Biology subjects

Etter, G.

Publications and source records attributed to Etter, G..

6 recordsLinked to original sources

bioRxiv2026

Theta sequences support real-time spatial memory retrieval

Oscillations play a critical role in orchestrating the timing of neural activity during cognitive processes. In the hippocampus, place cells encode spatial locations, and their activity is organized by theta oscillations into sequences that represent past, present, and potential future states. Recently, scrambling theta oscillations in vivo was shown to impair memory retrieval during navigation and decision-making, while leaving place cell activity intact. This surprising result highlights that the exact mechanisms underlying real-time memory encoding and retrieval in the hippocampus remain undefined. Here, we use a spiking neural network model of hippocampal theta sequences to evaluate the consequences of theta frequency scrambling on both representational learning and real-time spatial memory retrieval. We first show that frequency scrambling disrupts theta sequences and phase precession. Specifically, increased frequency scrambling was associated with a gradually degraded relationship between theta phase and spike timing, and ultimately abolished the predictive nature of place fields. When hippocampal spikes are required to solve a T-maze alternation task, the number of successful trials and navigation efficiency decreased with increasing values of frequency scrambling. These results could advance our understanding of how theta modulated neural activity supports memory-guided decision-making in real time and demonstrate why navigation fails when disrupting theta, despite intact spatial representations.

neuroscience↗

bioRxiv2024

Structured and unstructured reactivations during REM sleep are modulated by novel experiences

Mammalian sleep alternates between rapid eye movement (REM) and non-REM (NREM) phases, each with distinct patterns of neural activity. The replay of waking experience during NREM sleep is a well-established substrate for memory consolidation, but whether comparable reactivation occurs during REM sleep has remained unclear, owing to the sparse firing that characterizes this state and the lack of robust methods for detecting replay within it. Here we combined calcium imaging with electrophysiology to monitor large populations of hippocampal CA1 neurons in mice and applied complementary analytical approaches to test for neural reactivation during REM sleep. We found that structured waking activity is re-expressed during REM sleep across a range of temporal organizations, from the reactivation of co-active cell assemblies to temporally ordered sequential replay. This reactivation was selectively shaped by the behavioral context of prior experience. Novel experience enhanced sequential replay and refined assembly composition, selectively reducing the participation of weakly contributing neurons; anxiogenic experience, by contrast, increased the frequency of assembly reactivation and reinforced the recruitment of strongly contributing neurons. REM sleep thus emerges as an active stage of memory processing that reorganizes recent experience according to its behavioral significance, engaging distinct operations for novel and for emotionally salient events.

neuroscience↗

bioRxiv2023

A population code for idiothetic representations in the hippocampal-septal circuit

The hippocampus is a higher-order brain structure responsible for encoding new episodic memories and predicting future outcomes. In absence of external stimuli, neurons in the hippocampus express sequential activities which have been proposed to support path integration by tracking elapsed time, distance traveled, and other idiothetic variables. On the other hand, with sufficient external sensory inputs, hippocampal neurons can fire with respect to allocentric cues. Previously, these idiothetic codes have been described in conditions where running speed is clamped experimentally. To this day, the exact determinants of idiothetic representations during free navigation remain unclear. Additionally, whether CA1 and CA3 temporal and distance codes are transmitted downstream to the lateral septum has not been established. Here, we develop an unsupervised model trained to compress neural information with minimal loss, and find that we can efficiently decode elapsed time and distance traveled from low-dimensional embeddings of neural activity in freely moving mice. We also developed unbiased information metrics that are minimally sensitive to quantization parameters and enable comparisons across modalities and brain regions. In more than 30,000 CA1 pyramidal neurons, we show that spatiotemporal information is represented as a mixture of idiothetic and allocentric information, the balance of which is dictated by task demand and environmental conditions. In particular, we find that a subset of CA1 pyramidal neurons encode the spatiotemporal distance to rewards. Single cell and population statistics across the hippocampal-septal circuit reveal that idiothetic variables emerge in CA1 and are integrated postsynaptically in the lateral septum. Finally, we implement a computational model trained to replicate real world neural activity, and find that grid cells could provide a plausible input for CA1 representations of time and distance. Altogether, our results suggest that hippocampal CA1 continuously integrates both idiothetic and allocentric signals depending on task demand and available cues, and these high-level representations are effectively transmitted to downstream regions.

neuroscience↗

bioRxiv2021

Optogenetic scrambling of hippocampal theta oscillations alters working memory retrieval but not hippocampal spatiotemporal codes

The precise temporal coordination of activity in the brain is thought to be fundamental for memory encoding and retrieval. Pacemaker GABAergic neurons in the medial septum (MS) provide the largest source of innervation to the hippocampus and play a major role in controlling hippocampal theta (~8 Hz) oscillations. While pharmacological inhibition of the MS is known to disrupt memory, the exact role of MS inhibitory neurons and theta frequency rhythms in hippocampal representations and memory is not fully understood. Here, we dissociate the role of theta rhythms in spatiotemporal coding and memory using an all-optical interrogation and recording approach in freely behaving mice. We propose a novel paradigm to dissociate encoding of space, time and distance in freely moving mice and apply complementary optogenetic stimulation paradigms of MS GABAergic neurons to either pace or abolish theta altogether while recording large hippocampal cell assemblies using calcium imaging conjointly. We first show that optogenetic frequency scrambling of MS GABAergic neuron activity abolished theta rhythms and modulated the activity of a subpopulation of CA1 neurons. Such stimulation led to decreased memory retrieval in both a delayed non-match to sample task, a novel place object recognition task, as well as spontaneous cue-guided linear alternation. Strikingly, scrambled stimulations were not associated with disrupted encoding of place, time, distance, or multiplexed information. Our study suggests that theta rhythms play a specific and essential role in supporting working memory retrieval and maintenance while not being necessary for hippocampal spatiotemporal codes.

neuroscience↗

bioRxiv2020

Conjunctive spatial and idiothetic codes are topographically organized in the lateral septum

The hippocampal spatial codes relevance for downstream neuronal populations - particularly its major subcortical output the lateral septum (LS) - is still poorly understood. Here, using calcium imaging combined with unbiased analytical methods, we functionally characterized and compared the spatial tuning of LS GABAergic cells to those of dorsal CA3 and CA1 cells. We identified a significant number of LS cells that are modulated by place, speed, acceleration, and direction, as well as conjunctions of these properties, directly comparable to hippocampal CA1 and CA3 spatially modulated cells. Interestingly, Bayesian decoding of position based on LS spatial cells reflected the animals location as accurately as decoding using the activity of hippocampal pyramidal cells. A portion of LS cells showed stable spatial codes over the course of multiple days, potentially reflecting long-term episodic memory. The distributions of cells exhibiting these properties formed gradients along the anterior-posterior and dorsal-ventral axes of the LS, directly reflecting the topographical organization of hippocampal inputs to the LS. Finally, we show using trans-synaptic tracing that LS neurons receiving CA3 and CA1 excitatory input send projections to the hypothalamus and medial septum, regions that are not targeted directly by principal cells of the dorsal hippocampus. Together, our findings demonstrate that the LS accurately and robustly represents spatial, directional as well as self-motion information and is uniquely positioned to relay this information from the hippocampus to its downstream regions, thus occupying a key position within a distributed spatial memory network.

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↗