Biology subjects

Bazhenov, M.

Publications and source records attributed to Bazhenov, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Firing Without Wiring: How REM-like Sleep Reactivation Rebalances Excitation and Inhibition to Orthogonalize Memories

The role of Rapid Eye Movement (REM) sleep in memory consolidation remains poorly understood. Several influential ideas propose that REM sleep serves to eliminate crosstalk between memories, thereby orthogonalizing representations of similar ones; however, mechanistic confirmation remains sparse. Here we used recurrent neural networks trained with Equilibrium Propagation (EP), a biologically plausible training algorithm, to investigate the role of REM-like sleep in memory consolidation. We found that REM-like sleep reduces memory interference by decorrelating task-specific neuronal populations, isolating individual memory traces. During sleep, reactivation was dominated by the population encoding the most recent memory. Net excitatory input onto this population decreased, most strongly that from older-memory populations, which allowed inputs of older memories to drive their own neurons again. Spreading the same total synaptic change evenly across all target populations abolished this recovery. We call this mechanism reactivation-targeted pruning: plasticity is confined to one population, whose neurons lose inputs that are silent when they fire. The mechanism held under Dale's law, and depression of excitatory synapses alone, without any inhibitory plasticity, was sufficient. These findings suggest mechanistic principles underlying REM sleep's role in memory consolidation and highlight the potential for integrating human-like learning behaviors into artificial neural networks.

neuroscience↗

bioRxiv2026

Sleep to forget: active control of consolidation and forgetting by slow-wave sleep dynamics

Sleep supports both the consolidation of new memories and the forgetting of others, but how the cortex flexibly controls these outcomes remains poorly understood. Recent work has shown that two types of Up states may play distinct, competing roles during slow-wave sleep (SWS): slow waves actively consolidate memory traces, whereas delta waves promote their weakening. Here we use a biophysical thalamocortical network model equipped with spike-timing-dependent plasticity to investigate the synaptic mechanisms underlying this dissociation. By manipulating the intrinsic Ca2+ dynamics of cortical pyramidal cells, we generate both slow and delta wave Up states within a single network. Using a sequence-learning task paradigm we recapitulate the optogenetic dissociation: removing plasticity during slow waves degrades the memory, while removing it during delta waves enhances consolidation. Mechanistically, the model reveals the longer slow wave Up state affords a spontaneous reactivation phase, occurring after the interfering input, during which the trained memory is selectively reactivated and protected, a phase the truncated delta Up state cannot support. We further find that delta waves sparsen the synaptic representation more than slow waves and predict that the balance between consolidation and forgetting can be flexibly tuned by the ratio of slow to delta waves during SWS.

neuroscience↗

bioRxiv2026

Resonance-driven enhancement of sleep spindles using thalamic temporal interference stimulation

Sleep spindles are hallmarks of non-rapid eye movement sleep and support memory consolidation yet remain difficult to modulate non-invasively. Combining computational modeling and human sleep recordings, we show that thalamus-targeted temporal interference stimulation (TIS) with a 5Hz envelope increases spindle density via subthreshold resonance in thalamocortical relay neurons. Our results demonstrate a mechanistic framework for the rational design of interventions to selectively augment sleep spindles.

neuroscience↗

bioRxiv2026

Data-Constrained Recurrent Network Neural Model Uncovers the Circuit Mechanism of Olfactory OFF Responses

Sensory neural circuits must encode both the presence and termination of a stimulus. Following odor offset, projection neurons (PNs) in the insect antennal lobe (AL) exhibit transient increases in firing rate, termed OFF responses, yet the circuit mechanisms that generate them in recurrent excitatory-inhibitory networks remain poorly understood. Here, we constructed a biologically-constrained firing rate-based recurrent neural network (RNN) model of the locust AL and trained it on electrophysiological recordings from 110 in vivo PNs to reconstruct their odor-evoked temporal dynamics across five odorants. The trained model faithfully reproduced the firing rates of constrained neurons, while unconstrained PNs and LNs developed biologically plausible temporal response patterns and response-type diversity. Using targeted input and connectivity perturbations, we found that OFF responses arise through two mechanistically distinct pathways. A feedforward pathway transmits offset-type olfactory receptor neuron (ORN) input directly to downstream PNs, while a recurrent pathway generates post-stimulus excitation independently of offset input. Selective perturbation of individual recurrent connections identified LN-LN mutual inhibition as the dominant recurrent pathway, and decomposition of excitatory and inhibitory inputs revealed that it produces net excitation through the transient release of inhibition rather than through increased drive. The two pathways recruit largely non-overlapping PN populations, indicating that OFF response identity is an emergent property of the network state rather than a cell-intrinsic feature. These findings provide a circuit-level account of OFF response generation and demonstrate how data-constrained RNNs can dissect circuit mechanisms directly from in vivo recordings.

