Biology subjects

Losonczy, A.

Publications and source records attributed to Losonczy, A..

At least 19 recordsLinked to original sources

bioRxiv2026

NeuralFieldManifold: Uncovering the Manifold Geometry of Local Field Potentials

Manifold geometry has become a powerful framework for understanding how the brain organizes high-dimensional neural activity into compact, interpretable structure. To date, however, most theoretical and methodological advances in neural manifold analysis have focused on spiking activity, leaving unresolved a fundamental question: What are the geometric principles governing the dynamics of local field potentials (LFPs); population level signals reflecting integrated subthreshold and suprathreshold neural activity that are broadly integral to clinical and brain-computer interface settings. Here, we establish, analytically and empirically, that the lag-embedded dynamics of LFP signals lie on a low-dimensional K-torus, where K directly relates to sustained oscillatory modes present in the signal. Modeling LFPs as bounded autoregressive processes, we show that each oscillatory component contributes an independent circular degree of freedom, while aperiodic 1/f structure and noise contribute only geometric thickness around the manifold without altering its topology. To apply this theory to real nonstationary recordings, we introduce DeepLagField, a physics-informed network that jointly estimates time-varying AR structure and effective model order, with formal guarantees that local toroidal geometry is preserved despite non-stationary nature of neuronal processes. Persistent homology analysis on real recordings reveals topological signatures consistent with the predicted toroidal Betti numbers in primate V1 LFP, rodent hippocampal LFP, and mouse cortical EEG, with varying degrees of agreement across recordings and temporal windows. Crucially, decoding analyses across three distinct neural datasets demonstrate that these geometric structures are not merely descriptive but capture behaviorally relevant information rivaling conventional spectral features. Together, these findings establish a theoretically grounded and empirically validated geometric framework for neural field analysis, extending the manifold perspective beyond spiking activity and offering a complementary language to spectral analysis for understanding and translating population dynamics across neural modalities.

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bioRxiv2026

Hippocampo-septal inhibitory feedback controls the medial septal circuit as a function of brain state, pyramidal cell activity and movement speed.

Encoding novel experiences and retrieving stored information rely on the hippocampal theta rhythm in several mammalian species. Coordination of putative pacemakers in the medial septum (MS) by the GABAergic hippocampo-septal (HS) feedback is hypothesized to be essential for theta genesis. In order to reveal how the HS-feedback controls the MS circuit, and through it hippocampal oscillations in general, we set out to uncover the behavior-related activity of HS neurons as well as how different components of the MS network are regulated by the HS-feedback. Utilizing computational modelling and in vivo optogenetic manipulation of the HS pathway, we show that the HS feedback is not required for theta generation, which is primarily governed by intra-MS connections. HS blockade did not alter the phase coupling of MS neurons to hippocampal theta rhythm nor the synchrony between rhythmic MS neurons but augmented ongoing theta and coupled mid-gamma oscillations in the hippocampus. By contrast, correlated activity between non-rhythmic MS neurons was sensitive to HS blockade. Moreover, the HS feedback scaled with hippocampal activity and running speed. In turn, modulation of MS neurons by running and ripples was diminished by the transient suppression of HS terminals. These findings suggest that the HS feedback exerts inhibitory control of the MS depending upon hippocampal output and movement speed.

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bioRxiv2026

Photon-Resolved Excitation-Denoised (PRED) Three-Photon Imaging Improves Detection of Neuronal Activity in Awake and Behaving Mice

