Noise correlations in neural ensemble activity limit the accuracy of hippocampal spatial representations.
Ontology highlight
ABSTRACT:
SUBMITTER: Hazon O
PROVIDER: S-EPMC9314334 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.
| S-EPMC5004124 | biostudies-literature
Project description:Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages 'neural implicit representations' that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.
| S-EPMC10511537 | biostudies-literature
Project description:Neuronal activity correlations are key to understanding how populations of neurons collectively encode information. While two-photon calcium imaging has created a unique opportunity to record the activity of large populations of neurons, existing methods for inferring correlations from these data face several challenges. First, the observations of spiking activity produced by two-photon imaging are temporally blurred and noisy. Secondly, even if the spiking data were perfectly recovered via deconvolution, inferring network-level features from binary spiking data is a challenging task due to the non-linear relation of neuronal spiking to endogenous and exogenous inputs. In this work, we propose a methodology to explicitly model and directly estimate signal and noise correlations from two-photon fluorescence observations, without requiring intermediate spike deconvolution. We provide theoretical guarantees on the performance of the proposed estimator and demonstrate its utility through applications to simulated and experimentally recorded data from the mouse auditory cortex.
| S-EPMC8354639 | biostudies-literature
Project description:The receptive field of a neuron describes the regions of a stimulus space where the neuron is consistently active. Sparse spiking outside of the receptive field is often considered to be noise, rather than a reflection of information processing. Whether this characterization is accurate remains unclear. We therefore contrasted the sparse, temporally isolated spiking of hippocampal CA1 place cells to the consistent, temporally adjacent spiking seen within their spatial receptive fields ("place fields"). We found that isolated spikes, which occur during locomotion, are strongly phase coupled to hippocampal theta oscillations and transiently express coherent nonlocal spatial representations. Further, prefrontal cortical activity is coordinated with and can predict the occurrence of future isolated spiking events. Rather than local noise within the hippocampus, sparse, isolated place cell spiking reflects a coordinated cortical-hippocampal process consistent with the generation of nonlocal scenario representations during active navigation.
| S-EPMC8494358 | biostudies-literature
Project description:The visual system can compute summary statistics of several visual elements at a glance. Numerous studies have shown that an ensemble of different visual features can be perceived over 50-200 ms; however, the time point at which the visual system forms an accurate ensemble representation associated with an individual's perception remains unclear. This is mainly because most previous studies have not fully addressed time-resolved neural representations that occur during ensemble perception, particularly lacking quantification of the representational strength of ensembles and their correlation with behavior. Here, we conducted orientation ensemble discrimination tasks and electroencephalogram (EEG) recordings to decode orientation representations over time while human observers discriminated an average of multiple orientations. We modeled EEG signals as a linear sum of hypothetical orientation channel responses and inverted this model to quantify the representational strength of orientation ensemble. Our analysis using this inverted encoding model revealed stronger representations of the average orientation over 400-700 ms. We also correlated the orientation representation estimated from EEG signals with the perceived average orientation reported in the ensemble discrimination task with adjustment methods. We found that the estimated orientation at approximately 600-700 ms significantly correlated with the individual differences in perceived average orientation. These results suggest that although ensembles can be quickly and roughly computed, the visual system may gradually compute an orientation ensemble over several hundred milliseconds to achieve a more accurate ensemble representation.
| S-EPMC11325722 | biostudies-literature
Project description:Recent evidence suggests that mesoscopic neural oscillations measured via intracranial electroencephalography exhibit spatial representations, which were previously only observed at the micro- and macroscopic level of brain organization. Specifically, theta (and gamma) oscillations correlate with movement, speed, distance, specific locations, and goal proximity to boundaries. In entorhinal cortex (EC), they exhibit hexadirectional modulation, which is putatively linked to grid cell activity. Understanding this mesoscopic neural code is crucial because information represented by oscillatory power and phase may complement the information content at other levels of brain organization. Mesoscopic neural oscillations help bridge the gap between single-neuron and macroscopic brain signals of spatial navigation and may provide a mechanistic basis for novel biomarkers and therapeutic targets to treat diseases causing spatial disorientation.
| S-EPMC6601347 | biostudies-literature
Project description:Lysergic acid diethylamide (LSD) produces hallucinations, which are perceptions uncoupled from the external environment. How LSD alters neuronal activities in vivo that underlie abnormal perceptions is unknown. Here, we show that when rats run along a familiar track, hippocampal place cells under LSD reduce their firing rates, their directionality, and their interaction with visual cortical neurons. However, both hippocampal and visual cortical neurons temporarily increase firing rates during head-twitching, a behavioral signature of a hallucination-like state in rodents. When rats are immobile on the track, LSD enhances cortical firing synchrony in a state similar to the wakefulness-to-sleep transition, during which the hippocampal-cortical interaction remains dampened while hippocampal awake reactivation is maintained. Our results suggest that LSD suppresses hippocampal-cortical interactions during active behavior and during immobility, leading to internal hippocampal representations that are degraded and isolated from external sensory input. These effects may contribute to LSD-produced abnormal perceptions.
| S-EPMC9798728 | biostudies-literature
Project description:All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30?minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24?h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a 'hard-wired' system.
| S-EPMC5674977 | biostudies-literature
Project description:We characterized the population-level neural coding of ensemble representations in visual working memory from human electroencephalography. Ensemble representations provide a unique opportunity to investigate structured representations of working memory because the visual system encodes high-order summary statistics as well as noisy sensory inputs in a hierarchical manner. Here, we consistently observe stable coding of simple features as well as the ensemble mean in frontocentral electrodes, which even correlated with behavioral indices of the ensemble across individuals. In occipitoparietal electrodes, however, we find that remembered features are dynamically coded over time, whereas neural coding of the ensemble mean is absent in the old/new judgment task. In contrast, both dynamic and stable coding are found in the continuous estimation task. Our findings suggest that the prefrontal cortex holds behaviorally relevant abstract representations while visual representations in posterior and visual areas are modulated by the task demands.
| S-EPMC6906315 | biostudies-literature
Project description:Across the domains of spatial navigation and episodic memory, the hippocampus is thought to play a critical role in disambiguating (pattern separating) representations of overlapping events. However, it is not fully understood how and why hippocampal patterns become separated. Here, we test the idea that event overlap triggers a "repulsion" among hippocampal representations that develops over the course of learning. Using a naturalistic route-learning paradigm and spatiotemporal pattern analysis of human fMRI data, we found that hippocampal representations of overlapping routes gradually diverged with learning to the point that they became less similar than representations of non-overlapping events. In other words, the hippocampus not only disambiguated overlapping events but formed representations that "reversed" the objective similarity among routes. This finding, which was selective to the hippocampus, is not predicted by standard theoretical accounts of pattern separation. Critically, because the overlapping route stimuli that we used ultimately diverged (so that each route contained overlapping and non-overlapping segments), we were able to test whether the reversal effect was selective to the overlapping segments. Indeed, once overlapping routes diverged (eliminating spatial and visual similarity), hippocampal representations paradoxically became relatively more similar. Finally, using a novel analysis approach, we show that the degree to which individual hippocampal voxels were initially shared across route representations was predictive of the magnitude of learning-related separation. Collectively, these findings indicate that event overlap triggers a repulsion of hippocampal representations-a finding that provides critical mechanistic insight into how and why hippocampal representations become separated.
| S-EPMC5576038 | biostudies-literature