Biology subjects

Narayanan, R.

Publications and source records attributed to Narayanan, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Plasticity degeneracy underlies flexible formation and reconfiguration of spatial representations in hippocampal granule cells

Learning and memory require neural representations that are simultaneously robust and adaptable. How do neural systems reconcile these opposing demands of stability and flexibility? Here, we use hippocampal granule cells, which transform spatially diffuse entorhinal inputs into sparse sharply-tuned spatial representations, to address this fundamental question using emergence, stabilization, remapping, and suppression of place-fields as ideal systems to study stability-flexibility balance. Employing an unbiased population-based approach involving different strengths of plasticity in several components, we show that sharply tuned place-cell firing does not arise from a unique plasticity mechanism. We employed a population of heterogeneous, experimentally constrained biophysical models of dentate gyrus granule cells receiving cortical grid-field and contextual inputs to demonstrate that spatially restricted synaptic potentiation is insufficient to selectively route spatial information. Instead, each of the four distinct target transitions, namely reliable emergence, stabilization, remapping, and suppression of place-fields, were achievable by coordinated plasticity spanning excitatory synapses and several intrinsic ion channels. Across each of these four transitions, we identify numerous mechanistically distinct plasticity combinations that generate equivalent functional outcomes. These solutions are non-random and highly diverse, exhibiting weak dependence among individual plasticity components despite converging onto constrained functional states, through a combination of targeted amplification and global suppression of spatial firing. Our findings reveal extensive plasticity degeneracy in hippocampal spatial coding and suggest that flexible neural representations emerge not from unique plasticity rules involving one single component, but from a repertoire of alternative plasticity routes that are capable of implementing the same computation.

neuroscience↗

bioRxiv2026

HCN channels enhance synchrony propagation in heterogeneous synfire chains

MotivationInformation flow and temporal coding in cortical circuits depend critically on the reliable transmission of precisely timed synchronous spike patterns. Although cortical assemblies achieve such transmission despite pronounced intrinsic heterogeneities and stochastic high-conductance states, the mechanisms underlying effective synchrony propagation under in vivo conditions remain poorly understood. MethodologyIn this study, we address this gap using large-scale, conductance-based models of excitatory and inhibitory neurons organized into feedforward synfire chains operating in noisy, high-conductance regimes. Using independent stochastic search algorithms, we first identified physiologically valid heterogeneous populations of cortical neurons. Both excitatory and inhibitory populations exhibited cellular-scale degeneracy, whereby distinct combinations of biophysically identified molecular components produced signature physiological characteristics. We then constructed synfire chains with varying degrees of heterogeneity using these populations and assessed the propagation of different spike packets across neuronal assemblies. ResultsWe found synchrony propagation to be inherently probabilistic, revealing a stochastic separatrix that separated input patterns that consistently succeeded from those that consistently failed in propagation. The stochastic nature of this separatrix highlighted a critical role for background synaptic fluctuations, defining a regime in which identical inputs alternately propagated or failed across trials solely due to stochastic background activity. Comparing networks with different degrees of intrinsic heterogeneity, we found that increasing heterogeneity did not alter mean propagation efficacy but reduced network-to-network variability, indicating a stabilizing role for intrinsic diversity. Strikingly, when we tested the impact of neuronal intrinsic properties on synchrony propagation, hyperpolarization-activated cyclic nucleotide-gated (HCN) channels emerged as robust enhancers of synchrony propagation across all heterogeneity regimes. Mechanistically, the slow restorative kinetics of HCN conductances narrowed the temporal window for spike initiation, sharpening output synchrony, and improving propagation reliability. This effect was abolished when HCN kinetics were accelerated, underscoring the importance of the slow negative feedback mediated by these channels. ImplicationsTogether, our analyses identify HCN channels as key regulators of synchronous information transfer and reveal strong interactions among intrinsic conductances, input characteristics, neuronal heterogeneity, and stochastic background activity in shaping cortical synchrony propagation. The ability of diverse cellular and network configurations to achieve similar propagation efficacy further highlights degeneracy as a fundamental principle governing robust and flexible neural computation.

neuroscience↗

bioRxiv2026

Spatial information transfer in recurrent place-cell networks depends on excitation-inhibition balance, neural-circuit heterogeneities, and trial-to-trial variability

