Frontiers in Neural Circuits

Recent studies have shown that symbiotic bacteria can have drastic effects on host neurobiology, but few simple, accessible models currently exist in which to study these interactions. Hawaiian bobtail squid (Euprymna scolopes) participate in a binary symbiosis with the bacterium Vibrio fischeri, a population of which resides in a specialized hindgut-derived organ called the light organ. Upon colonization by V. fischeri, the light organ undergoes transcriptional changes that suggest neurons are impacted by the initiation of symbiosis, but the nascent light organs innervation has remained uncharacterized. Here, we show that the light organ-associated nervous system (LONS) in hatchling E. scolopes is a remarkably complex segment of the peripheral nervous system. The LONS is largely plexiform and originates from two primary nerves connected by a local commissure. The abundance of synapsin-like immunoreactivity (-lir) indicates that the lobe plexus is highly interconnected. We also highlight a small number of serotonin-lir neurites that innervate the anterior appendages whose developmental fate may be directly affected by symbiont-driven light organ morphogenesis. Finally, we present evidence that a limited but diverse population of neurons reside within the light organ and are often located near internal symbiont-interacting structures. This description of the E. scolopes LONS serves to provide a foundation from which to investigate how beneficial bacterial symbionts affect host peripheral neurobiology in a tractable model system.

The brain is one of the most complex animal organs but the development of the many different neuron types remains enigmatic. A set of brain-specific transcription factors is known to be involved in brain patterning but their specific contributions remain to be elucidated in most cases, including foxQ2II. This transcription factor is known to be conserved in anterior neuroectodermal patterning of most animals while it has been lost from vertebrates. However, the contribution of foxQ2II-positive neurons to the adult brain has remained enigmatic. Here, we use an enhancer trap, immunostainings and our newly established beetle brainbow system to categorize Tc-foxQ2II-positive neurons into nine clusters with different projection patterns. All clusters contain neurons with the fast activating neurotransmitters acetylcholine and glutamate while no Tc-foxQ2II positive neuron is GABA-ergic or serotonin-positive. Interestingly, we found that many dopaminergic neurons were Tc-foxQ2II positive and we homologize them with dopaminergic neurons of the PPL2c, PPM1 and PPL1 cluster described in the Drosophila brain. Our results show that Tc-foxQ2II marks subsets of fast-acting interneurons contributing to the higher order brain centers mushroom bodies and central complex. Taken together, our work expands the known functional range of foxQ2 genes from sensory and neurosecretory cell specification to interneurons involved in the function of higher order brain centers.

The phenomenon of splitting was originally observed in hamsters which, after prolonged exposure to constant light, exhibit two rest/wake cycles within a subjective day. Splitting is a consequence of the left and right suprachiasmatic nuclei (SCN) falling out of synchrony. While it is known that split activity is characterized by an antiphase relationship between the left and right SCN and between the core and shell within each hemisphere, the role of the commissural projections that connect the right and left SCN is not known. In the present study, we investigate the impact of the inter-hemispheric connections on the split and unsplit dynamics of a computational model of the bilateral SCN. Our model has 4 nodes corresponding to each right and left core and shell. We simulated our bilateral model under different lighting conditions and measured its period and the phase relationships among the 4 nodes. To further characterize the dynamics of the system, we performed a bifurcation analysis. We found that the bilateral model automatically splits unless entrained by bright light/dark cycles, or unless it has excitatory inter-hemispheric connections. This suggests that excitatory cross-connections may be important for freerunning behavior. We found that constant light of varying intensities transitions the model between split and unsplit activity only in very limited conditions, but the strength and polarity of the contralateral connections play a much greater role in this dynamical transition. These findings suggest that splitting may involve plasticity of the inter-hemispheric connections of the SCN.