Three-photon microscopy (3PM) has enabled the optical access of neurons [~]500-1500 {micro}m below the brain surface but has been limited to slow imaging frame rates or small total imaged areas by competing constraints: a high peak power requirement for nonlinear excitation, and the need to limit total average power. Additionally, the high sensitivity to laser power fluctuations and the inherently dim signals introduce additional challenges and add error. When combined with other effects, like brain motion in behaving animals, 3P imaging of neuronal activity during animal behavior has remained inadequate. Herein, we systematically address these limitations by 1) carefully balancing scanning speed with power requirements, 2) using a deeply cooled silicon photomultiplier detector and Bayesian statistics-based processing (PRED-correction) to reduce excess noise, and 3) by spatiotemporal shaping of excitation pulses. Our improvements enable rapid (20-30Hz) imaging of calcium activity in the dorsal hippocampal dentate gyrus of behaving mice, allowing the identification of spatially tuned neurons and the recapitulation of established functional properties across different cell types in this brain region. PRED-3P imaging provides a new approach to functional characterization of cells deep in the brain that were previously inaccessible to two-photon imaging.

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bioRxiv2026

A transcriptomic axis aligns with in vivo functional dynamics in hippocampal inhibitory circuits

Linking molecular identity to function in vivo at single-cell resolution remains an outstanding challenge in neuroscience. Here, we bridge this gap in the mouse hippocampus with an end-to-end pipeline of cell-resolved two-photon imaging and spatial transcriptomics. CA1 interneurons exhibiting heterogeneous physiological responses during a virtual-reality navigation task were post hoc clustered by gene expression into 5 GABAergic subclasses and 14 types. Physiological responses of individual cells aligned with a transcriptomic axis, and a classifier trained on physiological features alone recovered the same ordered organization. Our approach establishes a direct, scalable framework for linking in vivo circuit dynamics to constituent cell identity, revealing a transcriptomic axis that encompasses the structural and functional diversity of hippocampal inhibitory neurons. One-Sentence SummaryTracking neurons from behavior to spatial transcriptomics links in vivo function to molecular identity in the hippocampus.

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bioRxiv2025

From Morphology to Computation: How Synaptic Organization Shapes Place Fields in CA1 Pyramidal Neurons

How spatial organization of synaptic inputs along the dendritic tree of neurons influence their feature selectivity is a central question in neuroscience. Here, we mapped the three-dimensional distribution of all excitatory and inhibitory synapses across the entire dendritic arbor of individual mouse CA1 pyramidal neurons in vivo and built biophysically detailed computational models to probe their functional impact on their ability to emerge as place cells. We found that excitatory, but not inhibitory, synapses are non-uniformly distributed, forming structural clusters preferentially on terminal dendrites. These excitatory synaptic clusters generate high-quality place fields more efficiently than randomized synaptic distributions, requiring fewer active synapses to achieve equivalent somatic output. Crucially, even when firing rates are matched, clustered inputs sustain significantly higher voltage-gated calcium influx and NMDA receptor activation, key substrates for synaptic plasticity. Further analysis reveals that clustering enables domain-specific computational strategies: oblique dendrites rely on cluster location, basal dendrites on cumulative synaptic strength, and the trunk on local input dispersion. Disrupting clustering collapses this compartmentalized processing into uniform summation. Our results establish synaptic clustering as a key mechanism that maximizes computational efficiency and enables sophisticated dendritic processing underlying hippocampal spatial representation.

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bioRxiv2025

Rare mutations implicate CGE interneurons as a vulnerable axis of cognitive deficits across psychiatric disorders

Neuropsychiatric disorders such as autism spectrum disorder (ASD) and schizophrenia (SCZ) share genetic risk factors, including genes affected by rare high-penetrance single nucleotide variants (SNVs) and copy number variants (CNVs). ASD and SCZ exhibit both overlapping and distinct clinical phenotypes. Cognitive deficits and intellectual disability--critical predictors of long-term outcomes--are common to both conditions. To investigate shared and disorder-specific neurobiological impact of highly penetrant rare mutations in ASD and SCZ, we analyzed human single-nucleus whole-brain sequencing data to identify strongly affected brain cell types. Our analysis revealed caudal ganglionic eminence (CGE)-derived GABAergic interneurons as a key nexus for cognitive deficits across these disorders. Notably, genes within 22q11.2 deletions, known to confer a high risk for SCZ, ASD, and cognitive impairment, showed a strong expression bias toward vasoactive intestinal peptide-expressing cells (VIP+) among CGE subtypes. To explore perturbations of VIP+ GABAergic interneurons in the 22q11.2 deletion syndrome in vivo, we examined their activity in the Df(16)A+/- mouse model during a spatial navigation task and observed reduced activity along with altered responses to random rewards. At the population level, VIP+ interneurons exhibited impaired spatial encoding and diminished subtype-specific activity suggesting deficient disinhibition in CA1 microcircuits in the hippocampus, a region essential for learning and memory. Overall, these results demonstrate the crucial role of CGE-derived interneurons in mediating cognitive processes that are disrupted across a range of psychiatric and neurodevelopmental disorders.

