Biology subjects

Poirazi, P.

Publications and source records attributed to Poirazi, P..

18 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↗

bioRxiv2026

A unified law for inhibitory control in active dendrites

Neuronal computation depends on the balance between excitation and inhibition, yet how this balance is implemented across the dendritic tree remains unclear. Classical views predict that inhibition should be most effective near the soma or along the path from excitation to output, but many interneuron subtypes preferentially target remote dendritic compartments. This apparent paradox is sharpened by active dendrites, where local NMDA spikes, calcium plateaus and backpropagating action potentials can make distal branches powerful contributors to somatic firing. Here we develop an analytical framework that extracts general principles of inhibition from biophysically detailed multi-compartment simulations. By reformulating the implicit voltage update of detailed neuron models as a matrix recursion, we derive exact voltage sensitivities to inhibitory synaptic perturbations. This leads to a unified {Phi}-a law: the somatic impact of inhibition factorizes into a global dendritic susceptibility term and a local synaptic perturbation term. Using this law to map inhibitory leverage and identify optimal inhibitory interventions, we show that active dendritic excitation can shift inhibitory hot zones from perisomatic regions toward distal or intermediate compartments. Across neocortical, hippocampal and striatal neuron models, the same response law explains convergent inhibitory strategies despite distinct cellular mechanisms. Our framework turns detailed numerical simulation into analytical theory, providing a general principle for how diverse dendritic inhibition controls active neurons.

neuroscience↗

bioRxiv2025

Contextual modulation and blunted defensive responses to predators in head-fixed and freely moving mice

Behavioral responses to threat -- such as fleeing, freezing, or fighting--can be innate, learned, and strongly shaped by context or competing goals. Here, we asked whether exposure to an ecologically relevant predator obligatorily elicits canonical defensive behaviors across behavioral contexts. We examined predator responses in mice across four experimental conditions: one novel head-fixed reward-driven foraging task and three established paradigms in freely moving animals. In the head-fixed condition, water-deprived mice were trained to walk on a treadmill controlling a virtual environment and water reward delivery and were subsequently exposed to a live rat positioned above the lick spout. Despite the presence of the predator, most mice (5 of 7) maintained foraging performance at baseline levels. However, individual mice exhibited significant, coordinated changes in running speed, pupil diameter, eye movements, and posture, indicating engagement with the threat. To assess how context influences predator responses, we exposed 36 naive, freely moving mice to fear-inducing stimuli, including looming visual cues, rat odor, and a live rat. Even under these conditions, defensive behaviors were variable: only a subset of mice displayed avoidance or escape, and when presented with a freely moving rat, approximately half of the mice avoided the predator. Together, these findings show that predator threat does not elicit a uniform or obligatory defensive repertoire in mice. Instead, defensive responses are expressed flexibly, and are shaped by environmental constraints, task demands, and individual variability. These results challenge the assumption that innate fear behaviors are automatically triggered by predator encounters. Graphical AbstractUsing a head-fixed, reward-based foraging task and complementary freely moving paradigms, we show that mouse responses to predator-related stimuli are variable and context dependent, with limited expression of canonical defensive behaviors such as freezing or flight. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/658679v3_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@a05a31org.highwire.dtl.DTLVardef@1815084org.highwire.dtl.DTLVardef@cd3a11org.highwire.dtl.DTLVardef@19f5d95_HPS_FORMAT_FIGEXP M_FIG C_FIG

animal behavior and cognition↗

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↗

bioRxiv2025

Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures

Microstimulation is a powerful tool for causal interrogation of neural circuits and for therapeutic neuromodulation. However, predicting network-level responses to focal perturbations remains a major challenge. To address this problem in a tractable manner, we combine experiments on networks of hippocampal neurons cultured on high-density multielectrode arrays with spiking network model simulations. To characterize spontaneous and stimulation-evoked network dynamics, we employ a combination of direct electrophysiological readouts and information-theoretic measures. We find that single-channel stimulation reliably evokes a small set of site-specific, stereotyped network activity patterns. Remarkably, effective connectivity inferred from spontaneous activity captures the spatial organization of perturbation responses, enabling reliable ranking of stimulation-evoked effects across the network. Our spiking network model reproduces these observations and reveals the interplay between short-term synaptic depression and distance-dependent excitatory and inhibitory connectivity in shaping both spontaneous and evoked interactions at different effective scales. Spontaneous activity involves local structural routes, while perturbation-evoked responses engage comparatively longer, polysynaptic pathways. By unifying in silico modeling with experimental measurements, this work links spontaneous network structure to stimulation-evoked dynamics, suggesting that spontaneous effective connectivity may serve as a tractable proxy for stimulation targeting in recurrent circuits, with potential implications for the rational design of neuromodulatory interventions.

