Biology subjects

Soltesz, I.

Publications and source records attributed to Soltesz, I..

17 recordsLinked to original sources

bioRxiv2026

Hippocampal microcircuits constrain the generation of epileptiform activity

Epilepsy is largely characterized using macroscale measures of neural activity, such as electroencephalography, which are unable to resolve the underlying cellular-level substrates of pathological activity. Although there is a growing understanding that hippocampal microcircuits, comprised of distinct CA1 principal cells (PCs), inhibitory neurons, and input/output relationships, route information through parallel hippocampal pathways, the relevance of this microscale organization in epilepsy is poorly understood. To address this gap, we focally knocked out the {beta}3 subunit of the GABAA receptor from CA1 PCs, which strongly impaired parvalbumin-mediated inhibition to deep PCs, but not superficial PCs - potentially promoting a microcircuit-selective epilepsy manifestation. Indeed, we observed robust interictal epileptiform discharges (IEDs) at the site of focal knockout with features consistent with a potential CA2-to-deep CA1 PC generation mechanism. In line with prior observations that CA2 inputs potently drive deep PCs under physiological conditions, IEDs could be reliably evoked by optogenetic stimulation of CA2 in {beta}3 focal knockout mice. Resolving cellular activity across the CA1 PC network during IEDs, in vivo 2-photon calcium imaging demonstrated stronger activation of deep versus superficial PCs. Altogether, these data demonstrate that macroscopic electrophysiological patterns such as IEDs can have underlying microcircuit constraints, which has important implications for designing therapies that target the underlying microcircuit generators of epileptiform activity.

neuroscience↗

bioRxiv2025

LIVN: A testbed for learning to interact with in vitro neural networks

AO_SCPLOWBSTRACTC_SCPLOWThe investigation of cultured biological neural networks is a critical frontier in neuroscience with profound implications for the advancement of brain-machine interfaces, treatments of neurological diseases, and fundamental insights into neural computation and cognition. Advances in induced pluripotent stem cell (iPSC) technology and machine learning are converging to enable novel approaches to interrogating neural circuits in vitro. However, progress in this emerging field is hampered by the technical challenges and resource-intensive nature of acquiring datasets suitable for machine learning. Experimental recordings typically do not allow for interactive learning and generally lack ground-truth information that would enable rigorous algorithm development and validation. To overcome the limitations of experimental setups, simulated biophysical models of neurons and neuronal networks can serve as crucial accelerators. They allow for more controlled and systematic exploration than currently possible with living cultures and serve as interpretable intermediaries between abstract computational theory and complex biological reality. Using this approach, we introduce livn: an open source interactive simulation environment for learning to control in vitro neural networks. livn generates synthetic neural data with ground truth at scale, enabling the development and testing of ML models in interactive settings that mimic experimental platforms. We describe benchmark tasks that challenge ML models to exploit simulated neural dynamics and release generated synthetic datasets that mimic in vitro systems. By providing an open, extensible platform for developing and benchmarking machine learning models, livn aims to accelerate progress in both ML-driven understanding and engineering of in vitro neural systems and fundamental understanding of computation in biological neural networks.

neuroscience↗

bioRxiv2024

LoCS-Net: Localizing Convolutional Spiking Neural Network for Fast Visual Place Recognition

Visual place recognition (VPR) is the ability to recognize locations in a physical environment based only on visual inputs. It is a challenging task due to perceptual aliasing, viewpoint and appearance variations and complexity of dynamic scenes. Despite promising demonstrations, many state-of-the-art VPR approaches based on artificial neural networks (ANNs) suffer from computational inefficiency. Spiking neural networks (SNNs), on the other hand, implemented on neuromorphic hardware, are reported to have remarkable potential towards more efficient solutions computationally, compared to ANNs. However, the training of the state-of-the-art (SOTA) SNNs for the VPR task is often intractable on large and diverse datasets. To address this, we develop an end-to-end convolutional SNN model for VPR, that leverages back-propagation for tractable training. Rate-based approximations of leaky integrate-and-fire (LIF) neurons are employed during training to enable back-propagation, and the approximation units are replaced with spiking LIF neurons during inference. The proposed method outperforms the SOTA ANNs and SNNs by achieving 78.2% precision at 100% recall on the challenging Nordland dataset, compared with 53% SOTA performance, and exhibits competitive performance on the Oxford RobotCar dataset while being easier to train and faster in both training and inference when compared to other ANN and SNN-based methods.

neuroscience↗

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.

neuroscience↗

bioRxiv2022

Topological supramolecular network enabled highly conductive and stretchable organic bioelectronics