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bioRxiv2025

Optimal sparsity in autoencoder memory models of the hippocampus

Storing complex correlated memories is significantly more efficient when memories are recoded to obtain compressed representations. Previous work has shown that compression can be implemented in a simple neural circuit, which can be described as a sparse autoencoder. The activity of the encoding units in these models recapitulates the activity of hippocampal neurons recorded in multiple experiments. However, these investigations have assumed that the level of sparsity is fixed and that inputs have the same statistics and, hence, that they are uniformly compressible. In contrast, biological agents encounter environments with vastly different memory demands and compressibility. Here, we investigate whether the compressibility of inputs determines optimal sparsity in sparse autoencoders. We find 1) that as the compressibility of inputs increases, the optimal coding level decreases, 2) that the desired coding level diverges from the observed coding level as a function of both memory demand and input compressibility, and 3) that optimal memory capacity is achieved when sparsity is weakly enforced. In addition, we characterize how sparsity and the strength of sparsity enforcement jointly control optimal performance. These results provide predictions for how sparsity in the hippocampus should change in response to environmental statistics and theoretical grounds for why sparsity is dynamically tuned in the brain.

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bioRxiv2024

Recurrent Connectivity Shapes Spatial Coding in Hippocampal CA3 Subregions

Stable and flexible neural representations of space in the hippocampus are crucial for navigating complex environments. However, how these distinct representations emerge from the underlying local circuit architecture remains unknown. Using two-photon imaging of CA3 subareas during active behavior, we reveal opposing coding strategies within specific CA3 subregions, with proximal neurons demonstrating stable and generalized representations and distal neurons showing dynamic and context-specific activity. To test the causal role of excitatory connectivity in neural computation, we employed a genetic manipulation approach in which local disruption of glutamatergic synaptic transmission impaired context-specific spatial coding in distal CA3. Aligned with these experimental results, we show in artificial neural network models that varying the recurrence level causes these differences in coding properties to emerge. We confirmed the contribution of recurrent connectivity to functional heterogeneity by characterizing the representational geometry of neural recordings and comparing it with theoretical predictions of neural manifold dimensionality. Our results indicate that local circuit organization, particularly recurrent connectivity among excitatory neurons, plays a key role in shaping complementary spatial representations within the hippocampus. HighlightsO_LIProximodistal CA3 subregions implement complementary coding strategies in relation to time and context C_LIO_LIDisrupting excitatory recurrence in distal CA3 impairs context-discriminative neural coding C_LIO_LISparse and dense neural networks capture the functional heterogeneity of CA3 subcircuits C_LIO_LIRecurrence level tunes the geometry of neural manifolds both in vivo and in silico C_LI

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bioRxiv2024

Disorganized Inhibitory Dynamics and Functional Connectivity in Hippocampal area CA1 of 22q11.2 Deletion Mutant Mice

