Biology subjects

Lengyel, M.

Publications and source records attributed to Lengyel, M..

18 recordsLinked to original sources

bioRxiv2026

The activation function of CA3 pyramidal neurons is optimal for the stable recall of memory patterns

Hippocampal area CA3 is widely believed to serve a core memory function: retrieving distributed patterns of neural activity that were previously stored in the recurrent connections between neurons. However, it remains unknown how the physiological properties of individual neurons contribute to this function. Here, we present a mathematical analysis of a canonical recurrent neural network model of CA3. Our analysis provides three main, experimentally testable predictions for recurrent circuits performing stable memory recall. Two of these predictions, that pyramidal cells should be characterized by elevated intrinsic excitability and by inhibition-dominated recurrent connections, are consistent with previous experimental findings. We thus focused on testing the third prediction: that neuronal activation functions have an exponent slightly above $1$. For this, we performed \textit{in vivo} intracellular recordings of 49 pyramidal neurons from the CA3 area of behaving mice. Fitting a range of parametric models to these voltage traces and performing statistical model comparison revealed an average neural activation exponent slightly above $1$, confirming our theoretical predictions, with substantial heterogeneity across cells, which remains to be accounted for by the theory. An analysis of 133 further cells from two other previous studies provided neural activation exponent estimates highly consistent with those we found in our data. Our results suggest that the properties of hippocampal CA3 area are tuned to support reliable memory recall even at the level of single neurons.

neuroscience↗

bioRxiv2026

Implicit vs. explicit choice promotes the combination vs. selection of sensorimotor memories under contextual uncertainty

Skilled action requires expressing motor memories as appropriate for the current context, but context is often uncertain. Theoretical models make conflicting proposals about memory expression under contextual uncertainty, predicting belief-weighted combination of memories versus the selection of the most probable memory. We tested these predictions by training human participants to reach in two opposing force fields cued by the direction of a random dot motion stimulus whose coherence varied. When participants moved before reporting dot direction, adaptation scaled with coherence: low-reliability cues produced partial expression of both memories. Fitting Bayesian observer models to behavior favored belief-weighted memory combination. In contrast, when participants reported their choice before moving, adaptation was independent of coherence and model fits favored categorical memory selection. Thus, sensorimotor memories are expressed as either a probabilistic combination or categorical selection, depending on whether participants contextual inference remains implicit or is made explicit at the time of memory expression.

animal behavior and cognition↗

bioRxiv2025

G protein subtype preference dictates paroxetine-enhanced serotonin receptor response in normal breast epithelial cells

In the healthy mammary gland serotonin (5-HT) has a key role in shaping tissue morphology and regulating lactation and involution. Serotonin re-uptake inhibitors (SSRIs) alter the distribution of 5-HT across the cell membrane, but how increased extracellular 5-HT may impact breast cell homeostasis is unclear. We demonstrate that paroxetine reduces 5-HT levels in immortalized primary breast epithelial (HME-hTert) cells, mitigates free oxygen radical formation and decreases cell migration and proliferation. Accordingly, pathways related to cell cycle and DNA damage repair were underrepresented in the transciptomic profile of paroxetine-treated cells. On the other hand, enriched transcipts overrepresented genes affecting neural transmission and GPCR signaling, suggesting an increase in 5-HT receptor (5HTR) activation. As 5-HT induced the levels of both cAMP and inositol triphosphate (IP3), the contribution of individual 5HTRs were decyphered using receptor-selective agonists and antagonists. 5HTRs coupled to each G protein subtype were expressed and functional in HME-hTert cells. The activation of GS-coupled receptor 5HTR7 and antagonists of Gi- and Gq-coupled receptors 5HTR1D and 5HTR2B generally suppressed proliferation. The induction of cAMP levels by 5-HT was reduced by the 5HTR7 antagonist and IP3 induction was blocked by an 5HTR2B inhibitor, while the 5HTR1D antagonist further increased cAMP levels induced by 5-HT. Paroxetine-dependent growth suppression was reversed by inhibitors of GS-coupled 5HTR7, protein kinase A or adenylyl cyclase, and agonists of Gi or Gq-coupled receptors. These results suggest that the anti-proliferative responses to 5-HT in non-malignant breast cells align with the G protein preference of the receptors, and reveal potential benefits of repurposing receptor subtype-selective agents and SSRIs for cancer risk reduction. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/658790v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1a4e2a4org.highwire.dtl.DTLVardef@1b9ea7org.highwire.dtl.DTLVardef@14cfe9dorg.highwire.dtl.DTLVardef@1e02960_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