A key challenge in understanding spatial navigation and memory is explaining how hippocampal networks sustain robust spatial information transfer despite pronounced trial-to-trial variability and pervasive neural-circuit heterogeneities. Although hippocampal heterogeneities and physiological variability are well-characterized, circuit-scale understanding of stable information transfer in recurrent place-cell networks remains limited. Here, we first show that even recurrent networks composed of intrinsically identical neurons and receiving identical place-field inputs express pronounced neuron-to-neuron variability in spatial tuning profiles, place-field widths, subthreshold ramp amplitudes, and spatial information transfer. Introduction of intrinsic within-type heterogeneities to excitatory and inhibitory neurons further increased diversity in firing properties, but strikingly improved robustness of spatial information transfer under high trial-to-trial variability. Although strengthening inhibition expectedly narrowed place fields and reduced firing across all networks, the impact of inhibition on information-transfer profiles was stronger in heterogeneous networks manifesting high degree of trial-to-trial variability. Across networks, increasing trial-to-trial variability reduced information transfer and shifted the spatial location of peak information from the high-slope regions of the tuning curve to its peak-firing location. Finally, incorporating afferent heterogeneities allowed neurons to be tuned to distinct place-field centers, reducing peak information values while amplifying neuron-to-neuron diversity in information-transfer profiles. Together, we demonstrate that excitation-inhibition balance, trial-to-trial variability, within-type heterogeneities, and afferent input diversity jointly regulate spatial information transfer in recurrent place-cell circuits. We also highlight intrinsic heterogeneities as substrates for enhanced robustness of spatial coding against perturbations. Importantly, the convergence of multiple disparate mechanisms in yielding similar information-transfer profiles underscores degeneracy as a fundamental organizing principle in neural-circuit physiology.

neuroscience↗

bioRxiv2026

Task-dependence of network-to-network variability in learning, performance, and dynamics of heterogeneous recurrent networks

Artificial recurrent networks are powerful models for studying neural dynamics and representations underlying complex cognitive tasks. However, the impact of neural-circuit heterogeneities on learning, dynamics, robustness, and generalization in these networks remains poorly understood. Here, we systematically investigated the impact of graded intrinsic heterogeneities in artificial recurrent networks trained on different cognitive tasks using reward- modulated Hebbian learning. Across networks trained with distinct hyperparameters and different levels of intrinsic heterogeneity, we observed pronounced network-to-network and task-to-task variability in training convergence, error dynamics during training, and task performance. These effects were strongly task dependent, with memory-dependent tasks exhibiting greater sensitivity to heterogeneity than memoryless tasks. We assessed these networks for robustness to multiple forms of graded post-training perturbations. Perturbations to intrinsic time constant distributions altered network dynamics, but had limited impact on final task accuracy in most cases. In contrast, perturbations to initial conditions, exploratory activity impulses, or task epoch durations strongly affected memory-dependent tasks. Among all perturbations, synaptic jitter was consistently the most detrimental, impairing performance across all tasks and heterogeneity levels. Importantly, despite such pronounced impact of heterogeneities, none of the metrics (spanning training, performance, dynamics, and robustness) varied monotonically with the level of training heterogeneity, instead showing additional dependencies on task demands, network configuration, and perturbation type. Finally, networks trained on a single task were able to perform structurally related untrained tasks, but failed on fundamentally distinct tasks. Strikingly, similar task performances emerged from divergent activity trajectories across networks and training conditions, together revealing pronounced functional degeneracy in network dynamics. Collectively, our findings establish that heterogeneous recurrent networks operate in a complex systems regime, where robust function emerges from non-unique, task-specific interactions among hyperparameters, dynamics, and heterogeneities. Our analyses emphasize the need for population- of-networks approaches that focus on interactions among multiple forms of neural heterogeneities in shaping learning and computation.

neuroscience↗

bioRxiv2026

Intrinsic excitability of rat hippocampal granule cells increases along the dorsal-to-ventral axis

