Biology subjects

Ewell, L. A.

Publications and source records attributed to Ewell, L. A..

10 recordsLinked to original sources

bioRxiv2026

Reward-tethered place cells support flexible magnitude coding and remapping in the hippocampus

Learning to navigate a changing environment requires the ability to detect when a reward no longer matches a previous expectation. While the hippocampus is essential for adapting behavior strategies when reward expectations are violated and is known to integrate goal-related information into spatial maps, the precise dynamics by which these maps update during an unexpected reduction in reward magnitude is not well understood. Using longitudinal calcium imaging of neuronal activity in behaving mice, we found that hippocampal CA1 population activity encodes reward magnitude through elevated event rates at high value locations. Within this population, we discovered a specialized group of reward tethered place cells that bind spatial context to reward magnitude. These reward tethered place cells exhibit spatial fields across the environment while simultaneously exhibiting activity anchored to high-value reward locations. Upon reward reduction, CA1 population activity equalizes and these neurons undergo a selective and rapid remapping that precedes behavioral adjustment to the reward downshift. The broader spatial map remains intact, indicating that this change allows the animal to update the value of a goal while preserving a stable representation of its surroundings. This selective reorganization of hippocampal firing patterns could support adaptive decision making by updating internal models of the world when expectations are violated. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/725501v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@1725be8org.highwire.dtl.DTLVardef@eff7c1org.highwire.dtl.DTLVardef@72d4b3org.highwire.dtl.DTLVardef@ea3e41_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Masala N., Donahue M., etal. C_FIG

neuroscience↗

bioRxiv2025

UNIQUE DEFICITS IN PLACE CODING ACROSS SUBFIELDS OF THE HIPPOCAMPUS IN A MOUSE MODEL OF TEMPORAL LOBE EPILEPSY.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSMemory problems are comorbid with Temporal Lobe Epilepsy (TLE). Animal models of TLE reveal impairments in spatial firing fields of hippocampal place cells, providing a potential neural substrate for memory problems. Each subfield of the hippocampus carries out unique aspects of spatial memory, yet little is known about how individual subfields are perturbed. Here, we investigated the spatial coding properties of the three major subfields of the hippocampus. MethodsSingle unit recordings were made from CA1, CA3 and the dentate gyrus (DG) of mice (N = 10, 6M/4F) induced with epilepsy using the supra-hippocampal kainate model and in control mice injected with saline (N = 6, 3M/3F). Place cell activity was measured while mice foraged in highly familiar environments to assess basic place cell properties and in novel environments to assess remapping. ResultsA lower percentage of cells were classified as place cells in CA1 of epileptic mice, whereas percentages were similar in CA3 and DG compared to control. Place fields of CA1 were less coherent, place fields of CA3 were less stable, and place fields in DG has smaller differences between in-field and out of field firing. All regions constructed new distinct maps within the first session of exposure to a novel environment, however new maps in CA3 trended toward instability. SignificanceThese results point to specific deficits within subfields of the hippocampus, which may indicate that there are different cellular and network mechanisms at play. Such heterogeneity would be predicted to contribute differently to memory deficits. Key PointsO_LICA1 exhibits place map quality problems; CA1 has fewer place cells and those remaining have reduced spatial coherence C_LIO_LICA3 exhibits place map stability problems; CA3 has lower spatial correlation within sessions and trends toward not forming stable new maps in a novel environment. C_LIO_LIDG exhibits reduced signal to noise; DG has smaller differences between in-field and out of field firing rates. C_LI

neuroscience↗

bioRxiv2025

Early impairment in dentate pattern separation in a rodent model of temporal lobe epilepsy.

The dentate gyrus of the hippocampal formation utilizes sparse neural coding to support memory formation. In temporal lobe epilepsy (TLE), the dentate undergoes extensive restructuring, including loss of local inhibition. It is unknown how circuit changes impact dentate network computations in awake behaving animals. Here we perform high-density tetrode recordings from the dentate in male rats treated with systemic kainic acid (KA) to induce TLE. In KA-treated rats, dentate spatial representations were less precise at the single neuron level, however the stability of the spatial code in representing repeated visits to the same location was retained at the population level. Despite spared spatial representation, the network computations for pattern separation were disrupted. The reorganized dentate network in KA-treated rats generated less distinct population activity patterns for different environments, specifically as a result of impaired rate-remapping. Changes in network computations paralleled impairments in a dentate-dependent memory task, potentially providing a link between altered pattern separation and behavioral discrimination. These deficits were present in animals with confirmed dentate circuit reorganization that had not yet developed spontaneous seizures - suggesting that the dentate network changes underlying altered rate-remapping and impaired dentate-dependent memory occur early in epileptogenesis.

neuroscience↗

bioRxiv2025

Dentate spikes comprise a continuum of relative input strength from the lateral and medial entorhinal cortex.