Octopamine is involved in a variety of different physiological and behavioral mecha-nisms in Drosophila melanogaster. Throughout the life cycle of the fruit fly, from the larva to the adult, octopaminergic neurons in both the central and the peripheral nerv-ous system target a multitude of neurons and even non-neuronal tissues, making it challenging to analyze individual mechanisms of octopamine function. One approach to deconstructing this complex system is to examine the postsynaptic components of signal transmission. In Drosophila, octopamine interacts with six distinct G-protein-coupled receptors. For some of these receptors, expression maps and functional im-plications have been described. In contrast, other receptors have been neglected, partly due to the lack of suitable genetic tools. Here, for the first time, we compiled a complete set of mutant lines of all known octopamine receptors, all generated using the same genetic tool, the recently established Trojan Exon system. It integrates the Gal4/UAS binary expression strategy while simultaneously impairing receptor func-tion. This enabled us to generate a comprehensive anatomical map of receptor ex-pression in the larva and, at the same time, analyze the function of individual octopa-mine receptors during larval development, chemosensory perception and locomotion. All octopamine receptors (Oamb, Oct2R, Oct{beta}1R, Oct{beta}2R, Oct{beta}3R, and Oct-TyrR) showed extensive signal in the central nervous system. The same was found for the peripheral nervous system, with the exception of Oct{beta}2R, which showed pronounced expression in the somatic muscles. We also observed a previously undescribed role of Oct{beta}1R, Oct{beta}3R, and Oct-TyrR in larval hatching and in the survival of larvae and pupae. Molecular evaluation of the Trojan Exon octopamine lines supports our analy-sis. In addition, we combined the experimental results with gene expression data from the different development stages of Drosophila melanogaster and from different tis-sues and cell populations throughout the body. Overall, we compiled, analyzed and validated a complete set of octopamine lines which, together with gene expression analysis, provides a basis for further functional studies on the larval octopaminergic system.

The cerebellar nuclei form the main output structures of the cerebellum and are composed of a deeply conserved set of cell types. Two excitatory cell classes, Class-A and -B, are present in each cerebellar nucleus and mediate all excitatory output of the cerebellum. To provide genetic access to these cell types, here we identified Acan as a marker gene for Class-B cells and generated a knock-in Acan-P2A-Cre mouse line. We demonstrate that this Acan-Cre line selectively labels Class-B neurons in the cerebellar nuclei and validate its use in viral projection tracing. This new mouse line provides a valuable genetic tool to study cerebellar nuclei organization and function.

Vestibular neurons are core elements of the pathways involved in vestibulo-motor functions, such as vestibulo-spinal and vestibulo-ocular reflexes. To meet behavioral needs, electrophysiological neuronal properties are adequately adapted to the sensory-motor computation sustaining these distinct vestibular reflexes. During frog metamorphosis, there is a complete reorganization of the posturo-locomotor system while the oculomotor system remains minimally changed, probably associated to so far unknown changes in vestibular neuronal properties. We used this unique model to investigate the central developmental mechanisms underlying such a reconfiguration of vestibular-associated behaviors. Central vestibular neurons exhibit two types of electrophysiological phenotypes: tonic neurons with a continuous discharge and phasic neurons with a transitory discharge mainly due to the activation of Kv1.1 channel. Electrophysiological recordings and Kv1.1 immunolabeling of vestibulospinal (VS) and vestibulo-ocular (VO) neurons at both larval and juvenile stages revealed that the majority of VS neurons exhibited a tonic discharge in larvae but a phasic discharge in juvenile, while VO neurons remained mainly tonic throughout development. Changes in phasic and tonic neurons proportions in VS population are partly explained by neurogenesis. But we provide evidences that an electrophysiological phenotype switch is a concomitant developmental mechanism participating in the maturation of these central vestibular neurons. All together our results showed that the maturation process in central vestibular neuronal groups is highly related to the metamorphosis-induced remodeling of vestibulo-motor functions they are involved in, with the ultimate purpose of ensuring an adequate adaptation of neuronal elements properties to the developmental changes of behavioral constrains.

Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation that has demonstrated competitive performance across a range of machine learning tasks. Recent work has extended EP to spiking neural networks (SNNs), leveraging leaky integrate-and-fire (LIF) neurons and spike-based plasticity rules to improve biological realism while maintaining strong performance. In this work, we propose an EP-based SNN framework that combines LIF neural dynamics with a predictive learning rule, replacing conventional spike-timing-dependent plasticity (STDP) with a learning rule more directly aligned with predictive coding principles. We evaluate the proposed model on multiple image classification benchmarks, including MNIST, KMNIST, and Fashion-MNIST, and compare its performance with a BP-trained LIF SNN baseline. Our results show that the proposed EP-based LIF model (EP+LIF) achieves competitive accuracy across datasets, with performance approaching that of the BP-trained counterpart (BP+LIF) while relying on a biologically motivated local learning rule. In addition, analysis of hidden-layer spiking activity reveals that EP+LIF produces more persistent hidden-state activity, whereas BP+LIF yields sparser spiking representations. These results demonstrate that predictive learning can support effective EP-based training in LIF spiking networks, while also highlighting differences in activity patterns that motivate future work on activity regulation and sparse spiking dynamics.

Dendritic arbor morphology is shaped in part by interactions with neighboring dendrites, and its geometry strongly influences the spatial distribution and strength of synapses. These observations raise the possibility that local dendritic contacts help determine where synapses accumulate and strengthen. Previous work in cultured hippocampal neurons showed that dendrite-dendrite contact sites are non-random and associated with local synaptic clustering. Here we asked whether a different type of dendritic contact, formed between a dendrite and the soma of a neighboring neuron, behaves similarly. Using dissociated hippocampal cultures, immunofluorescence imaging, time-lapse microscopy, quantitative image analysis, stochastic spatial simulations, and minimal quantitative modeling, we identified three recurrent classes of dendrite-soma interactions (DSIs): dendrites crossing directly over a neighboring soma, growing tangentially along the soma perimeter, or contacting the proximal region where a neighboring dendrite emerges from the soma. These interactions were abundant, occurred exclusively between different neurons, and showed substantial structural persistence over several days. Their overall frequency exceeded stochastic predictions across culture densities, and two configurations - proximal and tangential contacts - were selectively enriched above random expectation, whereas soma-crossing contacts were largely consistent with stochastic overlap. DSI composition also changed over development, with proximal contacts becoming progressively more prevalent. At DSI sites, synaptophysin-positive puncta were significantly denser and more intense than on non-interacting dendritic segments, consistent with local enrichment and strengthening of presynaptic specializations. Minimal modeling further indicated that biased formation together with developmental stabilization explains the observed organization better than stochastic geometry alone. These findings identify DSIs as non-random structural motifs in cultured hippocampal networks and suggest that dendrite contact geometry can contribute to synaptic distribution and strengthening.

Electrical transmission is mediated by intercellular channels that cluster into structures known as gap junctions (GJ). In vertebrates, GJ channels are encoded by the gene family of connexin (Cx) proteins that assemble as hexamers, termed hemichannels, in the pre- and postsynaptic membranes, and that subsequently dock to form GJ channels. Auditory contacts on the fish Mauthner cells serve as model to study the properties and organization of vertebrate electrical synapses. Electrical transmission at these synapses is mediated by multiple co-existing GJs at which the presence of intercellular channels is regulated by a molecular scaffold. Zebrafish contain four homologs of the neuronal Cx36: Cx35.5 and Cx35.1 (gjd2a and b, respectively), and Cx34.1 and Cx34.7 (gjd1a and b). Cx mutations suggested that GJs are formed by heterotypic channels made of presynaptic Cx35.5 and postsynaptic Cx34.1. Using transgenic fish in which Cxs were tagged, we found that a second Cx, Cx34.7, is present together with Cx34.1 on the postsynaptic side at some but not all GJs at these terminals. When exogenously expressed, both Cx34.1 and Cx34.7 formed heterotypic functional channels with Cx35.5, each with substantially different voltage-dependent properties, indicating they can serve differential functions. However, we previously demonstrated that electrical transmission is lost in Cx34.1 but not Cx34.7 null mutants, suggesting that Cx34.7 cannot compensate for the loss of Cx34, despite the intrinsic ability of Cx34.1 and Cx34.7 to create functional channels. The findings reveal an unanticipated functional organization in the electrical synapse, where Cx34.1 is obligatory and Cx34.7 accessory, roles that appear to be defined by the postsynaptic molecular scaffold, with two postsynaptic Cxs possibly assembling under specific functional contexts. Thus, our results indicate that electrical synapses share an organizational motif with chemical synapses, akin to how they combine postsynaptic receptor types to modify synaptic function.