The dentate gyrus (DG) of the hippocampus exhibits striking anatomical and functional heterogeneity along its dorsoventral axis, yet the intrinsic electrophysiological diversity of its principal excitatory neurons, the granule cells, across the dorsoventral axis remains unexplored. Here, we systematically examined the electrophysiological properties of DG granule cells across the dorsal, intermediate, and ventral regions of the rat hippocampus. We found a progressive increase in input resistance, impedance amplitude, and firing rate of granule cells, accompanied by a gradual slowdown in repolarization kinetics of their action potentials along the dorsal-to-ventral axis. Our analyses demonstrated that granule cells acted as class I integrators that lacked strong resonance properties across the dorsoventral axis. We performed pairwise correlation and dimensionality reduction analyses to reveal weak dependencies across physiological measurements and the absence of distinct clusters for dorsal, intermediate, or ventral granule cells. Importantly, blade-specific analyses of granule cell physiology revealed that all measurements manifested pronounced heterogeneities even within a given dorsoventral section and a specific blade. Strikingly, ventral granule cells in the infrapyramidal blade manifested higher firing rates compared to their counterparts in the suprapyramidal blade. These blade-specific differences were limited to the ventral granule cells, with negligible distinctions between granule cells in the two blades of either dorsal or intermediate hippocampus. Together, our findings unveil a progressive increase in excitability of DG granule cells along the dorsal-to-ventral axis and a blade-specific granularity of firing properties, adding new dimensions to the several known anatomical, molecular, and behavioral differences across the hippocampal dorsoventral axis.

neuroscience↗

bioRxiv2025

Transcription factor LHX2 suppresses astrocyte proliferation in the postnatal mammalian cerebral cortex

In the developing cerebral cortex astrocytes arise from progenitors in the ventricular and subventricular zones (V-SVZ), and also from local proliferation within the parenchyma. In the mouse neocortex, astrocytes that occupy upper versus deep layers (UL/DL) are known to be distinct populations in terms of molecular and morphological features. Transcription factor LHX2 is expressed both in V-SVZ gliogenic progenitors and in differentiated astrocytes throughout development and into adulthood. Here we show that loss of Lhx2 at birth results in an increased astrocyte proliferation in UL but not the DL of the cortex in the first postnatal week. Consistent with this, transcriptomic signatures of UL astrocytes increase. By 3 months, Lhx2 mutant astrocytes display upregulation of GFAP, and transcriptomic signatures associated with "reactive" astrocytes, in the absence of injury. These results demonstrate a novel role for Lhx2 in regulating proliferation and molecular features of cortical astrocytes. Summary StatementLoss of Lhx2 causes increased astrocyte proliferation in the upper but not deep layers of the mammalian cortex, and upregulation of reactive gliosis-like signatures in the absence of injury.

neuroscience↗

bioRxiv2025

Degeneracy explains diversity in interneuronal regulation of pattern separation in heterogeneous dentate gyrus networks

Background and motivationsThe Marr-Albus theory postulates that pattern separation is realized by divergent feedforward excitatory connectivity. Yet, there are several lines of evidence for strong but differential regulation of pattern separation by local circuit connectivity, even when feedforward connectivity is divergent. What are the relative contributions of divergent feedforward connectivity and local circuit interactions to pattern separation? How do we reconcile the contrasting lines of evidence on local circuit regulation of pattern separation in circuits endowed with divergent feedforward connectivity? In this study, we quantitatively address these questions in a population of heterogeneous dentate gyrus (DG) networks, where we enforced feedforward connectivity to be identically divergent. MethodologyWe generated tens of thousands of random spiking neuronal models to arrive at thousands of non-repeating heterogeneous single-neuron models of four different DG neuronal subtypes, each satisfying their respective functional characteristics. We connected these heterogeneous populations of neurons with subtype proportions and local connectivity that reflected the DG microcircuit. In a second level of unbiased search, we generated 20,000 identical networks that differed from each other only in their synaptic weight values. Thus, within the Marr-Albus framework, these networks that were identical in terms of neuronal composition and divergent feedforward connectivity should all be capable of performing effective pattern separation. To test this, we fed these networks with morphed sets of input patterns, recorded granule cell outputs, and computed similarity metrics based on correlation measures across input or output patterns. We developed novel quantitative metrics for pattern separation from plots of output similarity vs. input similarity and validated each network with bounds on these metrics. ResultsDespite being identical in terms of divergent feedforward connectivity, we found only a very small proportion (47 of 20,000 or 0.23%) of the randomly generated networks to manifest effective pattern separation. We tested the specific contributions of interneurons by assessing pattern separation in all pattern-separating networks after individually deleting each of the three interneuron subtypes. Strikingly, we found pronounced network-to-network variability in how each interneuron subtype contributed to granule cell sparsity and pattern separation. We traced this variability to differences in local synaptic connectivity, which also resulted in network-to-network variability in firing rates and sparsity of different interneurons. Finally, we found heterogeneous DG networks to be more resilient to synaptic jitter compared to their homogeneous counterparts, with specific reference to pattern separation computed through average firing rate correlations. ImplicationsOur population-of-networks approach clearly shows that divergent connectivity of afferent inputs does not guarantee pattern separation in DG networks. Instead, we demonstrate strong yet variable roles for interneurons and local connectivity in implementing pattern separation. Importantly, our analyses unveil degeneracy in DG circuits, whereby similar pattern separation efficacy was achieved through disparate local-circuit interactions. These observations, alongside network-to-network variability in dependencies on different interneurons, strongly advocate the complex adaptive systems approach as a unifying framework to study DG pattern separation.