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↗

bioRxiv2024

A dendritic substrate for temporal diversity of cortical inhibition

In the mammalian neocortex, GABAergic interneurons (INs) inhibit cortical networks in profoundly different ways. The extent to which this depends on how different INs process excitatory signals along their dendrites is poorly understood. Here, we reveal that the functional specialization of two major populations of cortical INs is determined by the unique association of different dendritic integration modes with distinct synaptic organization motifs. We found that somatostatin (SST)-INs exhibit NMDAR-dependent dendritic integration and uniform synapse density along the dendritic tree. In contrast, dendrites of parvalbumin (PV)-INs exhibit passive synaptic integration coupled with proximally enriched synaptic distributions. Theoretical analysis shows that these two dendritic configurations result in different strategies to optimize synaptic efficacy in thin dendritic structures. Yet, the two configurations lead to distinct temporal engagement of each IN during network activity. We confirmed these predictions with in vivo recordings of IN activity in the visual cortex of awake mice, revealing a rapid and linear recruitment of PV-INs as opposed to a long-lasting integrative activation of SST-INs. Our work reveals the existence of distinct dendritic strategies that confer distinct temporal representations for the two major classes of neocortical INs and thus dynamics of inhibition.

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

A Tale of Two Trees: Modeling Apical and Basal Tree Contribution to L2/3 V1 Pyramidal Cell Orientation Selectivity

Pyramidal neurons, a mainstay of cortical regions, receive a plethora of inputs from various areas onto their morphologically distinct apical and basal trees. Both trees differentially contribute to the somatic response, defining distinct anatomical and possibly functional sub-units. To elucidate the contribution of each tree to the encoding of visual stimuli at the somatic level, we modeled the response pattern of a L2/3 V1 pyramidal neuron to orientation tuned synaptic input. Towards this goal, we used a morphologically detailed computational model of a single cell that replicates electrophysiological and two-photon imaging data. Our simulations predict a synergistic effect of apical and basal trees on somatic action potential generation: basal tree activity, in the form of either depolarization or dendritic spiking, is necessary for producing somatic activity, despite the fact that most somatic spikes are heavily driven by apical dendritic spikes. This model provides evidence for synergistic computations taking place in the basal and apical trees of the L2/3 V1 neuron along with mechanistic explanations for tree-specific contributions and emphasizes the potential role of predictive and attentional feedback input in these cells. O_FIG O_LINKSMALLFIG WIDTH=181 HEIGHT=200 SRC="FIGDIR/small/501858v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1b69239org.highwire.dtl.DTLVardef@13a0a32org.highwire.dtl.DTLVardef@1738a7corg.highwire.dtl.DTLVardef@88e826_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract FigureC_FLOATNO Graphical Representation of AP Generation in a L2/3 V1 Pyramidal Neuron Model Action potential generation requires the spatiotemporal coincidence of apical sodium spikes with either basal sodium spikes or significant basal depolarizations, allowing the neuron to respond to salient stimuli that may or may not be affected by attentional and/or predictive signals from higher-order areas. C_FIG

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↗

bioRxiv2021

Co-allocation to overlapping dendritic branches in the retrosplenial cortex integrates contextual memories across time

Events occurring close in time are often linked in memory, providing an episodic timeline and a framework for those memories. Recent studies suggest that memories acquired close in time are encoded by overlapping neuronal ensembles, but the role of dendritic plasticity mechanisms in linking memories is unknown. Using activity-dependent labeling and manipulation approaches, longitudinal one- and two-photon imaging of somatic and dendritic compartments, and computational modeling, we show that memory linking is not only dependent on ensemble overlap in the retrosplenial cortex, but also on branch-specific dendritic allocation mechanisms. The same dendritic segments are preferentially activated by two linked memories, and spine clusters added after each of the two linked memories are allocated to the same dendritic segments. Our results demonstrate a causal mechanistic role for dendritic plasticity in memory integration and reveal a novel set of rules that govern how linked and independent memories are allocated to dendritic compartments.