The G-protein coupled receptor OXER1 is a tissue redox sensor essential for intestinal epithelial barrier integrity.

Generation of reactive oxygen species is an important part of the innate immune response. Generating microbicidal levels of reactive oxygen species (ROS) requires adaptation of mucosal barriers. High tolerance of ROS provides improved innate immune defenses against pathogens, whereas low tolerance renders host cells prone to chronic toxicity and mutagenesis, which can promote inflammation (e.g., in asthma and Crohns disease) and cancerogenesis. The mechanisms that sense and mediate host tolerance to ROS are little understood. In this study, we discover an unexpected role for the redox-sensitive, chemokine-like lipid 5-oxo-eicosatetraenoic acid (5-KETE) in redox adaptation. 5-KETE is known to attract leukocytes to damaged/infected mucosal barriers by signaling through its receptor, OXER1. Suggestive of a distinct non-immune function, we here report that the loss of the OXER1 ortholog Hcar1-4 causes barrier defects and baseline inflammation in the intestine of live zebrafish larvae. In zebrafish and cultured human cells, OXER1 signaling protects against oxidative nucleotide lesions by inducing DNA-protective Nudix hydrolases. Our data reveal the oxoeicosanoid pathway as a conserved ROS resilience mechanism that fortifies pathogen-exposed mucosal linings against increased oxidative stress in vivo.

immunology↗

bioRxiv2025

DHRS7 Integrates NADP+/NADPH Redox Sensing with Inflammatory Lipid Signalling via the Oxoeicosanoid Pathway

During the innate immune response at epithelial wound sites, oxidative stress acts microbicidal and--mechanistically less well understood--as an immune and resilience signal. The reversible sulfhydryl (SH) oxidation of kinases, phosphatases, and transcription factors constitute the perhaps best-known redox signalling paradigm, whereas mechanisms that transduce metabolic redox cues, such as redox cofactor balance, remain little explored. Here, using mammalian cells, microsomes, and live zebrafish, we identify DHRS7, a short-chain fatty acid dehydrogenase/reductase (SDR), as conserved, 5-hydroxyeicosanoid dehydrogenase (5-HEDH). Under oxidative stress, DHRS7 consumes NADP+ to convert arachidonic acid (AA)-derived 5(S)-HETE into the inflammatory lipid 5-KETE, which activates leukocyte chemotaxis via the OXER1 receptor. While Dhrs7 acts as a NADPH-dependent 5-KETE sink in unstressed, healthy tissue, it promotes rapid, 5-KETE dependent leukocytic inflammation in wounded zebrafish skin. Thus, DHRS7 epitomizes an underappreciated mode of redox signalling--beyond classic SH oxidation--that leverages NADPH metabolism to generate or quench a paracrine lipid signal. Metabolic redox sensors like DHRS7 might be promising therapeutic targets in diseases characterized by disturbed redox balance.

immunology↗

bioRxiv2025

Human milk oligosaccharide metabolism by Clostridium species suppresses inflammation and pathogen growth

Gut microbiome development is strongly impacted by breastmilk and human milk oligosaccharides (HMOs), with short- and long-term health implications. HMO metabolism is best characterised within bifidobacteria, but the full range of other HMO-utilising species remains unknown. This study examined HMO-utilising bacteria that colonise preterm infants (born <32 weeks gestation), their role in microbiome modulation, and their effects on intestinal barrier function. We found that infant-derived Clostridium perfringens, and three other Clostridium species, metabolise HMOs, producing short chain fatty acids, tryptophan catabolites, and other metabolites known to improve host health. C. perfringens inhibited pathobiont growth and supressed inflammation in preterm-derived intestinal organoids. These findings suggest a previously unrecognised role for C. perfringens, specifically those lacking the perfringolysin O gene, in promoting healthy gut development during infancy.

