Biology subjects

Peyrache, A.

Publications and source records attributed to Peyrache, A..

3 recordsLinked to original sources

bioRxiv2017

An ensemble code in medial prefrontal cortex links prior events to outcomes during learning

The prefrontal cortex is implicated in learning the rules of an environment through trial and error. But it is unclear how such learning is related to the prefrontal cor-texs role in short-term memory. Here we asked if the encoding of short-term memory in prefrontal cortex was used by rats learning decision rules in a Y-maze task. We found that a similar pattern of neural ensemble activity was selectively recalled after reinforcement for a correct decision. This reinforcement-selective recall only reliably occurred immediately before the abrupt behavioural transitions indicating successful learning of the current rule, and faded quickly thereafter. We could simultaneously decode multiple, retrospective task events from the ensemble activity, suggesting the recalled ensemble activity had multiplexed encoding of prior events. Our results suggest that successful trial-and-error learning is dependent on reinforcement tagging the relevant features of the environment to maintain in prefrontal cortex short-term memory.

neuroscience↗

bioRxiv2017

Resolving neuronal population code and coordination withgradient boosted trees

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem of neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction system, we present evidence how gradient boosted trees, a non-linear and supervised machine learning tool, learns the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. As an example, we show how the method reveals a temporally shifted coordination in a thalamo-cortical circuit during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine learning tools thus open new avenues for benchmarking model-based characterization of spike trains.

neuroscience↗

bioRxiv2017

Cocaine place conditioning strengthens location-specific hippocampal inputs to the nucleus accumbens

Conditioned place preference (CPP) is a widely used model of addiction-related behavior whose underlying mechanism is not understood. In this study, we used dual site silicon probe recordings in freely moving mice to examine interactions between the hippocampus and nucleus accumbens in cocaine CPP. We found that CPP was associated with recruitment of nucleus accumbens medium spiny neurons to fire in the cocaine-paired location, and this recruitment was driven predominantly by selective strengthening of hippocampal inputs arising from place cells that encode the cocaine-paired location. These findings provide in vivo evidence that the synaptic potentiation in the accumbens caused by repeated cocaine administration preferentially affects inputs that were active at the time of drug exposure. This provides a potential physiological mechanism by which drug use becomes associated with specific environmental contexts.

neuroscience↗