neuroscience↗

bioRxiv2021

A Locus Coeruleus- dorsal CA1 dopaminergic circuit modulates memory linking

Individual memories are often linked so that the recall of one triggers the recall of another. For example, contextual memories acquired close in time can be linked, and this is known to depend on temporary increase in excitability that drive the overlap between dorsal CA1 (dCA1) hippocampal ensembles encoding the linked memories. Here, we show that the Locus Coeruleus (LC) cells projecting to dCA1 have a key permissive role in contextual memory linking, without affecting contextual memory formation, and that this effect is mediated by dopamine and not by noradrenaline. Additionally, we found that LC to dCA1 projecting neurons modulate the excitability of dCA1 neurons, and the extent of overlap between dCA1 memory ensembles, as well as the stability of coactivity patterns within these ensembles. This discovery of a neuromodulatory system that specifically affects memory linking without affecting memory formation, reveals a fundamental separation between the brain mechanisms that modulate these two distinct processes.

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↗

bioRxiv2019

Structured connectivity exploits NMDA non-linearitites to induce dimensionally diverse responses in a PFC circuit

Prefrontal Cortex (PFC) exerts control on action selection and mediates behavioral flexibility during working memory (WM) task execution, when integration and retention of sensory information takes place. We used biophysical circuit modelling to investigate the dendritic, neuronal and circuit mechanisms that underlie these computations, aiming to causally link these three processing levels. Our simulations predict that dendritic NMDA non-linearities drive distinct activity dynamics of the same network, thus enabling adaptive coding in the absence of plasticity mechanisms. Specifically, we find that distinct assemblies of mixed-selectivity neurons emerge and fire in stable trajectories in a stimulus-dependent manner. Synaptic inputs that are spatio-temporally clustered, as provided by the structured connectivity of the PFC, facilitate these activity dynamics, thus further increasing the flexibility of the network. Our study suggests that behavioral flexibility may result from the formation of memoranda-specific assemblies in the PFC which are utilized dynamically in relation to the task at hand.

neuroscience↗

bioRxiv2019

Contribution of Apical and Basal Dendrites of L2/3 Pyramidal Neurons to Orientation Encoding in Mouse V1

Pyramidal neurons integrate synaptic inputs from basal and apical dendrites to generate stimulus-specific responses. It has been proposed that feed-forward inputs to basal dendrites drive a neurons stimulus preference, while feedback inputs to apical dendrites sharpen selectivity. However, how a neurons dendritic domains relate to its functional selectivity has not been demonstrated experimentally. We performed 2-photon dendritic micro-dissection on layer-2/3 pyramidal neurons in mouse primary visual cortex. We found that removing the apical dendritic tuft did not alter orientation-tuning. Furthermore, orientation-tuning curves were remarkably robust to the removal of basal dendrites: ablation of 2-3 basal dendrites was needed to cause a small shift in orientation preference, without significantly altering tuning width. Computational modeling corroborated our results and put limits on how orientation preferences among basal dendrites differ in order to reproduce the post-ablation data. In conclusion, neuronal orientation-tuning appears remarkably robust to loss of dendritic input.

neuroscience↗

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↗

bioRxiv2018

Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators

Interneurons are critical for the proper functioning of neural circuits and are typically considered to act as linear point neurons. However, exciting new findings reveal complex, sub- and/or supralinear computations in the dendrites of various interneuron types. These findings challenge the point neuron dogma and call for a new theory of interneuron arithmetic. Using detailed, biophysically constrained models, we predict that dendrites of FS basket cells in both the hippocampus and mPFC come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be explained by the point neuron reduction. Instead, a 2-stage Artificial Neural Network (ANN), with both sub- and supralinear hidden nodes, captures the variance. We propose that FS basket cells have substantially expanded computational capabilities sub-served by their non-linear dendrites and act as a 2-layer ANN.

neuroscience↗