Biology subjects

Manseau, F.

Publications and source records attributed to Manseau, F..

3 recordsLinked to original sources

bioRxiv2022

Medial septum glutamate neurons are essential for spatial goal-directed memory

The medial septum (MS), a brain region containing acetylcholine, GABA and glutamate neurons, is essential for learning and memory. However, whether MS glutamate neurons contribute to memory is unknown. Here, we use calcium imaging and optogenetic silencing to determine the function of MS glutamate neurons in mice performing a spatial allocentric memory navigation task. While MS glutamate neurons appeared randomly active during free exploration, two groups of glutamate neurons emerged during training in a five-arm star maze, distinguished by their peak activity. The largest group was predominantly activated before locomotion whereas the other was primarily active when mice reached the reward site. Both populations were preferentially activated for correct over incorrect trajectories. Interestingly, optogenetic silencing of MS glutamate neurons immediately before mice start navigating, induced significant spatial memory impairments. Together these results demonstrate that MS glutamate neurons heterogeneously encode navigationally relevant information and are essential for spatial learning.

neuroscience↗

bioRxiv2020

A characterization of the electrophysiological, morphological and input domains of vasoactive intestinal peptide (VIP) interneurons in the medial entorhinal cortex (MEC)

Circuit interactions within the medial entorhinal cortex (MEC) translate movement into a coherent code for spatial location. Entorhinal principal cells are subject to strong lateral inhibition, suggesting that a disinhibitory mechanism may drive their activation. Cortical Vasoactive Intestinal Peptide (VIP) expressing inhibitory neurons predominantly contact interneurons, providing a local disinhibitory mechanism. Here, we investigate the electrophysiological and morphological properties of VIP cells using in vitro whole-cell patch clamp recordings and use rabies-mediated circuit tracing to discover long-range inputs that may modulate this population in mice. We report physiological and morphological properties of VIP cells that differ across lamina and along the dorsal-ventral MEC axis. Furthermore, we reveal long-range inputs to VIP neurons from regions known to encode proprioceptive and auditory information, including the mesencephalic trigeminal nucleus and superior para-olivary nuclei, respectively. These results characterize the properties of VIP cells and reveal sensory modalities that could drive disinhibition in the MEC.

neuroscience↗

bioRxiv2019

A probabilistic framework for decoding behavior from in vivo calcium imaging data

Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.

neuroscience↗