Biology subjects

Chavlis, S.

Publications and source records attributed to Chavlis, S..

9 recordsLinked to original sources

bioRxiv2026

Dendritic hotspots support switching between competing rules without expanding the cortical engram

Adaptive behavior requires updating responses when contingencies change while preserving prior associations and the capacity to learn a new. How this trade-off is resolved remains unknown. Here, we combined in vivo imaging of apical tuft spines in the secondary motor cortex (M2) with biologically constrained network modeling in mice performing a cross-modal rule-switch task. M2 inactivation impaired rule-switching but not learning or maintenance, identifying it as a conflict resolution substrate. Adaptation was accompanied by elevated spine turnover concentrated within stable dendritic hotspots, in which the formation, elimination and clustering of new spines were coupled and pre-existing spines were lost early. A network model reproduces these dynamics and predicts that dendritic hotspots are critical for resource-efficient adaptation. Within these reusable domains, spines encoding the prior rule are replaced by newly-relevant ones via sharing of plasticity-related resources. Preventing reuse increases both the plasticity and the engram size requirements to encode the two rules. We propose that dendritic hotspots provide a mechanistic substrate for efficient adaptive learning.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

DendroTweaks: An interactive approach for unraveling dendritic dynamics

Neurons rely on the interplay between two critical components, dendritic morphology and ion channels, to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks, a toolbox designed to make detailed biophysical models with active dendrites more intuitive and more interactive. DendroTweaks features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, DendroTweaks focuses on subcellular properties, such as kinetics and distribution of ion channels, as well as the dynamics and placement of synaptic inputs. The toolbox supports various experimental protocols designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output, thereby enhancing model validation. It helps users build high-level, modular model representations and includes a rich set of tools for parsing, generating, and standardizing commonly used neuronal data formats. Finally, it enables model simplification through a built-in morphology reduction algorithm, allowing users to export models for further use in faster, more interpretable networks. By combining extensive visualization capabilities and comprehensive data management functionality, DendroTweaks introduces a novel interactive approach for unraveling dendritic dynamics. This approach will accelerate research on dendritic computations, their underlying mechanisms, and their fundamental role in brain function.

neuroscience↗

bioRxiv2023

Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks

While artificial machine learning systems achieve superhuman performance in specific tasks such as language processing, image and video recognition, they do so use extremely large datasets and huge amounts of power. On the other hand, the brain remains superior in several cognitively challenging tasks while operating with the energy of a small lightbulb. We use a biologically constrained spiking neural network model to explore how the neural tissue achieves such high efficiency and assess its learning capacity on discrimination tasks. We found that synaptic turnover, a form of structural plasticity, which is the ability of the brain to form and eliminate synapses continuously, increases both the speed and the performance of our network on all tasks tested. Moreover, it allows accurate learning using a smaller number of examples. Importantly, these improvements are most significant under conditions of resource scarcity, such as when the number of trainable parameters is halved and when the task difficulty is increased. Our findings provide new insights into the mechanisms that underlie efficient learning in the brain and can inspire the development of more efficient and flexible machine learning algorithms.

neuroscience↗

bioRxiv2022

Dendrify: a new framework for seamless incorporation of dendrites in Spiking Neural Networks

Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.

neuroscience↗

bioRxiv2022

Lateral entorhinal cortex inputs modulate hippocampal dendritic excitability by recruiting a local disinhibitory microcircuit

The lateral entorhinal cortex (LEC) provides information about multi-sensory environmental cues to the hippocampus through direct inputs to the distal dendrites of CA1 pyramidal neurons. A growing body of work suggests that LEC neurons perform important functions for episodic memory processing, coding for contextually-salient elements of an environment or the experience within it. However, we know little about the functional circuit interactions between LEC and the hippocampus. In this study, we combine functional circuit mapping and computational modeling to examine how long-range glutamatergic LEC projections modulate compartment-specific excitation-inhibition dynamics in hippocampal area CA1. We demonstrate that glutamatergic LEC inputs can drive local dendritic spikes in CA1 pyramidal neurons, aided by the recruitment of a disinhibitory vasoactive intestinal peptide (VIP)-expressing inhibitory neuron microcircuit. Our circuit mapping further reveals that, in parallel, LEC also recruits cholecystokinin (CCK)-expressing inhibitory neurons, which our model predicts act as a strong suppressor of dendritic spikes. These results provide new insight into a cortically-driven GABAergic microcircuit mechanism that gates non-linear dendritic computations, which may support compartment-specific coding of multi-sensory contextual features within the hippocampus. HIGHLIGHTSO_LISlice electrophysiology experiments investigate how lateral entorhinal cortex influences hippocampal area CA1 C_LIO_LILEC drives local spikes in distal dendrites but not in somata of CA1 pyramidal neurons C_LIO_LILEC inputs recruit VIP IN and CCK IN populations in CA1, but not SST INs C_LIO_LIComputational modeling and circuit manipulation experiments identify a VIP IN-mediated disinhibitory microcircuit for gating local dendritic spike generation C_LI IN BRIEFBilash et al. found that a distal cortical input is capable of driving local dendritic spikes in hippocampal pyramidal neurons. This dendritic spike generation is promoted by cortical recruitment of a local VIP interneuron-mediated disinhibitory microcircuit. Their results highlight new circuit mechanisms by which dynamic interaction of excitation, inhibition, and disinhibition support supralinear single-cell computations.