neuroscience↗

bioRxiv2026

Age-related sleep changes in the human brain: insights from a large-scale thalamocortical model

AbstractSleep-dependent memory consolidation relies on slow oscillations (SOs) that coordinate thalamocortical-hippocampal dynamics during slow-wave sleep (SWS). Aging disrupts SO properties, reducing SO amplitude, density, and slope, yet the circuit-level mechanisms linking structural brain changes to these disruptions remain poorly understood. Here we present a multi-scale, whole-brain thalamocortical network model incorporating biologically grounded human connectivity derived from diffusion MRI tractography, comprising over 10,000 cortical columns per hemisphere with spiking pyramidal and inhibitory neurons and an anatomically differentiated thalamic network. Simulating progressive synaptic loss, we find that selective degradation of recurrent excitatory connectivity, but not excitatory-inhibitory projections, reproduces empirically observed age-related SO changes. Increased SO duration was driven primarily by prolonged Down states, while Up state duration and spike density were reduced, suggesting a possible mechanism for impaired memory consolidation. These results suggest that aging selectively disrupts the temporal structure of SWS critical for interference-free memory consolidation, providing mechanistic insight into cognitive decline in the aging brain. Supported by: NIH (grants 1R01MH125557, 1RF1NS132913, 1R01AG099626 to MB)

neuroscience↗

bioRxiv2025

Closed-loop acoustic stimulation modified by cathodal tDCS is beneficial for retention

We investigated how combining transcranial direct current stimulation (tDCS) with closed-loop acoustic stimulation (CLAS) during slow-wave sleep (SWS) affects memory consolidation and sleep-related neural oscillations. Cathodal tDCS was used to slightly reduce cortical excitability, thereby simulating a shifted brain state during CLAS and allowing us to compare the effects of standard CLAS to CLAS delivered under altered cortical conditions (CmodCLAS). Twenty-three participants (mean age 21.3 {+/-} 2.6 years) completed two experimental nights: one with CLAS alone and one with CLAS combined with cathodal tDCS (CmodCLAS). Overnight retention on declarative memory tasks and morning learning performance were assessed. Memory outcomes revealed that CmodCLAS, but not CLAS alone, significantly improved overnight retention on the figural paired-associate task. In the EEG, CmodCLAS shifted the time-locked response to acoustic stimulation toward more negative potential values at the frontal region. CmodCLAS also prolonged slow oscillation (SO) duration at frontal sites while shortening SO duration at occipital sites, an effect not observed during standard CLAS. These findings demonstrate that the baseline level of cortical excitability during sleep modulates both the cognitive and electrophysiological effects of CLAS. They highlight the importance of brain state for non-invasive CLAS during sleep and suggest that CmodCLAS may serve as a useful approach for enhancing prefrontal cortical function during SWS. Significance statementNon-invasive brain stimulation (NIBS) is a promising approach to modulate brain activity and function. Unfortunately, NIBS studies are challenged by heterogenous findings. In addition to protocol differences, variability may depend strongly on the brains baseline activity (or brain state). Here, we used transcranial direct current stimulation (tDCS) to simulate a different brain state while applying closed-loop acoustic stimulation (CLAS) during slow-wave sleep (SWS). Sleep has been shown to enhance overnight memory retention. Results demonstrated significantly improved retention performance for cathodal tDCS modulated CLAS (CmodCLAS) as compared to CLAS alone. Furthermore, electrophysiological responses time-locked to the acoustic stimulus and spontaneous electrophysiological rhythms of SWS suggest that hippocampo-cortical function was specifically enhanced by CmodCLAS. Thus, underscoring the potential therapeutic relevance of CmodCLAS.

neuroscience↗

bioRxiv2025

Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learning

Sleep replay - the reactivation of memory traces during slow-wave sleep - is widely held to stabilize memories and reduce interference, yet exactly how replay reorganizes synaptic-weight space to preserve existing memories while incorporating new ones remains unclear. Here, we use a biophysically realistic network model to probe the synaptic-weight dynamics underlying this process. We find that replay drives synaptic weights toward stable configurations - synaptic attractors - that jointly support both old and new memories. Hippocampus-driven interactions between sharp-wave ripples and cortical slow waves guide this reorganization, allowing recently acquired memories to be incorporated without degrading prior ones. These results reveal a mechanistic and geometric framework for memory consolidation: sleep does not passively protect memories, but actively sculpts synaptic-weight space into attractor configurations from which forgetting requires escaping a stability basin. SIGNIFICANCE STATEMENTStoring, processing, and retrieving information underpins intelligent behavior. Sleep extracts invariant features from prior experience, promoting the emergence of explicit knowledge and insight. Yet despite abundant empirical findings, our understanding of how sleep reshapes memory representations at the level of synaptic organization remains limited. Here we present a novel framework that describes how memories are encoded in synaptic-weight space and how sleep dynamics reorganize synaptic landscape. These results advance our understanding of how the brain solves core problems of lifelong learning.