microbiology↗

bioRxiv2024

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA

Gaussian Process Factor Analysis (GPFA) is a powerful latent variable model for extracting low-dimensional manifolds underlying population neural activities. However, one limitation of standard GPFA models is that the number of latent factors needs to be pre-specified or selected through heuristic-based processes, and that all factors contribute at all times. We propose the infinite GPFA model, a fully Bayesian non-parametric extension of the classical GPFA by incorporating an Indian Buffet Process (IBP) prior over the factor loading process, such that it is possible to infer a potentially infinite set of latent factors, and the identity of those factors that contribute to neural firings in a compositional manner at each time point. Learning and inference in the infinite GPFA model is performed through variational expectation-maximisation, and we additionally propose scalable extensions based on sparse variational Gaussian Process methods. We empirically demonstrate that the infinite GPFA model correctly infers dynamically changing activations of latent factors on a synthetic dataset. By fitting the infinite GPFA model to population activities of hippocampal place cells during spatial tasks with alternating random foraging and spatial memory phases, we identify novel non-trivial and behaviourally meaningful dynamics in the neural encoding process.

neuroscience↗

bioRxiv2023

Bayesian nonparametric (non-)renewal processes foranalyzing neural spike train variability

Neural spiking activity is generally variable, non-stationary, and exhibits complex dependencies on covariates, such as sensory input or behavior. These dependencies have been proposed to be signatures of specific computations, and so characterizing them with quantitative rigor is critical for understanding neural computations. Approaches based on point processes provide a principled statistical framework for modeling neural spiking activity. However, currently, they only allow the instantaneous mean, but not the instantaneous variability, of responses to depend on covariates. To resolve this limitation, we propose a scalable Bayesian approach generalizing modulated renewal processes using sparse variational Gaussian processes. We leverage pathwise conditioning for computing nonparametric priors over conditional interspike interval distributions and rely on automatic relevance determination to detect lagging interspike interval dependencies beyond renewal order. After systematically validating our method on synthetic data, we apply it to two foundational datasets of animal navigation: head direction cells in freely moving mice and hippocampal place cells in rats running along a linear track. Our model exhibits competitive or better predictive power compared to state-of-the-art baselines, and outperforms them in terms of capturing interspike interval statistics. These results confirm the importance of modeling covariate-dependent spiking variability, and further analyses of our fitted models reveal rich patterns of variability modulation beyond the temporal resolution of flexible count-based approaches.

neuroscience↗

bioRxiv2023

Ignorance is bliss: effects of noise and metaboliccost on cortical task representations

Cognitive flexibility requires both the encoding of task-relevant and the ignoring of task-irrelevant stimuli. While the neural coding of task-relevant stimuli is increasingly well understood, the mechanisms for ignoring task-irrelevant stimuli remain poorly understood. Here, we study how task performance and biological constraints jointly determine the coding of relevant and irrelevant stimuli in neural circuits. Using mathematical analyses and task-optimized recurrent neural networks, we show that neural circuits can exhibit a range of representational geometries depending on the strength of neural noise and metabolic cost. By comparing these results with recordings from primate prefrontal cortex (PFC) over the course of learning, we show that neural activity in PFC changes in line with a minimal representational strategy. Specifically, our analyses reveal that the suppression of dynamically irrelevant stimuli is achieved by activity-silent, sub-threshold dynamics. Our results provide a normative explanation as to why PFC implements an adaptive, minimal representational strategy.