Intrinsically stretchable bioelectronic devices based on soft and conducting organic materials have been regarded as the ideal interface for seamless and biocompatible integration with the human body. However, the grand challenge remains for the conducting polymer to possess both high mechanical ductility and good electrical conduction at cellular level feature sizes. This longstanding material limitation in organic bioelectronics has impeded the full exploitation of its unique benefits. Here, we introduce a new molecular engineering strategy based on rationally designed topological supramolecular networks, which allows effective decoupling of competing effects from multiple molecular building blocks to meet complex requirements. We achieve two orders of magnitude improvement in the conductivity under 100% strain in physiological environment, along with the capability for direct photopatterning down to 2 m. These unprecedented capabilities allow us to realize previously inaccessible bioelectronic applications including high-resolution monitoring of soft and malleable creatures, e.g., octopus, and localized neuromodulation down to single nucleus precision for controlling organ-specific activities through delicate tissues, e.g., brainstem.

bioengineering↗

bioRxiv2021

Towards a general framework for modeling large-scale biophysical neuronal networks: a full-scale computational model of the rat dentate gyrus

Large-scale computational models of the brain are necessary to accurately represent anatomical and functional variability in neuronal biophysics across brain regions and also to capture and study local and global interactions between neuronal populations on a behaviorally-relevant temporal scale. We present the methodology behind and an initial implementation of a novel open-source computational framework for construction, simulation, and analysis of models consisting of millions of neurons on high-performance computing systems, based on the NEURON and CoreNEURON simulators (Carnevale and Hines, 2006, Kumbhar et al., 2019). This framework uses the HDF5 data format and software library (HDF Group, 2021) and includes a data format for storing morphological, synaptic, and connectivity information of large neuronal network models, and an accompanying open-source software library that provides efficient, scalable parallel storage and MPI-based data movement capabilities. We outline our approaches for constructing detailed large-scale biophysical models with topographical connectivity and input stimuli, and present simulation results obtained with a full-scale model of the dentate gyrus constructed with our framework. The model generates sparse and spatially selective population activity that fits well with in-vivo experimental data. Moreover, our approach is fully general and can be applied to modeling other regions of the hippocampal formation in order to rapidly evaluate specific hypotheses about large-scale neural architectural features.

neuroscience↗

bioRxiv2021

Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics

During spatial exploration, neural circuits in the hippocampus store memories of sequences of sensory events encountered in the environment. When sensory information is absent during "offline" resting periods, brief neuronal population bursts can "replay" sequences of activity that resemble bouts of sensory experience. These sequences can occur in either forward or reverse order, and can even include spatial trajectories that have not been experienced, but are consistent with the topology of the environment. The neural circuit mechanisms underlying this variable and flexible sequence generation are unknown. Here we demonstrate in a recurrent spiking network model of hippocampal area CA3 that experimental constraints on network dynamics such as population sparsity, stimulus selectivity, rhythmicity, and spike rate adaptation enable additional emergent properties, including variable offline memory replay. In an online stimulus-driven state, we observed the emergence of neuronal sequences that swept from representations of past to future stimuli on the timescale of the theta rhythm. In an offline state driven only by noise, the network generated both forward and reverse neuronal sequences, and recapitulated the experimental observation that offline memory replay events tend to include salient locations like the site of a reward. These results demonstrate that biological constraints on the dynamics of recurrent neural circuits are sufficient to enable memories of sensory events stored in the strengths of synaptic connections to be flexibly read out during rest and sleep, which is thought to be important for memory consolidation and planning of future behavior.

neuroscience↗

bioRxiv2021

The Neurodata Without Borders ecosystem for neurophysiological data science

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWBs impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

neuroscience↗

bioRxiv2020

Higher-order hub cells involved in feedforward motifs as critical factors in epileptic network instability

Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. We studied epileptic circuits using a newly developed integrated computational pipeline applied to cellular resolution functional imaging data. Control and preseizure neural dynamics in larval zebrafish and in chronically epileptic mice were captured using large-scale cellular-resolution calcium imaging. Biologically constrained effective connectivity modeling extracted the underlying cell-cell communication network. Novel analysis of the higher-order network structure revealed the existence of superhub cells that are unusually richly connected to the rest of the network through feedforward motifs. Instability in epileptic networks was causally linked to superhubs whose involvement in feedforward motifs critically enhanced downstream excitation. Disconnecting individual superhubs was significantly more effective in stabilizing epileptic networks compared to disconnecting hub cells defined traditionally by connection count. Collectively, these results predict a new, maximally selective and minimally invasive cellular target for seizure control. HighlightsO_LIHigher-order connectivity patterns of large-scale neuronal communication networks were studied in zebrafish and mice C_LIO_LIControl and epileptic networks were modeled from in vivo cellular resolution calcium imaging data C_LIO_LIRare superhub cells unusually richly connected to the rest of the network through higher-order feedforward motifs were identified C_LIO_LIDisconnecting single superhub neurons more effectively stabilized epileptic networks than targeting conventional hub cells defined by high connection count. C_LIO_LIThese data predict a maximally selective novel single cell target for minimally invasive seizure control C_LI

neuroscience↗

bioRxiv2020

A fluorescent sensor for spatiotemporally resolved endocannabinoid dynamics in vitro and in vivo