neuroscience↗

bioRxiv2025

Diversity in the impact of heterogeneities on recurrent networks performing a cognitive task

Background and motivationArtificial recurrent networks are widely used as models to study the complex dynamics underlying biological neural networks during execution of cognitive tasks. However, most studies assume individual units in the recurrent network to be homogeneous repeating units, whereas real neurons exhibit several forms of heterogeneities. In this study, we designed and employed a systematic framework for quantitative assessment of the impact of neural heterogeneities on recurrent networks that were trained to perform a cognitive task. MethodologyOur framework involved training of a population of recurrent networks, differing in terms of their hyperparameters, to perform a cognitive task in the presence of six graded levels of intrinsic heterogeneities. We tested the impact of heterogeneities on several performance metrics that encompassed training performance, task-execution dynamics, and resilience to different forms of post-training heterogeneities (also introduced at different levels). ResultsOur population-of-networks approach demonstrate that intrinsic heterogeneities impacted network performance and dynamics in diverse ways even if they were trained with the same training algorithm, convergence criteria, and task specifications. First, our analyses unveiled pronounced network-to-network variability in the dependence of training performance on the level of heterogeneity, in terms of the number of training trials required for learning and the error values associated with task performance. Second, the impact of training heterogeneities on network dynamics during task execution also manifested substantial variability across networks. Finally, our analyses revealed a prominent impact of different forms of post-training heterogeneities on performance errors and network dynamics. We observed progressive increases in errors as well as in trajectory deviations with graded increases in post-training heterogeneities. Importantly, we observed pronounced variability in how robustness to post-training heterogeneities depended on the level of training heterogeneities. Specifically, certain networks showed enhanced robustness to post-training heterogeneities when training heterogeneities were low, whereas others showed better robustness when training heterogeneities were high. ImplicationsThe striking nature of network-to-network variability observed in our analyses strongly advocates a complex systems viewpoint to study the impact of neural heterogeneities on circuit function. Within such a complex system framework, where several functionally specialized subsystems interact with each other in non-random ways to yield collective performance of the task, we argue that the emphasis should not be on heterogeneities in individual components of neural circuits. Instead, we emphasize the need to focus on the global structure of different forms and degrees of heterogeneities across different components and a systematic assessment of how they interact with each other towards adapting and achieving collective function.

neuroscience↗

bioRxiv2024

UBE3A reinstatement restores behavior and proteome in an Angelman Syndrome mouse model of Imprinting Defects