Understanding how neuronal populations interact to encode and transform sensory information is a fundamental challenge in computational neuroscience. Most existing studies, however, study neural encoding, behavioral readout, and functional connectivity as disjoint problems. Two-photon calcium imaging enables simultaneous recording of large neuronal ensembles in vivo, driven by diverse stimuli and eliciting distinct behaviors. However, extracting directional functional connectivity metrics as well as encoding and readout properties of neurons from such data remains difficult due to indirect and noisy observations of spiking activity, slow temporal dynamics, and the latent interplay between external stimuli and endogenous neural processes. Here, we introduce a unified conceptual and operational modeling and inference framework for directly extracting functional Granger causal (GC) effects between neurons, from external stimuli to neurons, and from neurons to behavior, from two-photon imaging data, in the sense of Granger. Inspired by the intersection information framework, we also identify neurons that encode features of sensory stimuli that inform behavioral readout. The resulting GC networks together with the taxonomy of functional sensori-behavioral relevance, which we call G-taxonomy, provides a powerful statistical analysis framework, enabled by the integration of several techniques including state-space modeling and inference, variational inference, and point processes. We applied the proposed framework to simulated and experimentally-recorded two-photon imaging from the mouse auditory cortex (A1) during both passive listening and active tone discrimination. Our simulation studies reveal significant improvement of our proposed methodology over existing techniques. Analysis of experimental data from the mouse A1 identifies distinct groups of cells with diverse sensori-behavioral relevance, as well as changes in functional connectivity associated with correct vs. incorrect behavior. In summary, this work provides a principled and data-driven methodology for uncovering directional interactions among the neurons, sensory stimuli, and behavior, all within the same statistical framework, offering new insights into how distributed cortical populations transform sensory inputs into behaviorally relevant representations. Author SummaryThe brain processes sensory inputs through the coordinated activity of large networks of neurons and produces readouts that elicit behavior. Understanding how information flows and is processed through these networks is a central goal of neuroscience. In this study, we present a new computational framework that identifies directional interactions among neurons in an ensemble as well as from sensory stimuli to neurons and from neurons to behavior. Utilizing the Granger formalism to identify directional effects, as opposed to common correlational measures, our framework extracts said effects directly from two-photon calcium imaging data. We tested our proposed method on both simulated data and recordings from the auditory cortex of mice during passive listening and active tone discrimination tasks. Our method revealed diverse groups of neurons in the auditory cortex with distinct functional roles and relevance to sensori-behavioral integration. Our framework provides a new way to study the flow of information in the brain and can be broadly applied to uncover neural computations across sensory and cognitive systems.