neuroscience↗

bioRxiv2025

Intrinsic ion dynamics underlies the temporal nature of resting-state functional connectivity

The neural mechanisms underlying the emergence of functional connectivity in resting-state fMRI remain poorly understood. Recent studies suggest that resting-state activity consists of brief periods of strong co-fluctuations among brain regions, which reflect overall functional connectivity. Others report a continuum in co-fluctuations over time, with varying degree of correlation to functional connectivity. These findings raise the critical question: what neural processes underlie the temporal structure of resting-state activity? To address this, we used a biophysically realistic whole-brain computational model in which resting-state activity emerged from temporal variations in the ion concentrations of potassium (K+) and sodium (Na+), intracellular chloride (Cl-), and the activity of the Na+/K+ ATPase. The model reproduced transient periods of high co-fluctuations, and the functional connectivity at different co-fluctuation levels correlated to varying degrees with the connectivity measured over the entire simulation, in line with experimental observations. The periods of high co-fluctuations were aligned with large changes in extracellular ion concentrations. Furthermore, critical parameters governing ion dynamics strongly affected both the timing of these transient events and the spatial structure of the resulting functional connectivity. The balance of excitatory and inhibitory activity further modulated their frequency and amplitude. Together, these results suggest that intrinsic fluctuations in ion dynamics could serve as a plausible neural mechanism for the temporal organization of co-fluctuations and resting-state functional connectivity.

neuroscience↗

bioRxiv2025

Detection of multiple per- and polyfluoroalkyl substances (PFAS) using a biological brain-based gas sensor

Per- and polyfluoroalkyl substances (PFAS) are man-made compounds that bioaccumulate in environments. Current PFAS detection technologies encounter difficulty in detecting trace concentrations and require complex data processing, limiting their on-site applicability. By leveraging biological chemical sensing systems (insect olfaction) we can detect broad ranges of PFAS. Insects advanced combinatorial coding mechanism at the level of olfactory sensory neurons enables highly sensitive and specific odor detection. Here, we harness the locust olfactory system to differentiate several PFAS. In-vivo extracellular neural recordings displayed unique odor-evoked responses for multiple PFAS at environmental concentrations. Using population neuronal response, we classified multiple PFAS with an average 87% accuracy. Machine learning algorithms incorporated separate training and testing datasets, reaching a 61% accuracy. Overall, our study demonstrates the first biological olfaction based broad PFAS detection system. Structured AbstractO_ST_ABSIntroductionC_ST_ABSPer- and polyfluoroalkyl substances (PFAS) pose a significant environmental threat due to their widespread presence in consumer waste and resistance to degradation. These "forever chemicals" persist in various ecosystems and exhibit bio accumulative behavior. Increased human exposure to PFAS has been linked to numerous health issues. Despite their growing relevance, current detection methods often lack the sensitivity and efficiency needed for comprehensive environmental monitoring and are unable to simultaneously detect multiple PFAS at environmental concentrations. RationaleThe locust (Schistocerca americana) possesses a highly developed olfactory system that has been extensively studied and is accessible for physiological recordings across multiple brain regions. Utilizing olfactory receptor neurons and combinatorial coding strategies, locusts can generate distinct neural fingerprints for trillions of odorants across a wide range of concentrations. Through spatiotemporal neural coding at the antennal lobe (AL) neurons, they can detect chemicals at parts per trillion levels, functioning as a biological chemical sensor with exceptional sensitivity and broad specificity. In this study, we aimed to directly harness the locusts olfactory neural circuitry to develop a next-generation, brain-based cyborg sensor capable of (1) simultaneously identifying multiple PFAS and (2) determining their concentration ranges, addressing key limitations of existing detection technologies with the integration of machine learning algorithms. ResultsIn-vivo extracellular neural recordings from the locust AL revealed that individual neurons exhibited distinct response profiles to different PFAS and their varying concentrations, indicating that neuronal activity if modulated by both chemical identity and concentration. By incorporating both spatial and temporal dimensions of neural activity, the recorded neuronal populations produced unique and reproducible response patterns corresponding to specific PFAS and concentration levels. Using this approach, the cyborg sensor demonstrated an overall detection accuracy of 87% across a panel of seven PFAS, with high sensitivity and specificity for both individual analytes and broader chemical groupings. Notably, classification of PFAS concentration ranges down to parts per trillion achieved 84% accuracy, with PFOS concentrations reaching 100% detection rate. A machine learning algorithm trained on high concentration data and tested on low concentration data achieved a 61% accuracy. These results underscore the potential of biologically integrated cyborg sensors for real-time, high-resolution environmental monitoring of several PFAS. ConclusionThis study demonstrates, for the first time, the locust olfactory neural network harnessed as a highly effective cyborg sensor for detecting and classifying various PFAS across concentration ranges. This sensor can accurately distinguish between multiple PFAS and controls with high sensitivity and specificity. Through combinatorial coding and spatiotemporal neural dynamics, the locust neural computation encodes distinct activity patterns in response to PFAS and their concentrations. These neural signatures serve as unique "fingerprints" for individual PFAS and concentrations, enabling precise identification. In-vivo electrophysiological recordings revealed clear, compound-specific differences in neural activity, with high classification accuracy. Real-time and machine learning analysis further addressed key limitations of conventional PFAS sensors. This novel approach represents a significant step toward the development of compact, real-time, brain-based PFAS detection sensor capable of discriminating multiple compounds and concentrations simultaneously.