Angelman Syndrome (AS) is a severe neurodevelopmental disorder wionly symptomatic treatment currently available. Besides mutations within the UBE3A gene, AS is caused by deletions, imprinting center defects (mICD) or uniparental disomy of chromosome 15 (UPD). Current mouse models are Ube3a-centric and do not address expression changes of other 15q11-q13 genes on AS pathophysiology. Here, we studied a mouse line that harbors a mutation affecting the AS-PWS imprinting center, hence modeling mICD/UPD AS subtypes. mICD mice showed significant reduction in UBE3A protein, bi-allelic expression of Ube3a-ATS and Mkrn3-Snord115 gene cluster, leading to robust AS behavioral deficits and proteome alterations similar to Ube3aKO mice. Genetic UBE3A overexpression in mICD mice, mimicking therapeutic strategies that effectively activate the biallelic silenced Ube3a gene, resulted in a complete rescue of all behavioral and proteome alterations. Subsequently, treatment with an antisense oligonucleotide (ASO) to directly activate the biallelic silenced Ube3a gene in mICD mice also resulted in efficient reinstatement of UBE3A, alongside a partial rescue of behavioral phenotypes. Taken together, these findings demonstrate that UBE3A loss is the primary factor underlying AS phenotypes in the mICD/UPD mouse model, and also corroborate that UBE3A reinstatement is an attractive therapeutic strategy for mICD/UPD AS individuals.

neuroscience↗

bioRxiv2024

Dichotomous impact of afferent sensory noise on grid-patterned firing and path integration in a continuous attractor network model

The continuous attractor network (CAN) model has been effective in explaining grid-patterned firing in the rodent medial entorhinal cortex, with strong lines of experimental evidence and widespread utilities in understanding path integration. A surprising lacuna in CAN analyses is the lack of quantitative studies assessing the impact of sensory noise in the velocity inputs on the emergence of grid-patterned activity and path integration. In addressing this, we employed an established 2D CAN model which received velocity inputs from a virtual animal traversing a 2D arena to generate grid fields. We introduced different levels of Gaussian noise to the afferent velocity inputs to the model and assessed its impact on the network functions in generating grid fields and performing path-integration. We computed a position estimate at each time step using the network activity and quantified position accuracy using the difference between the real and estimated positions. We performed all simulations using several trajectories for the virtual animal, as computed grid scores and position accuracy showed pronounced trajectory-to-trajectory variability even in noise-free cases. We found that the presence of low levels of sensory noise was beneficial to the generation of grid fields, specifically with trajectories where there was no grid-patterned activity in a no-noise scenario. With trajectories where there was grid-patterned activity in the absence of noise, low levels of noise improved position estimation accuracy. In contrast, high levels of sensory noise impaired position estimates as well as grid-patterned activity, although position estimates were more sensitive to sensory noise compared to grid-patterned activity. Together, these analyses demonstrate the manifestation of stochastic resonance in a 2D CAN model, where low levels of sensory noise were beneficial towards the emergence of grid-patterned firing and in tracking position. Next, motivated by the proposed role of border-cells as an error-correction mechanism, we introduced north and east border cells and connected them to grid cells based on co-activity patterns. For different levels of noise, we computed grid scores and position accuracy in the presence vs. absence of border cells. Interestingly, while border inputs led to grid field formation in cases where grid fields were not generated, their presence only had a marginal impact on position accuracy. Together, our analyses suggest that biological CANs could evolve to yield optimal performance in the presence of noise in biological sensory systems, serving as a stabilizing factor yielding functional robustness through the manifestation of stochastic resonance.

neuroscience↗

bioRxiv2024

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are multifactorial diseases, requiring personalized assessment and treatment. The advancements in multi-omics technologies, namely RNA-seq and whole genome sequencing, have offered translational researchers a comprehensive view of the human genome; utilizing this data, we can reveal novel biomarkers and segment patient populations based on personalized risk factors. Limitations in these technologies in failing to capture disease complexity can be accounted for by using an integrated approach, characterizing variants alongside expression related to emerging phenotypes. Designed and implemented data analytics methodology is based on a nexus of orthodox bioinformatics, classical statistics, and multimodal artificial intelligence and machine learning techniques. Our approach has the potential to reveal the intricate mechanisms of CVD that can facilitate patient-specific disease risk and response profiling. We sourced transcriptomic expression and variants from CVD and control subjects. By integrating these multi-omics datasets with clinical demographics, we generated patient-specific profiles. Utilizing a robust feature selection approach, we reported a signature of 27 transcripts and variants efficient at predicting CVD. Here, differential expression analysis and minimum redundancy maximum relevance feature selection elucidated biomarkers explanatory of the disease phenotype. We used Combination Annotation Dependent Depletion and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVDs. Overall, we observed an XGBoost model hyperparameterized using Bayesian optimization perform the best (AUC 1.0). Using SHapley Additive exPlanations, we compiled risk assessments for patients capable of further contextualizing these predictions in a clinical setting. We discovered a 27-component signature explanatory of phenotypic differences in CVD patients and healthy controls using a feature selection approach prioritizing both biological relevance and efficiency in machine learning. Literature review revealed previous CVD associations in a majority of these diagnostic biomarkers. Classification models trained on this signature were able to predict CVD in patients with high accuracy. Here, we propose a framework generalizable to other diseases and disorders.

