activity in state-space, whereas high-dose AMPH destabilized trajectories. We propose that low-dose AMPH increases both excitability and stability, which preserves information and accelerates evolution of a neural script for task execution. Excessive excitability at high doses overcomes stability enhancement to suppress weakly encoded features (e.g. reward) and cause deviation from the script, which interrupts task performance. Significance StatementAmphetamine reduced reward signaling by individual neurons in rat prefrontal cortex, but increased the stability of ensemble dynamics. These effects account for animals increased task engagement, despite reduced reward intake.

neuroscience↗

Unsupervised detection of cell-assembly sequences with edit similarity score

Cell assembly is a hypothetical functional unit of information processing in the brain. While technologies for recording large-scale neural activity have been advanced, mathematical methods to analyze sequential activity patterns of cell-assembly are severely limited. Here, we propose a method to extract cell-assembly sequences repeated at multiple time scales and various precisions from irregular neural population activity. The key technology is to combine \"edit similarity\" in computer science with machine-learning clustering algorithms, where the former defines a \"distance\" between two strings as the minimal number of operations required to transform one string to the other. Our method requires no external references for pattern detection, and is tolerant of spike timing jitters and length irregularity in assembly sequences. These virtues enabled simultaneous automatic detections of hippocampal place-cell sequences during locomotion and their time-compressed replays during resting states. Furthermore, our method revealed previously undetected cell-assembly structure in the rat prefrontal cortex during goal-directed behavior. Thus, our method expands the horizon of cell-assembly analysis.

neuroscience↗