Hippocampal dentate spikes (DS) are synchronous population events generated in the dentate gyrus thought to support memory processing. DS are classified into type 1 (DS1) and type 2 (DS2) based on current sinks in the outer or middle molecular layer, where the axons of the lateral or medial entorhinal cortex terminate, respectively. This widely used classification method constrains DS into a bimodal distribution without properly testing whether such constraint is appropriate. Here we utilized silicon probe recordings with high spatial resolution spanning all layers of the dentate gyrus and discovered that the contribution of LEC/MEC inputs to DS follows a continuous distribution. We introduce a third type (DS3) that captures a previously unreported component of the distribution: simultaneous current sinks in the outer/middle molecular layer, suggestive of synchronized activation from the LEC/MEC which could facilitate binding. We verify DS3 in several data sets from independent laboratories and characterize their brain state dependence and neural recruitment.

neuroscience↗

bioRxiv2024

Interictal spikes during spatial working memory carry helpful or distracting representations of space and have opposing impacts on performance.

In temporal lobe epilepsy, interictal spikes (IS) - hypersynchronous bursts of network activity - occur at high rates in between seizures. We sought to understand the influence of IS on working memory by recording hippocampal local field potentials from epileptic mice while they performed a delayed alternation task. We found that IS disrupted performance when they were spatially non-restricted and occurred during running. In contrast, when IS were clustered at reward locations, animals performed well. A machine learning decoding approach revealed that IS at reward sites were larger than IS elsewhere on the maze, and could be classified as occurring at specific reward locations - suggesting they carry informative content for the memory task. Finally, a spiking model revealed that spatially clustered IS preserved hippocampal replay, while spatially dispersed IS disrupted replay by causing over-generalization. Together, these results show that IS can have opposing outcomes on memory.

neuroscience↗

bioRxiv2022

Low-cost platform for multi-animal chronic local field potential video monitoring with graphical user interface (GUI) for seizure detection and behavioral scoring

Experiments employing chronic monitoring of neurophysiological signals and video are commonly used in studies of epilepsy to characterize behavioral correlates of seizures. Our objective was to design a low-cost platform that enables chronic monitoring of several animals simultaneously, synchronizes bilateral local field potential and video streams in real-time, and parses recorded data into manageable file sizes. We present a hardware solution leveraging Intan and Open Ephys acquisition systems and a software solution implemented in Bonsai. The platform was tested in 48-hour continuous recordings simultaneously from multiple mice (male and female) with chronic epilepsy. To enable seizure detection and scoring, we developed a graphical user interface (GUI) that reads the data produced by our workflow and allows a user with no coding expertise to analyze events. Our Bonsai workflow was designed to maximize flexibility for a wide variety of experimental applications, and our use of the Open Ephys acquisition board would allow for scaling recordings up to 128 channels per animal. SIGNIFICANCE STATEMENTWe present a low-cost hardware and software solution intended for multi-animal chronic seizure monitoring, that prioritizes experimental freedom, and requires no coding expertise of the user. We provide details for using an Intan adapter board to enable user freedom regarding the type of electrodes used. Video and local field potential data streams are synchronized and parsed in Bonsai - an open-source visual programming language that has pre-written libraries that allow our workflow to be adapted to other data types or to integrate with open-source toolboxes. Finally, for those intending to use our platform for seizure monitoring, we provide an accessible GUI to aid in seizure detection and behavioral scoring.

neuroscience↗

bioRxiv2020

Dentate gyrus population activity during immobility drives formation of precise memories

The hippocampal dentate gyrus is an important relay conveying sensory information from the entorhinal cortex to the hippocampus proper. During exploration, the dentate gyrus has been proposed to act as a pattern separator. However, the dentate gyrus also shows structured activity during immobility and sleep. The properties of these activity patterns at cellular resolution, and their role in hippocampal-dependent memory processes have remained unclear. Using dual-color in-vivo two-photon Ca2+ imaging, we show that in immobile mice dentate granule cells generate sparse, synchronized activity patterns associated with entorhinal cortex activation. These population events are structured and modified by changes in the environment; and they incorporate place- and speed cells. Importantly, they recapitulate population patterns evoked during self-motion. Using optogenetic inhibition during immobility, we show that granule cell activity during immobility is required to form dentate gyrus-dependent spatial memories. These data suggest that memory formation is supported by dentate gyrus replay of population codes of the current environment.