Individuals with the 22q11.2 deletion syndrome, one of the strongest genetic risk factors for schizophrenia, demonstrate cognitive impairments, including episodic memory dysfunction. Place cell activity of excitatory pyramidal neurons in the hippocampus supporting episodic memory is impaired in a mouse model for the 22q11.2 deletion (Df(16)A+/-). While excitatory dynamics are under tight inhibitory control by multiple subtypes of GABAergic interneurons, previous studies have predominantly focused on a single subtype of PV-expressing interneurons; there have not yet been studies describing the functional relationships between molecularly identified inhibitory types in Df(16)A+/-mice. Here, we examined interneuron subtype-specific activity dynamics in the dorsal hippocampal area CA1 of Df(16)A+/- mice during random foraging and spatial reward navigation tasks. Capitalizing on 3D acousto-optical deflector two-photon microscopy with post hoc immunohistochemical identification, we found that multiple interneuron types exhibit aberrant responses to reward locations and delayed reward enrichment extinction. Df(16)A+/- inhibitory interneurons also carry markedly reduced spatial information in a subtype-dependent manner. We observed task-dependent changes in the correlation structure and coactivity among multiple GABAergic subtypes, suggesting a broadly disorganized microcircuit functionality in mutant mice. Overall, we identify widespread and heterogeneous subtype-specific alterations in interneuron dynamics during spatial reward navigation, reflecting impaired flexibility and organization in CA1 inhibitory microcircuits. Our study provides critical insights into how schizophrenia-risk mutations affect local-circuit interactions among diverse cell types in the mouse hippocampus during learning and spatial navigation.

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bioRxiv2024

2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons

Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the molecular properties onto functionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. Using this novel function-forward pipeline to selectively label and transcriptionally profile previously inaccessible place and silent cells in the hippocampus of behaving mice, we identify unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits. One-Sentence Summary2P-NucTag - A novel high-throughput on-demand phototagging approach to identify selective gene expression of functionally distinct neurons in vivo in behaving animals.

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bioRxiv2024

Distal tuft dendrites shape and maintain new place fields

Hippocampal pyramidal neurons support episodic memory by integrating complementary information streams into new place fields. Distal tuft dendrites are widely thought to initiate place field formation by locally generating prolonged, globally-spreading Ca2+spikes known as plateau potentials. However, the hitherto experimental inaccessibility of distal tuft dendrites in the hippocampus has rendered their in vivo function entirely unknown. Here we gained direct optical access to this elusive dendritic compartment. We report that distal tuft dendrites do not serve as the point of origin for place field-forming plateau potentials. Instead, the timing and extent of peri-formation distal tuft recruitment is variable and closely predicts multiple properties of resultant place fields. Therefore, distal tuft dendrites play a more powerful role in hippocampal feature selectivity than simply initiating place field formation. Moreover, place field formation is not accompanied by global Ca2+influx as previously thought. In addition to shaping new somatic place fields, distal tuft dendrites possess their own local place fields. Tuft place fields are back-shifted relative to that of their soma and appear to maintain somatic place fields via post-formation plateau potentials. Through direct in vivo observation, we provide a revised dendritic basis for hippocampal feature selectivity during navigational learning.

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bioRxiv2024

Functional architecture of intracellular oscillations in hippocampal dendrites

Fast electrical signaling in dendrites is central to neural computations that support adaptive behaviors. Conventional techniques lack temporal and spatial resolution and the ability to track underlying membrane potential dynamics present across the complex three-dimensional dendritic arbor in vivo. Here, we perform fast two-photon imaging of dendritic and somatic membrane potential dynamics in single pyramidal cells in the CA1 region of the mouse hippocampus during awake behavior. We study the dynamics of subthreshold membrane potential and suprathreshold dendritic events throughout the dendritic arbor in vivo by combining voltage imaging with simultaneous local field potential recording, post hoc morphological reconstruction, and a spatial navigation task. We systematically quantify the modulation of local event rates by locomotion in distinct dendritic regions and report an advancing gradient of dendritic theta phase along the basal-tuft axis, then describe a pre-dominant hyperpolarization of the dendritic arbor during sharp-wave ripples. Finally, we find spatial tuning of dendritic representations dynamically reorganizes following place field formation. Our data reveal how the organization of electrical signaling in dendrites maps onto the anatomy of the dendritic tree across behavior, oscillatory network, and functional cell states.