genomics↗

bioRxiv2024

Comparative vitreous proteomic profiling of proliferative diabetic retinopathy and diabetic with no-retinopathy subjects implicates impaired autophagy in DR pathogenesis

PurposeDiabetic retinopathy (DR) is a neurovascular complication of diabetes (DM) causing the loss of neurons (ganglion cells) in the retina. The disease etiology and potential pathogenic mechanisms in this disease remains unclear. In the present study, we aimed to further understand the key and novel pathogenic mechanisms involved in DR pathogenesis by taking cues from our global proteomics data. MethodologyThe study was approved by the institutional review board (IRB) of LVPEI, Hyderabad, India. Vitreous humour samples (PDR; n=3, DM; n=3, Control; n=3) were collected from patients undergoing vitrectomy and subjected to LC-MS-MS analysis. The acquired raw data were searched against the human vitreous proteome and was further analysed by various bioinformatic and proteomic tools. Western blotting and IHC was performed to validate crucial pathways. Blood samples from patients (DM, PDR & NPDR) and controls (n=50); cadaveric retinas from diabetic and non-diabetic donors (n=10) and epiretinal membranes (ERM, n=10) from DR cases and controls were collected and RNA was isolated. Quantitative expression of genes involved in autophagy were performed. CT was compared across different categories and significance estimated using a student t-test. ResultA total of 1079 proteins were identified with 16 completely novel proteins in eye proteome. Top pathways identified were autophagy, inflammation, LXR/RXR activation (lipid metabolism), ROS generation by macrophages, apoptosis and protein degradation. Regulatory proteins identified were associated with cell death, phagocytic activation, angiogenesis and apoptosis. Autophagy inducers such as ROS was found to be accumulated in the DR vitreous. TREM2, microglial receptor was identified as a novel protein in PDR vitreous. The expression of TREM2, an autophagy-associated gene was significantly (p-value = 0.05) upregulated in all categories as compared to control (NDM and/or NDM/No-DR). TREM2 protein also seemed to colocalise with microglial marker F4/80 in retinal tissues and intense expression was observed near the blood vessels in case of PDR retina. Other autophagy-associated markers were also differentially regulated in DR as compared to controls. ConclusionThis study emphasises on the strong role of autophagy pathways and its associated genes in the development of DR.

genomics↗

bioRxiv2024

Dichotomy between extracellular signatures of active dendritic chemical synapses and gap junctions

Local field potentials (LFPs) are compound signals that represent the dynamic flow of information across the brain, which have been historically associated with chemical synaptic inputs. How do gap junctional inputs onto active compartments shape LFPs? We developed methodology to record extracellular potentials associated with different patterns of gap junctional inputs onto conductance-based models. We found that synchronous inputs through chemical synapses yielded a negative deflection in proximal extracellular electrodes, whereas those onto gap junctions manifested a positive deflection. Importantly, we observed extracellular dipoles only when inputs arrived through chemical synapses, but not with gap junctions. Remarkably, hyperpolarization-activation cyclic nucleotide-gated channels, which typically conduct inward currents, mediated outward currents triggered by the fast voltage transition caused by synchronous inputs. With rhythmic inputs at different frequencies arriving through gap junctions, we found strong suppression of LFP power at higher frequencies as well as frequency-dependent differences in the spike phase associated with the LFP, when compared to respective chemical synaptic counterparts. All observed differences in LFP were mediated by the relative dominance of synaptic currents vs. voltage-driven transmembrane currents with chemical synapses vs. gap junctions, respectively. Our analyses unveil a hitherto unknown role for active dendritic gap junctions in shaping extracellular potentials.