neuroscience↗

bioRxiv2023

Spatial uncertainty and environmental geometry in navigation

Knowing ones place in the world requires integrating sensory inputs with respect to the geometry of the environment. Consistent with this, variations in environmental geometry, such as the shape and size of an enclosure, have profound effects on navigational behavior1-4 and its neural underpinning in grid cells5-7. Here, we show that these effects arise as a consequence of a single, unifying principle: to navigate efficiently, the brain must maintain and update the uncertainty about ones location. We develop an image-computable Bayesian ideal observer model of navigation, continually combining noisy visual and self-motion inputs, and a neural encoding model representing the spatial uncertainty computed by the ideal observer. Critically, we find that a key determinant of spatial uncertainty is the dimensionality reduction inherent in the retinal projection of the environment. Mathematical analysis and simulations show that spatial uncertainty accounts for a diverse range of sometimes paradoxical distortions in human homing behavior across trapezoidal, stretched, and compressed environments. Moreover, the neural encoding of this uncertainty accounts for observed changes in grid field size, anisotropy, rescaling, and boundary-dependent tethering under analogous geometric manipulations. Our results show that spatial uncertainty arising unavoidably during navigation is key to understanding navigational behavior and its neural underpinnings.

neuroscience↗

bioRxiv2022

Training stochastic stabilized supralinear networks by dynamics-neutral growth

There continues to be a trade-off between the biological realism and performance of neural networks. Contemporary deep learning techniques allow neural networks to be trained to perform challenging computations at (near) human-level, but these networks typically violate key biological constraints. More detailed models of biological neural networks can incorporate many of these constraints but typically suffer from subpar performance and trainability. Here, we narrow this gap by developing an effective method for training a canonical model of cortical neural circuits, the stabilized supralinear network (SSN), that in previous work had to be constructed manually or trained with undue constraints. SSNs are particularly challenging to train for the same reasons that make them biologically realistic: they are characterized by strongly-connected excitatory cells and expansive firing rate non-linearities that together make them prone to dynamical instabilities unless stabilized by appropriately tuned recurrent inhibition. Our method avoids such instabilities by initializing a small network and gradually increasing network size via the dynamics-neutral addition of neurons during training. We first show how SSNs can be trained to perform typical machine learning tasks by training an SSN on MNIST classification. We then demonstrate the effectiveness of our method by training an SSN on the challenging task of performing amortized Markov chain Monte Carlo-based inference under a Gaussian scale mixture generative model of natural image patches with a rich and diverse set of basis functions - something that was not possible with previous methods. These results open the way to training realistic cortical-like neural networks on challenging tasks at scale.

neuroscience↗

bioRxiv2022

Beyond the Euclidean brain: inferring non-Euclidean latent trajectories from spike trains

Neuroscience faces a growing need for scalable data analysis methods that reduce the dimensionality of population recordings yet retain key aspects of the computation or behaviour. To extract interpretable latent trajectories from neural data, it is critical to embrace the inherent topology of the features of interest: head direction evolves on a ring or torus, 3D body rotations on the special orthogonal group, and navigation is best described in the intrinsic coordinates of the environment. Accordingly, we recently proposed the manifold Gaussian process latent variable model (mGPLVM) to simultaneously infer latent representations on non-Euclidean manifolds and how neurons are tuned to these representations. This probabilistic method generalizes previous Euclidean models and allows principled selection between candidate latent topologies. While powerful, mGPLVM makes two unjustified approximations that limit its practical applicability to neural datasets. First, consecutive latent states are assumed independent a priori, whereas behaviour is continuous in time. Second, its Gaussian noise model is inappropriate for positive integer spike counts. Previous work in Euclidean LVMs such as GPFA has shown significant improvements in performance when modeling such features appropriately (Jensen et al., 2021). Here, we extend mGPLVM by incorporating temporally continuous priors over latent states and flexible count-based noise models. This improves inference on synthetic data, avoiding negative spike count predictions and discontinuous jumps in latent trajectories. On real data, we also mitigate these pathologies while improving model fit compared to the original mGPLVM formulation. In summary, our extended mGPLVM provides a widely applicable tool for inferring (non-)Euclidean neural representations from large-scale, heterogeneous population recordings. We provide an efficient implementation in python, relying on recent advances in approximate inference to e.g. fit 10,000 time bins of recording for 100 neurons in five minutes on a single GPU.