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bioRxiv2023

behaviorMate: An Intranet of Things Approach for Adaptable Control of Behavioral and Navigation-Based Experiments

1Investigators conducting behavioral experiments often need precise control over the timing of the delivery of stimuli to subjects and to collect the precise times of the subsequent behavioral responses. Furthermore, investigators want fine-tuned control over how various multi-modal cues are presented. behaviorMate takes an "Intranet of Things" approach, using a networked system of hardware and software components for achieving these goals. The system outputs a file with integrated timestamp-event pairs that investigators can then format and process using their own analysis pipelines. We present an overview of the electronic components and GUI application that make up behaviorMate as well as mechanical designs for compatible experimental rigs to provide the reader with the ability to set up their own system. A wide variety of paradigms are supported, including goal-oriented learning, random foraging, and context switching. We demonstrate behaviorMates utility and reliability with a range of use cases from several published studies and benchmark tests. Finally, we present experimental validation demonstrating different modalities of hippocampal place field studies. Both treadmill with burlap belt and virtual reality with running wheel paradigms were performed to confirm the efficacy and flexibility of the approach. Previous solutions rely on proprietary systems that may have large upfront costs or present frameworks that require customized software to be developed. behaviorMate uses open-source software and a flexible configuration system to mitigate both concerns. behaviorMate has a proven record for head-fixed imaging experiments and could be easily adopted for task control in a variety of experimental situations.

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bioRxiv2023

Synaptic Basis of Behavioral Timescale Plasticity

Learning and memory are fundamental to adaptive behavior and cognition. Various forms of synaptic plasticity have been proposed as cellular substrates for the emergence of feature selectivity in neurons underlying episodic memory. However, despite decades of work, our understanding of how synaptic plasticity underlies memory encoding remains limited, largely due to a shortage of tools and technical challenges associated with the visualization of synaptic plasticity at single-neuron resolution in awake-behaving animals. Behavioral Timescale Synaptic Plasticity (BTSP) postulates that synaptic inputs active during a seconds-long time window preceding and immediately following a large depolarizing plateau spike are potentiated, while synaptic inputs active outside this time window are depressed. We experimentally tested this model in vivo in awake-behaving mice using an all-optical approach by inducing place fields (PFs) in single CA1 pyramidal neurons (CA1PNs) while monitoring the spatiotemporal tuning of individual dendritic spines and changes in their corresponding synaptic weights. We identified an asymmetric synaptic plasticity kernel resulting from bidirectional modifications of synaptic weights around plateau burst induction. Surprisingly, our work also uncovered compartment-specific differences in the magnitude and temporal expression of synaptic plasticity between basal and oblique dendrites of CA1PNs. Our results provide the first experimental evidence linking synaptic plasticity to the rapid emergence of spatial selectivity in hippocampal neurons, a critical prerequisite for episodic memory.

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bioRxiv2023

Organization and Plasticity of Inhibition in Hippocampal Recurrent Circuits

Excitatory-inhibitory interactions structure recurrent network dynamics for efficient cortical computations. In the CA3 area of the hippocampus, recurrent circuit dynamics, including experience-induced plasticity at excitatory synapses, are thought to play a key role in episodic memory encoding and consolidation via rapid generation and flexible selection of neural ensembles. However, in vivo activity of identified inhibitory motifs supporting this recurrent circuitry has remained largely inaccessible, and it is unknown whether CA3 inhibition is also modifiable upon experience. Here we use large-scale, 3-dimensional calcium imaging and retrospective molecular identification in the mouse hippocampus to obtain the first comprehensive description of molecularly-identified CA3 interneuron dynamics during both spatial navigation and sharp-wave ripple (SWR)-associated memory consolidation. Our results uncover subtype-specific dynamics during behaviorally distinct brain-states. Our data also demonstrate predictive, reflective, and experience-driven plastic recruitment of specific inhibitory motifs during SWR-related memory reactivation. Together these results assign active roles for inhibitory circuits in coordinating operations and plasticity in hippocampal recurrent circuits.