Homeostatic plasticity of intrinsic excitability (IE) in the visual system has been essentially shown at the cortical level but whether thalamic nuclei also express homeostatic plasticity of IE is unknown. We show here that 4 days of monocular deprivation (MD) at eye opening induces a homeostatic change in IE in dorsal lateral geniculate nucleus (dLGN) neurons. Neurons recorded in the dLGN region activated by the deprived eye are more excitable than neurons recorded in the dLGN region activated by the open eye. No significant changes were observed following 7 days of MD, however. Enhanced excitability in neurons from the deprived side after 4 days of MD was associated with a reduced Kv1-dependent LTP-IE, a smaller voltage ramp, and a reduced inter-spike interval, suggesting that Kv1 channels are down-regulated in deprived dLGN neurons. Furthermore, the ankyrin G signal of the axon initial segment was larger in deprived dLGN neurons compared with open ones, indicating that Nav1 channel number also undergoes homeostatic regulation, and Kv1.1 channel signals were lower in deprived neurons compared to open ones. In addition, electrical coupling was found to be strengthened in neurons displaying enhanced IE following either brief (4 days) or long (10 days) MD. These results suggest that homeostatic and Hebbian plasticity in the dLGN share common expression mechanisms involving the regulation of Kv1 channels, Nav1 channels and electrical coupling between relay neurons.

Apuschkin et al. (2024) proposed a GPCR-based transcriptomic atlas for midbrain dopamine (DA) neuron subpopulations, including candidates such as Nmur1, Cckar, and Ffar4. To guide genetic targeting, these markers must reflect functional expression in adult DA neurons. Using in situ hybridization, Cre-dependent reporter lines, and both intracranial and systemic viral approaches, we find no evidence of adult Nmur1-mediated recombination in DA neurons, while Cckar-driven recombination is consistent with developmental expression only. Notably, Ffar4 expression overlaps extensively with Ntsr1 midbrain populations, indicating that it does not define a distinct DA neuron class. Furthermore, analysis of independent spatial transcriptomic datasets together with our MERFISH data shows that many proposed GPCR markers are not detectably expressed in adult DA neurons. These findings demonstrate that transcriptomic enrichment does not always yield reliable adult markers and highlight the need for functional validation prior to use in circuit targeting.

We present a free and open-source, semi-automated, topologically robust pipeline for fitting cable models to 3D surface mesh morphology data of neuronal membranes, particularly suited to structures with complex shapes and topological holes. The motivation for this work is the discovery of morphologically complex neural spines on the auditory space-specific neurons of the barn owl (Tyto alba, Tyto furcata), dubbed "toric spines", notable for their high curvature, branching density, and holes/loops. Multicompartmental simulation software requires morphology to be represented as cable models (e.g., SWC format), yet existing software tools for fitting cable models to complex 3D surface meshes have not produced satisfactory results for toric spines, and loops are generally unsupported. We present the Mesh and Skeleton Cable Fitting (MASCAF) pipeline and software, which fits a cable model (e.g., SWC format) to a surface mesh using mean-curvature flow skeletonization. In this paper, we demonstrate how MASCAF is applied to fit cable models, how loops can be reconstructed in simulations with the Arbor and NEURON simulation software, and how the results can be validated using geometry and simulator-based methods. While non-tree morphologies such as toric spines are neuroanatomically special, our software pipeline provides a cable-model fitting approach for surface mesh data that is topologically robust, deterministic, open-source, and applicable to general morphologies, thereby closing a crucial gap between neuronal imaging and high-resolution simulation.