neuroscience↗

bioRxiv2019

DeepLabStream: Closing the loop using deep learning-based markerless, real-time posture detec-tion

In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Current technologies enable offline pose estimation with high spatio-temporal resolution, however to understand complex behaviors, it is necessary to correlate the behavior with neuronal activity in real-time. Here we present DeepLabStream, a highly versatile, closed-loop solution for freely moving mice that can autonomously conduct behavioral experiments ranging from behavior-based learning tasks to posture-dependent optogenetic stimulation. DeepLabStream has a temporal resolution in the millisecond range, can operate with multiple devices and can be easily tailored to a wide range of species and experimental designs. We employ DeepLabStream to autonomously run a second-order olfactory conditioning task for freely moving mice and to deliver optogenetic stimuli based on mouse head-direction.

animal behavior and cognition↗

bioRxiv2018

Temporal pattern separation in hippocampal neurons through multiplexed neural codes

Pattern separation is a central concept in current theories of episodic memory: this computation is thought to support our ability to avoid confusion between similar memories by transforming similar cortical input patterns of neural activity into dissimilar output patterns before their long-term storage in the hippocampus. Because there are many ways one can define patterns of neuronal activity and the similarity between them, pattern separation could in theory be achieved through multiple coding strategies. Using our recently developed assay that evaluates pattern separation in isolated tissue by controlling and recording the input and output spike trains of single hippocampal neurons, we explored neural codes through which pattern separation is performed by systematic testing of different similarity metrics and various time resolutions. We discovered that granule cells, the projection neurons of the dentate gyrus, can exhibit both pattern separation and its opposite computation, pattern convergence, depending on the neural code considered and the statistical structure of the input patterns. Pattern separation is favored when inputs are highly similar, and is achieved through spike time reorganization at short time scales (< 100 ms) as well as through variations in firing rate and burstiness at longer time scales. These multiplexed forms of pattern separation are network phenomena, notably controlled by GABAergic inhibition, that involve many celltypes with input-output transformations that participate in pattern separation to different extent and with complementary neural codes: a rate code for dentate fast-spiking interneurons, a burstiness code for hilar mossy cells and a synchrony code at long time scales for CA3 pyramidal cells. Therefore, the isolated hippocampal circuit itself is capable of performing temporal pattern separation using multiplexed coding strategies that might be essential to optimally disambiguate multimodal mnemonic representations.\n\nAuthor SummaryPattern separation (the process of disambiguating incoming patterns of neuronal activity) is a central concept in all current theories of episodic memory, as it is hypothesized to support our ability to avoid confusion between similar memories. For the last thirty years, pattern separation has been attributed to the dentate gyrus of the hippocampus, but this has been hard to test experimentally. Moreover, because it is unclear how to define activity patterns in the brain, such a computation could be achieved in many different ways. Here, we demonstrate that pattern separation is performed by hippocampal networks (dentate gyrus and CA3) through a variety of neural codes. By systematically testing different definitions of what it means for spike trains to be similar (using a range of time scales and various standard and innovative metrics that assume different views of the neural code), we assessed how the input-output transformation of multiple hippocampal celltypes relate to pattern separation and found that different celltypes favor complementary coding strategies. This might help storing rich but concise and unambiguous representations of complex events. Finally, we provide the first experimental evidence of the importance of inhibitory signals in mediating pattern separation, and identify through which coding strategies.

neuroscience↗

bioRxiv2017

Pattern separation of spiketrains by individual granule cells of the dentate gyrus

Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Theoretical work suggests that the dentate gyrus (DG) performs this role for memory processing but a direct demonstration is lacking. One limitation is the difficulty to measure DG inputs and outputs simultaneously. To rigorously assess pattern separation by DG circuitry, we used mouse brain slices to stimulate DG afferents and simultaneously record DG granule cells (GCs) and interneurons. Output spiketrains of GCs are more dissimilar than their input spiketrains, demonstrating for the first time temporal pattern separation at the level of single neurons in the DG. Pattern separation is larger in GCs than in fast-spiking interneurons and hilar mossy cells, and is amplified in CA3 pyramidal cells. Analysis of the neural noise and computational modelling suggest that this form of pattern separation is not explained by simple randomness and arises from specific presynaptic dynamics. Overall, by reframing the concept of pattern separation in dynamic terms and by connecting it to the physiology of different types of neurons, our study offers a new window of understanding in how hippocampal networks might support episodic memory.

neuroscience↗