neuroscience↗

bioRxiv2021

Optimal information loading into working memory in prefrontal cortex

Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the persistent activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a novel, theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics, and reveals a normative principle underlying dynamic coding.

neuroscience↗

bioRxiv2021

A universal probabilistic spike count model reveals ongoing modulation of neural variability

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work has demonstrated that this variability has predictable structure, can be modulated by sensory input and behaviour, and bears critical signatures of the underlying network dynamics and computations. However, current methods for characterising neural variability are primarily geared towards sensory coding in the laboratory: they require trials with repeatable experimental stimuli and behavioural covariates. In addition, they make strong assumptions about the parametric form of variability, rely on assumption-free but data-inefficient histogram-based approaches, or are altogether ill-suited for capturing variability modulation by covariates. Here we present a universal probabilistic spike count model that eliminates these shortcomings. Our method builds on sparse Gaussian processes and can model arbitrary spike count distributions (SCDs) with flexible dependence on observed as well as latent covariates, using scalable variational inference to jointly infer the covariate-to-SCD mappings and latent trajectories in a data efficient way. Without requiring repeatable trials, it can flexibly capture covariate-dependent joint SCDs, and provide interpretable latent causes underlying the statistical dependencies between neurons. We apply the model to recordings from a canonical non-sensory neural population: head direction cells in the mouse. We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations. Our approach paves the way to understanding the mechanisms and computations underlying neural variability under naturalistic conditions, beyond the realm of sensory coding with repeatable stimuli.

neuroscience↗

bioRxiv2020

Contextual inference underlies the learning of sensorimotor repertoires

Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation - contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. These results suggest that contextual inference is the key principle underlying how a diverse set of experiences is reflected in motor behavior.

neuroscience↗

bioRxiv2019

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots, and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory-inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization, as well as stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset, and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of awake monkey recordings. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function--fast sampling-based inference--and predict further properties of these motifs that can be tested in future experiments.

neuroscience↗

bioRxiv2017

Global, multiplexed dendritic computations under in vivo-like conditions

Dendrites integrate inputs in highly non-linear ways, but it is unclear how these non-linearities contribute to the overall input-output transformation of single neurons. Here, we developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex spatio-temporal synaptic input patterns. We used the hLN to predict the membrane potential of a detailed biophysical model of a L2/3 pyramidal cell receiving in vivo-like synaptic input and reproducing in vivo dendritic recordings. We found that more than 90% of the somatic response could be captured by linear integration followed a single global non-linearity. Multiplexing inputs into parallel processing channels could improve prediction accuracy by as much as additional layers of local non-linearities. These results provide a data-driven characterisation of a key building block of cortical circuit computations: dendritic integration and the input-output transformation of single neurons during in vivo-like conditions.

neuroscience↗

bioRxiv2016

Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex

Variability and correlations in cortical activity are ubiquitously modulated by stimuli. Correlated variability is quenched following stimulus onset across multiple cortical areas, suppressing low-frequency components of the LFP and of Vm-LFP coherence. Modulation of Fano factors and correlations in area MT is tuned for stimulus direction. What circuit mechanisms underly these behaviors? We show that a simple model circuit, the stochastic Stabilized Supralinear Network (SSN), robustly explains these results. Stimuli modulate variability by modifying two forms of effective connectivity between activity patterns that characterize excitatory-inhibitory (E/I) circuits. Increases in the strength with which activity patterns inhibit themselves reduce correlated variability, while increases in feedforward connections between patterns (transforming E/I imbalance into balanced fluctuations) increase variability. These results suggest an operating regime of cortical dynamics that involves fast fluctuations and fast responses to stimulus changes, unlike previous models of variability suppression through suppression of chaos or networks with multiple attractors.

neuroscience↗