Growing interest to describe the electrical behavior of glial cells, mainly astrocytes, in intact brain tissue poses more and more challenges to commonly accepted belief they only respond in a linear manner in uptake of the excess of extracellular potassium and maintenance of their network equipotentiality. Their highly conductive mutual interconnections via gap junction (GJ) connections introduce yet another class of nonlinear elements. As more studies report nonlinearities in membrane voltage Vm dependence of both, the membrane and junctional conductances, the need to formulate minimal dynamical models of their transient behavior is getting more acute. Since ODE models of coupled cells, even in simplest 1-d arrays, require simplified descriptions and small set of parameters, rare quantitative studies on glia makes the task even more difficult. This study attempts to qualify a self-coupled cell, or a glial cell coupled to fixed voltage as useful system for detecting the nature of instabilities and transitions coming from coupling. In a novel biophysical model of coupled astrocyte, we introduce nonlinear kinetics of deactivation for large junctional voltages for the first time. We found that N-shaped nonlinearities and corresponding fold structure in the vector field of isolated cell serves as a baseline on top of which coupling nonlinearities enrich the bifurcation picture. Numerical simulations of 1-d array of coupled astrocytes show that coupling increases the propensity of astrocytic Vm to bistability and front propagation. We believe that presented illustrations of possible effects of coupling nonlinearities will motivate neurobiologists to further explore their impact in disease. Significance statementTransient changes in membrane voltage of glial cells may produce significant transient voltage difference between directly coupled cells. Nonlinear steady-state conductance of their interconnection elements, the gap junctions, introduce nonlinear current profiles which are very difficult to measure and quantitate using the available methods due to marked permeability of the junctions and leakiness of glial membrane in general. We propose a minimal model of glial membrane extended with a self-coupled feedback loop, which under realistic simplifying assumptions could serve for qualitative analysis of the impact of coupling, on the stability of resting membrane voltage. Neuronal cells of the brain and spinal cord cannot exist and function without supportive and neuromodulatory functions of the diverse population of glial cells. This applies to virtually all physiological processes on cell level - from cell development, metabolic support, membrane signaling, slow molecular signal transduction, ion homeostasis, neurovascular coupling, myelination, to mention only a few, manifest neuro-glial interaction. Even though all glial cell types are interconnected, the most abundant ones, the astrocytes are massively interconnected by gap junctions to form ordered networks. Electrically, astrocytic networks display membrane voltage equipotentiality, which is considered system-wide resting state for given neuro-glial circuit or unit. With molecular and cellular substrates of glial connectivity being slowly elucidated, network science and dynamical modeling are slowly "invading" that area with many important issues left open. In this study using classical dynamical systems approaches we give indications how nonlinear intercellular coupling between astrocytes affects physiological resting state and its instabilities compared to isolated, uncoupled cell. We strongly believe the suggested minimal model could fill the gap in ODE modeling of neuro-glial circuits, within broadest scope of hypothesis-driven research in cell-level neuroscience.

The hippocampal CA2 region is critical for social novelty recognition memory--the discrimination of whether a conspecific is novel or familiar. However, its role in forming a memory of a pair-bonded mate is unknown. To examine how social memories of pair-bonded individuals are encoded, we sought to understand if CA2 and the neighboring CA1 region participate in the memorization and recognition of a pair-bonded mate in monogamous Peromyscus californicus (California mice). Here, we report that CA2 and CA1 show distinct changes in social encoding of an opposite sex conspecific following pair-bonding. Using multi-channel silicon probes, we recorded single units from CA2 and CA1 in freely behaving male mice before and after pair bond formation during interactions with novel and partner females. We found that the strength of CA2 representations of a novel female mouse weakened after pair bond formation, indicating that CA2 may be preferentially important for novelty detection. In contrast, CA1 demonstrated an increase in the strength of encoding a female partner after pair-bond formation, suggesting that CA1 may encode partner memory. These findings indicate that pair bonding shifts the discrimination of social information from CA2 to CA1.