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bioRxiv2022

Adult born hippocampal granule cells promote pattern separation by bidirectionally modulating the remapping of place and cue cells.

The hippocampal dentate gyrus (DG) exhibits a unique form of neural plasticity that results from continuous integration of adult born neurons, referred to as adult neurogenesis. Recent studies have proposed that adult neurogenesis promotes the ability to encode new memories without interference from previously stored memories that share similar features, through a neural computation known as pattern separation. However, due to lack of in vivo physiological evidence, the manner in which adult neurogenesis contributes to pattern separation remains unknown. Here, we investigate the contribution of functionally integrated yet immature adult born granule cells (iGCs) to DG computations by examining how chronic ablation or acute chemogenetic silencing of iGCs affects the activity of mature granule cells (mGCs) using in vivo 2-photon Ca2+ imaging. In both cases we observed altered remapping of mGCs but in opposite directions depending on their tuning selectivity. Rather than broadly modulating the activity of all mGCs, iGCs promote the remapping of place cells but limit the remapping of mGCs representing sensory cues (cue cells). We propose that these properties of iGCs explain their role in pattern separation because they promote the formation of non-overlapping representations for identical sensory cues encountered in different locations. Conversely, the absence of iGCs shifts the DG network to a state dominated by sensory cue information, a situation that is consistent with the overgeneralization often observed in anxiety disorders such as PTSD.

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bioRxiv2022

Direct Cortical Inputs to Hippocampal Area CA1 Transmit Complementary Signals for Goal-directed Navigation

The entorhinal cortex (EC) is central to the brains navigation system. Its subregions are conventionally thought to compute dichotomous representations for spatial processing: medial entorhinal cortex (MEC) provides a global spatial map, while lateral entorhinal cortex (LEC) encodes specific sensory details of experience. While local recordings of EC circuits have amassed a vast catalogue of specialized cell types that could support navigation computations in the brain, we have little direct evidence for how these signals are actually transmitted outside of the EC to its primary downstream reader, the hippocampus, which itself is critical for the formation of spatial and episodic memories. Here we exploit in vivo sub-cellular imaging to directly record from EC axon terminals as they locally innervate hippocampal area CA1, while mice performed navigational and spatial learning tasks in virtual reality. We find both distinct and overlapping representations of task, location, and context in both MEC and LEC axons. While MEC transmitted a highly location- and context-specific code, LEC inputs were strongly biased by ongoing navigational goals and reward. Surprisingly, the position of the animal could be accurately decoded from either entorhinal subregion. Our results challenge prevailing dogma on the routing of spatial and non-spatial information from the cortex to the hippocampus, indicating that cortical interactions upstream of the hippocampus are critical for combining these processing streams to support navigation and memory.

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bioRxiv2022

An inhibitory plasticity mechanism for world structure inference by hippocampal replay

Memory consolidation assimilates recent experiences into long-term memory. This process requires the replay of learned sequences, though the content of these sequences remains controversial. Recent work has shown that the statistics of replay deviate from those of experience: stimuli which are experientially salient may be either selected or suppressed. We find that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike time-dependent plasticity rule at inhibitory synapses. Using spiking networks at three levels of abstraction-leaky integrate-and-fire, biophysically detailed, and abstract binary-we show that this rule enables efficient inference of a model of the structure of the world. We present analytical results that these replayed sequences converge to ground truth under a mathematical model of replay. Finally, we make specific predictions about the consequences of intact and perturbed inhibitory dynamics for network dynamics and cognition. Our work outlines a potential direct link between the synaptic and cognitive levels of memory consolidation, with implications for both normal learning and neurological disease.

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