bioengineering↗

bioRxiv2025

Interleaved Replay of Novel and Familiar Memory Traces During Slow-Wave Sleep Prevents Catastrophic Forgetting

Humans and animals can learn continuously, acquiring new knowledge while integrating it into a lifelong memory pool. In contrast, artificial neural networks (ANNs) suffer from catastrophic forgetting, where new training disrupts existing memories. This issue can be alleviated in ANNs by interleaving training on new tasks with past data; however, whether the brain uses a similar strategy is unknown. In this work, we show that slow-wave sleep interleaves replay of familiar and novel (i.e. hippocampal-dependent) memory traces within individual slow waves, allowing new memories to integrate into the existing cortical pool without interference. This study presents a novel theory for how memory traces acquired across an animals life are organized within the cortical-hippocampal system to support continual learning and suggests novel principles for a broad range of continual learning AI.

neuroscience↗

bioRxiv2024

Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans

Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits which contribute to the associated evoked potentials (EPs). Here we find that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the subjects behavioral state. We optogenetically dissect the underlying circuit in mice, demonstrating that the late component of these EPs is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with the bursting frequency and synchronicity of thalamic neurons, modulated by the subjects behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that the cortical stimulation engages cortico-thalamo-cortical circuits highly preserved across different species and stimulation modalities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/578243v2_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@cae203org.highwire.dtl.DTLVardef@1bc5223org.highwire.dtl.DTLVardef@b48e43org.highwire.dtl.DTLVardef@1e7fe34_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Plasticity in inhibitory networks improves pattern separation in early olfactory processing

Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day - and potentially many times within a foragers lifetime - add to the complexity. The honeybee olfactory system, containing fewer than 1,000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational network model, tuned by in vivo electrophysiological data, and live imaging of the honeybees AL, we explored the neural mechanisms and functions of plasticity in the early olfactory system. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors. Significance StatementBy combining biophysical modeling, machine learning, electrophysiology and analysis of calcium imaging data, we demonstrate that associative and nonassociative plasticity in the honeybee antennal lobe (AL) the first relay of the insect olfactory system - work together to enhance the contrast between rewarded and unrewarded odors. Training the ALs inhibitory network within specific odor environments enables the suppression of neural responses to common odor components while amplifying responses to distinctive ones. This study sheds light on the olfactory systems ability to adapt and efficiently learn new odor-reward associations across varying environments, and it proposes innovative, energy-efficient principles applicable to artificial intelligence.

neuroscience↗

bioRxiv2023

Emergent effects of synaptic connectivity on cortical sleep slow wave amplitude, density and propagation in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease. Author SummarySlow Wave Sleep (SWS) is a primary brain state displayed during Non-Rapid Eye Movement (NREM) sleep. While previously thought of as homogenous waves of activity that sweep across the entire brain, modern research has suggested a more nuanced pattern of activity that can vary between local and global slow wave activity. However, understanding how these states emerge from small scale neuronal dynamics and network connectivity remains unclear. We developed a biophysically realistic model of the human brain capable of generating SWS-like behavior, and investigated the role of connectivity in the spatio-temporal dynamics of these slow waves. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network behavior - specifically, models with relatively weaker long-range connectivity resulted in mixed states of global and local slow waves. These results were compared to human data, and we found that models producing mixed states provided the best match to the network behavior and functional connectivity of human subject data. These findings shed light on how the spatio-temporal properties of SWS emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

neuroscience↗

bioRxiv2023

Network effects of traumatic brain injury: from infra slow to high frequency oscillations