BackgroundThe relevance of spontaneous activity has been unlocked thanks to recent large scale recordings that revealed, via Shared Variance Component Analysis (SVCA), the high-dimensional nature of the ongoing activity. A fundamental problem is how the dimension modifies when more neurons are included in the analysis. Contradictory results have been reported on this subject based on SVCA and Principal Component Analysis (PCA). New MethodWe investigate pro et contra of SVCA and PCA for the identification of reliable responses encoding underlying state variables. We focus on common features of the spectra of the reliable variances (RVs) and on their dimensionality. The analysis is demonstrated on previously published Ca2+ data from the visual and the dorsal cortex in head fixed mice during spontaneous behavior. ResultsRVs grow proportionally to the number N of neurons and show a power-law decay k- with the k-th SVC dimension over a range bounded by a maximal dimension kc, initially diverging as N 1/ and then saturating at sufficiently large N. The reliable dimensionality, estimated with different methodologies, also shows a clear saturation to an asymptotic value for large N. Furthermore, its value decreases when becomes larger, as demonstrated by employing experimental data as well as theoretical predictions. ConclusionWe have shown that SVCA is an extremely effective tool to extract reliable features from the neural signals, and that the exponent represents a biomarker able to reveal the level of correlation of the neurons as well as the dimensionality of the reliable space. HighlightsO_LIAdvantages and drawbacks of Shared Variance Component Analysis to extract reliable signals from neural data C_LIO_LIComparison of different methods to estimate reliable neural dimensionality associated to spontaneous activity C_LIO_LIAnalytical expressions of embedding dimensionality for power-law decaying reliable variances C_LIO_LIBounded growth of the dimensionality with the number of neurons C_LI

The rodent accessory olfactory system (AOS) detects chemosignals emitted by conspecifics and other species to support beneficial behaviors. Peripheral vomeronasal sensory neurons (VSNs), the AOS chemical sensors, detect fecal bile acids in patterns that have unknown significance to the animal. We used a combination of mass spectrometry and VSN calcium imaging to investigate the AOS capacity to use bile acid information to discriminate between fecal samples from captive reptiles and mice with varying gut microbiome states. Mass spectrometry analysis revealed bile acid patterns that distinguished biologically relevant samples from one another, representing theoretical discrimination axes. We measured VSN response patterns to bile acid stimuli aligned with theoretical discrimination axes. We found that VSNs perform stimulus "whitening" via an inverse relationship between natural bile acid abundance and population response magnitude. VSNs showed maximum sensitivity to taurine-conjugated bile acids, which have high theoretical discriminatory value, but were found at low natural abundance levels. Individual taurine-conjugated bile acids drove threat assessment behavior when added to familiar mouse fecal extracts, suggesting high behavioral significance. Finally, we analyzed the degree to which the AOS utilizes the theoretical information about species, diet, and gut microbiome status from bile acids. We found that VSN tuning patterns align with theoretical axes for discriminating reptilian predators from vegetarians, and between mice with different gut microbiome states. VSN tuning was especially well-aligned with the information available about conspecific gut microbiome status. These results show that AOS bile acid chemosensation supports discrimination of multiple biologically relevant states. Short abstractThe rodent accessory olfactory system (AOS) detects fecal bile acids via combinatorial codes with unknown biological significance. We investigated whether AOS bile acid chemosensation supports species and gut microbiome evaluation using mass spectrometry, calcium imaging in vomeronasal sensory neurons (VSNs), and analytical modeling. Bile acid excretion patterns theoretically supported discrimination of reptilian predators from vegetarians, and germ-free mice from conventionally raised counterparts. VSNs demonstrated stimulus "whitening" via an inverse relationship between natural bile acid abundance and population response magnitude. VSNs had highest sensitivity to taurine-conjugated bile acids, a novel class of chemosignals that elicited behavioral aversion. VSN tuning aligned with ideal discrimination axes, which was especially strong for gut microbiome-associated bile acid abundance patterns. These results show that AOS bile acid chemosensation supports discrimination of multiple biologically relevant states.