Endocannabinoids (eCBs) are retrograde neuromodulators that play an important role in a wide range of physiological processes; however, the release and in vivo dynamics of eCBs remain largely unknown, due in part to a lack of suitable probes capable of detecting eCBs with sufficient spatiotemporal resolution. Here, we developed a new eCB sensor called GRABeCB2.0. This genetically encoded sensor consists of the human CB1 cannabinoid receptor fused to circular-permutated EGFP, providing cell membrane trafficking, second-resolution kinetics, high specificity for eCBs, and a robust fluorescence response at physiological eCB concentrations. Using the GRABeCB2.0 sensor, we monitored evoked changes in eCB dynamics in both cultured neurons and acute brain slices. Interestingly, in cultured neurons we also observed spontaneous compartmental eCB transients that spanned a distance of approximately 11 m, suggesting constrained, localized eCB signaling. Moreover, by expressing GRABeCB2.0 in the mouse brain, we readily observed foot shock-elicited and running-triggered eCB transients in the basolateral amygdala and hippocampus, respectively. Lastly, we used GRABeCB2.0 in a mouse seizure model and observed a spreading wave of eCB release that followed a Ca2+ wave through the hippocampus. Thus, GRABeCB2.0 is a robust new probe for measuring the dynamics of eCB release under both physiological and pathological conditions.

neuroscience↗

bioRxiv2020

Adult-born granule cells support pathological microcircuits in the chronically epileptic dentate gyrus

Temporal lobe epilepsy (TLE) is characterized by recurrent seizures driven by synchronous neuronal activity. The dentate gyrus (DG) region of the hippocampal formation is highly reorganized in chronic TLE; in particular, pathological remodeling of the "dentate gate" is thought to open up pathological conduction pathways for synchronous discharges and seizures in the mesial temporal lobe. However, this pathophysiological framework lacks a mechanistic explanation of how macroscale synchronous dynamics emerge from alterations of the DG at the microcircuit level. In particular, the relative contribution of developmentally defined subpopulations of adult-born (abGCs) and mature (mGCs) granule cells to epileptiform network events remains unknown. To address this question, we optically recorded activity dynamics of identified populations of abGCs and mGCs during interictal epileptiform discharges (IEDs) in mice with chronic TLE. We find that disjoint subsets of IEDs differentially recruit abGC and mGC populations. We used these observations to develop a neural topic modeling framework, under which we find that the epileptic DG network organizes into disjoint, cell-type specific pathological ensembles, a subset of which are recruited by each IED. We found that statistics of this ensemble structure are highly conserved across animals, with abGCs disproportionately driving network activity in the epileptic DG during IEDs. Our results provide the first in vivo characterization of activity dynamics of identified GC subpopulations in the epileptic DG, the first microcircuit-level correlates of IEDs in vivo, and reveal a specific contribution of abGCs to interictal epileptic events. HighlightsO_LIWe relate electrographic signatures of epilepsy to microcircuit dynamics at single-cell resolution C_LIO_LIThe chronically epileptic dentate gyrus granule cell network is organized in lineage-specific pathological ensembles C_LIO_LIA novel generative model framework for ensemble recruitment relates local field potential signatures to microcircuit activation during interictal epileptiform discharges C_LIO_LIAdult-born granule cell-dominated ensembles are disproportionately represented among the inferred ensembles C_LIO_LIThe most active ensemble during an interictal epileptiform discharge can be decoded directly from the local field potential spectrum C_LIO_LIThis Latent Ensemble Recruitment model of cell recruitment by interictal events is the first application of Bayesian topic modeling to in vivo two-photon calcium imaging data C_LI

neuroscience↗

bioRxiv2020

Bidirectional synaptic plasticity rapidly modifies hippocampal representations independent of correlated activity

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic plateau potentials has been reported to underlie place field formation in hippocampal CA1 neurons. Here we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience.