Traumatic brain injury (TBI) can have a multitude of effects on neural functioning. In extreme cases, TBI can lead to seizures both immediately following the injury as well as persistent epilepsy over years to a lifetime. However, mechanisms of neural dysfunctioning after TBI remain poorly understood. To address these questions, we analyzed experimental data and developed a biophysical network model implementing effects of ion concentration dynamics and homeostatic synaptic plasticity to test effects of TBI on the brain network dynamics. We focus on three primary phenomena that have been reported in vivo after TBI: an increase in infra slow oscillations (<0.1 Hz), increase in delta (0.1 - 4 Hz) power, and the emergence of high frequency oscillations (HFOs) in the gamma range (30 - 100 Hz). We show that the infra slow oscillations can be directly attributed to extracellular potassium fluctuations, while the existence and characterization of HFOs is related to the increase in strength of synaptic weights from homeostatic synaptic scaling. The experimentally found transient increase in delta power can be attributed to the inter-HFO timings. We then show that buildup of high frequency oscillations in the injured region can lead to seizure-like events that span all neurons in the network; additional seizures can then be initiated in previously healthy regions. This study brings greater understanding of network effects of TBI, and how they can give rise to epileptic activity. This lays the foundation to begin investigating how injured networks can be healed and seizures prevented. Significance StatementThis project delineates and attempts to explain abnormalities seen in human brain following traumatic brain injury (TBI). TBI can lead to the development of seizures, which may last a lifetime and often become resistant to pharmaceutical treatments. The study identified key mechanisms responsible for occurrence of three characteristic changes in spatio-temporal network dynamics following TBI. This model provides predictions that can serve as a testing ground for potential therapeutic approaches.

neuroscience↗

bioRxiv2023

Melanin found in wheat spike husks

Melanin is the dark polymer pigment found in all kingdoms of life. Plant melanin, formed through the oxidation and polymerization of phenolic compounds, does not contain nitrogen, however it possesses similar properties with melanin of animal, fungal or bacterial origin. Melanin in plants is usually found in seed coats or fruit pericarp and is ascribed mechanical barrier or some other protective functions. Wild and formerly locally cultivated wheat species, like Persian wheat (Triticum carthlicum Nevski) frequently exhibit black color of spike husks and awns. The pigment causing it and the biological purpose of this coloration was not clarified before. In this paper using standard chemical extraction procedures for anthocyanins and melanin, ultraviolet-visible-near-infrared spectroscopy and hyperspectral imaging, we prove that the black color of Persian wheat spikes is caused by melanin but not anthocyanins. Also, we show that the dark pigment in husks and awns is located in epiderma and subepidermal sclerenchyma cells, that implies melanin potentially to have mechanical-enhancing and protection function. Other possible functions of melanin in cereals are discussed.

plant biology↗

bioRxiv2022

Cholinergic modulation supports DMN suppression during resting state

Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.

neuroscience↗

bioRxiv2022

Single acoustic closed loop stimulation in mice to modulate hippocampo-thalamo-cortical activity and performance

Neural brain rhythms of sleep reflect neuronal activity underlying sleep-associated memory consolidation. The modulation of brain rhythms, for instance the sleep slow oscillation (SO) is used both to investigate neurophysiological mechanisms as well as to measure the impact on presumed functional correlates. In humans, auditory closed-loop stimulation targeted to the SO Up-state successfully enhanced the slow oscillation rhythm and phase-dependent spindle activity, although effects on memory retention have varied. Here, we aim to disclose relations between stimulation induced hippocampo-thalamo-cortical activity and retention performance on a hippocampus dependent task in mice. Closed-loop acoustic stimuli applied during four SO phases always acutely increased sharp wave ripple (SPWR) activity without disrupting non-rapid eye movement (NREM) sleep. Stimulation achieved an above chance preference index for stimuli delivered across a 3 h retention interval of sleep at the SO Up-state and at the Down-to-Up-state, but not at the Down-state nor late Up-state/Up-to Down-state. Results support the use of closed-loop acoustic stimulation in mice to investigate the inter-regional mechanisms underlying memory consolidation.

neuroscience↗

bioRxiv2022

Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality

Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plumes composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.

neuroscience↗