Adolescence is an important developmental period during which there are diverse changes in the brain and behavior. Goal-directed behaviors and the component processes underlying those behaviors improve during adolescence, including working memory, response inhibition, and reinforcement learning. At the same time there is substantial pruning of excitatory connections in prefrontal cortex and ongoing myelination of axons. However, psychiatric disorders also become increasingly prevalent in late adolescence and early adulthood. In this study, we develop computational models that suggest a hypothesis for how the ongoing changes in the brain can give rise to the increased prevalence of psychiatric disorders. We show that both myelination and pruning during adolescence lead to attractor landscapes in which strongly encoded memories, driven by three-factor learning rules that modulate Hebbian plasticity, come to dominate the landscape of brain activity, at the expense of weakly encoded memories. Pruning and myelination lead to large, strong attractors which, if they are related to aversive emotions, can drive intrusive thoughts and compulsions in obsessive compulsive disorder, rumination in depression, and aversive memories in post-traumatic stress disorder. The link between pruning, myelination and the emergence of dominant attractors for emotionally salient memories is well supported by the models. The way these effects map onto forebrain circuits requires more work.

The dentate gyrus (DG) is thought to play a key role in the formation of dissociable memory representations for similar contexts. Neurons in the DG receive highly processed spatial and nonspatial sensory information from the medial and lateral entorhinal cortices, respectively. Changes in spatially tuned firing patterns of DG place cells occur after spatial changes to an environment, but the degree to which DG place cells respond to ethologically relevant nonspatial stimuli is largely unknown. Spatial and nonspatial information is thought to be transmitted to the DG during discrete local field potential events called dentate spikes. Here, we tested the extent to which different spatial and nonspatial stimuli modulate place cell firing patterns and dentate spike dynamics. We performed extracellular recordings of DG place cells and local field potentials in rats of both sexes exploring a familiar spatial environment, in which social stimuli and nonsocial odors of varying ethological relevance were presented, and a novel spatial environment. As expected, DG place cells exhibited different firing patterns between familiar and novel environments. Significant changes in firing were not observed, however, with any of the nonspatial stimuli. Surprisingly, the occurrence of dentate spikes associated with lateral entorhinal cortex input increased during exploration of ethologically relevant stimuli, and this increase was greater for social stimuli. Altogether, these results suggest that the DG preferentially responds to social stimuli at the network level, providing novel insights into how spatial and nonspatial information is processed in the DG. Significance StatementThe dentate gyrus (DG) encodes spatial and nonspatial sensory information. Here, we investigated how place cells in the DG respond to changes in spatial and nonspatial cues in familiar and novel environments in rats. We found that DG place cell firing patterns significantly changed in a novel spatial environment but did not significantly change when nonspatial stimuli were presented in a familiar environment. Conversely, discrete dentate spike events reflecting presumed nonspatial inputs from the lateral entorhinal cortex increased during investigation of ethologically relevant nonspatial stimuli. These findings suggest novel mechanisms of nonspatial information processing in the DG.

BackgroundThis study examines a competition-based model (C-model) designed to capture the temporal dynamics of successive brain microstates derived from electroencephalography (EEG) recordings during eyes-open conditions. The analyzed data were obtained from a public repository comprising microstate sequences from 60 sessions of a single subject [1]. When applied to microstate dynamics, the C-model posits a stochastic competition among neural circuits underlying the expression of individual microstates. MethodsThe model is formulated at a conceptual level (computational level in Marrs framework) and employs a geometric distribution to account for the long right tail of microstate duration distributions, interpreted as the probability of "failure" of the currently active microstate to persist. To account for the short-lived left tail, the model incorporates a transient increase in the stability of the currently active network, or equivalently, a temporary decrease in the activation probability of competing microstates (refractory period). ResultsThe model provides a good fit to the microstate duration distributions across all 60 sessions. One third of sessions showed microstate identity sequential dependency with respect to the previous microstates. DiscussionThese results suggest that the C-model captures key aspects of microstate temporal structure. Moreover, because microstate probabilities can be modulated by psychophysiological conditions--including the influence of previously active networks--the model may serve as a building block for more comprehensive neurobiological frameworks of neural and behavioral dynamics. In such frameworks, microstate sequences could emerge from structured competition and flow among neural networks supporting microstate expression.