neuroscience↗

bioRxiv2020

The dendritic spatial code: branch-specific place tuning and its experience-dependent decoupling

Dendrites of pyramidal neurons integrate different sensory inputs, and non-linear dendritic computations drive feature selective tuning and plasticity. Yet little is known about how dendrites themselves represent the environment, the degree to which they are coupled to their soma, and how that coupling is sculpted with experience. In order to answer these questions, we developed a novel preparation in which we image soma and connected dendrites in a single plane across days using in vivo two-photon microscopy. Using this preparation, we monitored spatially tuned activity in area CA3 of the hippocampus in head-fixed mice running on a linear track. We identified "place dendrites", which can stably and precisely represent both familiar and novel spatial environments. Dendrites could display place tuning independent of their connected soma and even their sister dendritic branches, the first evidence for branch-specific tuning in the hippocampus. In a familiar environment, spatially tuned somata were more decoupled from their dendrites as compared to non-tuned somata. This relationship was absent in a novel environment, suggesting an experience dependent selective gating of dendritic spatial inputs. We then built a data-driven multicompartment computational model that could capture the experimentally observed correlations. Our model predicts that place cells exhibiting branch-specific tuning have more flexible place fields, while neurons with homogenous or co-tuned dendritic branches have higher place field stability. These findings demonstrate that spatial representation is organized in a branch-specific manner within dendrites of hippocampal pyramidal cells. Further, spatial inputs from dendrites to soma are selectively and dynamically gated in an experience-dependent manner, endowing both flexibility and stability to the cognitive map of space. One sentence summaryHippocampal pyramidal cells show branch-specific tuning for different place fields, and their coupling to their soma changes with experience of an environment.

neuroscience↗

bioRxiv2018

Evolutionary Models of Amino Acid Substitutions Based on the Tertiary Structure of their Neighborhoods

Intra-protein residual vicinities depend on the involved amino acids. Energetically favorable vicinities (or interactions) have been preserved during evolution, while unfavorable vicinities have been eliminated. We describe, statistically, the interactions between amino acids using resolved protein structures. Based on the frequency of amino acid interactions, we have devised an amino acid substitution model that implements the following idea: amino acids that have similar neighbors in the protein tertiary structure can replace each other, while substitution is more difficult between amino acids that prefer different spatial neighbors. Using known tertiary structures for -helical membrane (HM) proteins, we build evolutionary substitution matrices. We constructed maximum likelihood phylogenies using our amino acid substitution matrices and compared them to widely-used methods. Our results suggest that amino acid substitutions are associated with the spatial neighborhoods of amino acid residuals, providing, therefore, insights into the amino acid substitution process.

evolutionary biology↗

bioRxiv2018

Breakdown of spatial coding and neural synchronization in epilepsy

Temporal lobe epilepsy causes significant cognitive deficits in both human patients and rodent models, yet the specific circuit mechanisms that alter cognitive processes remain unknown. There is dramatic and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models, but the functional consequences of these changes on information processing at the neuronal population level have not been well characterized. To examine spatial representations of epileptic and control mice, we developed a novel wire-free miniature microscope to allow for unconstrained behavior during in vivo calcium imaging of neuronal activity. We found that epileptic mice running on a linear track had severely impaired spatial processing in CA1 within a single session, as place cells were less precise and less stable, and population coding was impaired. Long-term stability of place cells was also compromised as place cells in epileptic mice were highly unstable across short time intervals and completely remapped across a week. Because of the large-scale reorganization of inhibitory circuits in epilepsy, we hypothesized that degraded spatial representations were caused by dysfunctional inhibition. To test this hypothesis, we examined the temporal dynamics of hippocampal interneurons using silicon probes to simultaneously record from CA1 and dentate gyrus during head-fixed virtual navigation. We found that epileptic mice had a profound reduction in theta coherence between the dentate gyrus and CA1 regions and altered interneuron synchronization. In particular, dentate interneurons of epileptic mice had altered phase preferences to ongoing theta oscillations, which decorrelated inhibitory population firing between CA1 and dentate gyrus. To assess the specific contribution of desynchronization on spatial coding, we built a CA1 network model to simulate hippocampal desynchronization. Critically, we found that desynchronized inputs reduced the information content and stability of CA1 neurons, consistent with the experimental data. Together, these results demonstrate that temporally precise intra-hippocampal communication is critical for forming the spatial code and that desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice.

neuroscience↗