neuroscience↗

bioRxiv2024

Forestwalk: A machine learning workflow brings new insights into posture and balance in rodent beam walking

AbstractThe beam walk is widely used to study coordination and balance in rodents. While the task has ethological validity, the main endpoints of foot slip counts and time to cross are prone to human-rater variability and offer limited sensitivity and specificity. We asked if machine learning-based methods could reveal previously hidden, but biologically relevant, insights from the task. Marker-less pose estimation, using DeepLabCut, was deployed to label 13 anatomical points on mice traversing the beam. Next, we automated classical endpoint detection, including foot slips, with high recall (>90%) and precision (>80%). A total of 395 features were engineered and a random-forest classifier deployed that, together with skeletal visualizations, could test for group differences and identify determinant features. This workflow, named Forestwalk, uncovered pharmacological treatment effects in C57BL/6J mice, revealed phenotypes in transgenic mice used to study Angelman syndrome and SLC6A1-related neurodevelopmental disorder, and will facilitate a deeper understanding of how the brain controls balance in health and disease.

animal behavior and cognition↗

bioRxiv2024

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Complex systems are neither fully determined nor completely random. Biological complex systems, including single neurons, manifest intermediate regimes of randomness that recruit integration of specific combinations of functionally segregated subsystems. Such emergence of biological function provides the substrate for the expression of degeneracy, the ability of disparate combinations of subsystems to yield similar function. Here, we present evidence for the expression of degeneracy in morphologically realistic models of dentate gyrus granule cells (GC) through functional integration of disparate ion-channel combinations. We performed a 45-parameter randomized search spanning 16 active and passive ion channels, each biophysically constrained by their gating kinetics and localization profiles, to search for valid GC models. Valid models were those that satisfied 17 sub- and supra-threshold cellular-scale electrophysiological measurements from rat GCs. A vast majority (>99%) of the 15,000 random models were not electrophysiologically valid, demonstrating that arbitrarily random ion-channel combinations wouldnt yield GC functions. The 141 valid models (0.94% of 15,000) manifested heterogeneities in and cross-dependencies across local and propagating electrophysiological measurements, which matched with their respective biological counterparts. Importantly, these valid models were widespread throughout the parametric space and manifested weak cross-dependencies across different parameters. These observations together showed that GC physiology could neither be obtained by entirely random ion-channel combinations nor is there an entirely determined single parametric combination that satisfied all constraints. The complexity, the heterogeneities in measurement and parametric spaces, and degeneracy associated with GC physiology should be rigorously accounted for, while assessing GCs and their robustness under physiological and pathological conditions. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/580453v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1fe6e90org.highwire.dtl.DTLVardef@10cc473org.highwire.dtl.DTLVardef@135732dorg.highwire.dtl.DTLVardef@1caec0_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

LHX2 regulates dendritic morphogenesis in layer II/III of the neocortex via distinct pathways in progenitors and postmitotic neurons.

In the mammalian neocortex, excitatory neurons that send projections via the corpus callosum are critical to integrating information across the two brain hemispheres. The molecular mechanisms governing the development of the dendritic arbours and spines of these callosal neurons are poorly understood, yet these features are critical to their physiological properties. LIM Homeodomain 2 (Lhx2), a regulator of fundamental processes in cortical development, is expressed in postmitotic callosal neurons occupying layer II/III of the neocortex and also in their progenitors residing in the embryonic day (E) 15.5 ventricular zone of the mouse neocortex. We tested whether this factor is essential for dendritic arbour configuration and spine morphogenesis of layer II/III neurons. Here, we report loss of Lhx2, either in postmitotic layer II/III neurons or their progenitors, resulted in shrunken dendritic arbours and perturbed spine morphology. The defects were more pronounced upon Lhx2 disruption in progenitors, and were recapitulated when this was driven exclusively in basal progenitors. In postmitotic neurons, LHX2 regulates dendritic and spine morphogenesis via the canonical Wnt /{beta} Catenin signalling pathway. Constitutive activation of this pathway in postmitotic neurons mimics the Lhx2 loss-of-function phenotype. In E15.5 progenitors, LHX2 acts in part via bHLH transcription factor NEUROG2 to regulate dendritic morphogenesis. We demonstrate that loss of Lhx2 causes a massive increase in Neurog2 expression, and that Neurog2 knockdown partially rescues the loss of Lhx2 phenotype. Our study uncovers novel LHX2 functions consistent with its temporally dynamic and diverse roles in development. TeaserThe mature architecture of a neuron is shaped by distinct genetic mechanisms that act in its mother cell and after it is born.