neuroscience↗

bioRxiv2020

The Neurovascular Basis of Postictal Amnesia

Long-lasting confusion and memory difficulties during the postictal state remain a major unmet problem in epilepsy that lacks pathophysiological explanation and treatment. We previously identified that long-lasting periods of severe postictal hypoperfusion/hypoxia, not seizures per se, are associated with memory impairment after temporal lobe seizures. While this observation suggests a key pathophysiological role for insufficient energy delivery, it is unclear how the networks that underlie episodic memory respond to vascular constraints that ultimately give rise to amnesia. Here, we focused on cellular/network level analyses in the CA1 of hippocampus in vivo to determine if neural activity, network oscillations, synaptic transmission, and/or synaptic plasticity are impaired following kindled seizures. Importantly, the induction of severe postictal hypoperfusion/hypoxia was prevented in animals treated by a COX-2 inhibitor, which experimentally separated seizures from their vascular consequences. We observed complete activation of CA1 pyramidal neurons during brief seizures, followed by a short period of reduced activity and flattening of the local field potential that resolved within minutes. During the postictal state, constituting tens of minutes to hours, we observed no changes in neural activity, network oscillations, and synaptic transmission. However, long-term potentiation of the temporoammonic pathway to CA1 was impaired in the postictal period, but only when severe local hypoxia occurred. Lastly, we tested the ability of rats to perform object-context discrimination, which has been proposed to require temporoammonic input to differentiate between sensory experience and the stored representation of the expected object-context pairing. Deficits in this task following seizures were reversed by COX-2 inhibition, which prevented severe postictal hypoxia. These results support a key role for hypoperfusion/hypoxia in postictal memory impairments and identify that many aspects of hippocampal network function are resilient during severe hypoxia except for long-term synaptic plasticity.

neuroscience↗

bioRxiv2019

The GABA-Glutamate supramammillary-dorsal Dentate Gyrus pathway controls theta and gamma oscillations in the DG during paradoxical sleep

Several studies suggest that neurons from the lateral region of the SuM (SuML) innervating the dorsal dentate gyrus (DG) display a dual GABAergic and glutamatergic transmission and are specifically activated during paradoxical (REM) sleep (PS). The objective of the present study is to fully characterize the anatomical, neurochemical and electrophysiological properties of the SuML-DG projection neurons and to determine how they control DG oscillations and neuronal activation during PS and other vigilance states. For this purpose, we combine structural connectivity techniques using neurotropic viral vectors (rabies virus, AAV), neurochemical anatomy (immunohistochemistry, in situ hybridization) and imaging (light, electron and confocal microscopy) with in vitro (patch clamp) and in vivo (LFP, EEG) optogenetic and electrophysiological recordings performed in transgenic VGLUT2-cre male mice. At the cellular level, we show that the SuML-DG neurons co-release GABA and glutamate on dentate granule cells and increase the activity of a subset of DG granule cells. At the network level, we show that activation of the SuML-DG pathway increases theta power and frequency during PS as well as gamma power during PS and waking in the DG. At the behavioral level, we show that the activation of this pathway does not change animal behavior during PS, induces awakening during slow wave sleep and increases motor activity during waking. These results suggest that the SuML-DG pathway is capable of supporting the increase of theta and gamma power in the DG observed during PS and plays an important modulatory role of DG network activity during this state.\n\nSignificant statementAn increase of theta and gamma power in the dentate gyrus (DG) is an hallmark of paradoxical (REM) sleep (PS) and is suggested to promote learning and memory consolidation by synchronizing hippocampal networks and increasing its outputs to cortical targets. However the neuronal networks involved in such control of DG activity during PS are poorly understood. The present study identifies a population of GABA/Glutamate neurons in the lateral supramammllary nucleus (SuML) innervating the DG that could support such control during PS. Indeed, we show that activation of these SuML-DG projections increase theta power and frequency as well as gamma power in the DG specifically during PS and modulate activity of a subset of DG granule cells.

neuroscience↗

bioRxiv2019

NWB:N 2.0: An Accessible Data Standard for Neurophysiology

Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

neuroscience↗

bioRxiv2016

Interneuronal mechanisms of hippocampal theta oscillation in a full-scale model of the rodent CA1 circuit

The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct in terneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.

neuroscience↗

bioRxiv2016

SimTracker tool and code template to design, manage and analyze neural network model simulations in parallel NEURON

Advances in technical computing enable larger and more detailed neural models that can incorporate ever more of the rapidly expanding body of quantitative neuroscience data. In principle, such complex network models that are strongly constrained by experimental data could direct experimental research and provide novel insights into experimental observations. However, as network models grow in complexity and scale, the necessary tasks of development and organization become unwieldy. Further, the models risk becoming inaccessible to experimentalists and other modelers, and their results may then be seen as less relevant to experimental work. To address these obstacles, we developed a tool for managing simulations called SimTracker. It supports users at each step of the modeling process, including execution of large scale parallel models on supercomputers. SimTracker is suitable for users with a range of modeling experience. SimTracker can be a valuable modeling resource that promotes iterative progress between experiment and model.

neuroscience↗