neuroscience↗

bioRxiv2023

HCN channels enhance robustness of patterned activity propagation in heterogeneous conductance-based ring networks

Continuous attractor network (CAN) models lend a powerful framework that has provided deep insights about several aspects of brain physiology. However, most CAN models employ homogeneous, rate-based or artificially spiking neurons with precisely structured synaptic connectivity, precluding detailed analyses of the impact of specific neural-circuit components and associated heterogeneities on CAN dynamics. To address this caveat, we built populations of tunable and scalable conductance-based, physiologically constrained, ring network models consisting of distinct rings of excitatory and inhibitory neurons. We assessed the network for its ability to sustain robust propagation of patterned activity across the rings. First, in homogeneous ring networks, we found that robust activity propagation could be sustained through several different combinations of synaptic weights, demonstrating synaptic degeneracy in the emergence of robust activity propagation. We incorporated intrinsic heterogeneity through randomized perturbations to ion channel parameters of all neurons and synaptic heterogeneity by adding jitter to the Mexican-hat connectivity between inhibitory neurons. We found the number of networks exhibiting robust propagation of patterned activity to reduce with increase in the degree of synaptic or intrinsic heterogeneities. Motivated by the ability of intrinsic neuronal resonance to stabilize heterogeneous rate-based CAN models, we hypothesized that increasing HCN-channel (a resonating conductance) density would stabilize activity propagation in heterogeneous ring networks. Strikingly, we observed that increases in HCN-channel density resulted in a pronounced increase in the proportion of heterogeneous networks that exhibited robust activity propagation, across multiple trials and across three degrees of either form of heterogeneity. Together, heterogeneous networks made of neurons with disparate intrinsic properties and variable HCN channel densities yielded robust activity propagation, demonstrating intrinsic degeneracy in the emergence of robust activity propagation. Finally, as HCN channels also contribute to changes in excitability, we performed excitability-matched controls with fast HCN channels that do not introduce resonance. We found that fast HCN channels did not stabilize heterogeneous network dynamics over a wide range of conductance values, suggesting that the slow negative feedback loop introduced by HCN channels is a critical requirement for network stabilization. Together, our results unveil a cascade of degeneracy in ring-network physiology, spanning the molecular-cellular-network scales. These results also demonstrate a critical role for the widely expressed HCN channels in enhancing the robustness of heterogeneous neural circuits by implementing a slow negative feedback loop at the cellular scale.

neuroscience↗

bioRxiv2023

miRNA-mediated inhibition of an actomyosin network in hippocampal pyramidal neurons restricts sociability in adult male mice

Social deficits are frequently observed in patients suffering from neurodevelopmental disorders, but the molecular mechanisms regulating sociability are still poorly understood. We recently reported that the loss of the microRNA cluster miR-379-410 leads to hypersocial behavior and anxiety in mice. Here, we show that ablating miR-379-410 in excitatory neurons of the postnatal mouse hippocampus recapitulates hypersociability, but not anxiety. At the cellular level, miR-379-410 loss in excitatory neurons leads to increased excitatory synaptic transmission and upregulation of an actomyosin gene network. Re-expression of three cluster miRNAs, as well as pharmacological inhibition of the actomyosin activator ROCK, was sufficient to reinstate normal sociability in miR-379-410 knockout mice. Several actomyosin genes and miR-379-410 family members were reciprocally dysregulated in isogenic human iPSC-derived neurons harboring a deletion present in Williams-Beuren-Syndrome patients, which are characterized by hypersocial behavior. Together, our results unveil a novel microRNA-actomyosin pathway involved in the control of sociability.

neuroscience↗