Frontiers in Neural Circuits

New tools in optogenetics and molecular biology have culminated in recent studies which mark immediate-early gene (IEG)-expressing neurons as memory traces or engrams. Although the activity-dependent expression of IEGs has been successfully utilised to label memory traces, their roles in engram specification is incompletely understood. Outstanding questions remain as to whether expression of IEGs can interplay with network properties such as functional connectivity and also if neurons expressing different IEGs are functionally distinct. We investigated the expression of Arc and c-Fos, two commonly utilised IEGs in memory engram specification, in cultured hippocampal neurons. After pharmacological induction of long-term potentiation (LTP) in the network, we noted an emergent network property of refinement in functional connectivity between neurons, characterized by a global down-regulation of network connectivity, together with strengthening of specific connections. Subsequently, we show that Arc expression correlates with the effects of network refinement, with Arc-positive neurons being selectively strengthened. Arc positive neurons were also found to be located in closer physical proximity to each other in the network. While the expression pattern of IEGs c-Fos and Arc strongly overlaps, Arc was more selectively expressed than c-Fos. These IEGs also act together in coding information about connection strength pruning. These results demonstrate important links between IEG expression and network connectivity, which serve to bridge the gap between cellular correlates and network effects in learning and memory.

Fundamental neural mechanisms such as activity dependent Hebbian and homeostatic neuroplasticity are driven by balanced excitatory - inhibitory synaptic transmission, and work in tandem to coordinate and regulate complex neural network dynamics in both healthy and perturbed conditions. These neuroplasticity processes shape neural network activity, as well as structural and functional aspects of network organization, information transmission and processing. While crucial for all aspects of network function, understanding how the brain utilizes plasticity mechanisms to retain or regain function during and after perturbation is often challenging. This is because these processes occur at varying spatiotemporal scales simultaneously across diverse circuits and brain regions and are thus highly complicated to distinguish from other underlying mechanisms. However, neuroplasticity and self-organizing properties of the brain are largely conserved in in vitro biological neural networks, and as such, these networks enable us to investigate both structural and functional plasticity responses to perturbation networks at the micro and mesoscale level. In this study, we selectively silenced excitatory synaptic transmission in in vitro neural networks to investigate the impact of the perturbation on structural and functional network organization and resilience. Our results demonstrate that selective inhibition of excitatory transmission leads to transient de-clustering of modular structure, increased path length and degree in perturbed networks. These changes indicate a transient loss of network efficiency; with the network subsequently reorganizing to a state of increased clustering and short path lengths following recovery. These findings highlight the remarkable capacity of neural networks to reconfigure their functional organization following perturbation. The ability to detect and decode such processes as they evolve highlights the robustness of our models to investigate certain dynamic network properties that are often not accessible by in vivo methods.

Neuronal network resilience, the ability of brain circuits to maintain and recover functional connectivity following perturbation, is fundamental to cognitive stability and adaptability. Using human cerebral organoids and multi-electrode arrays (MEAs), we investigated how mechanical stress disrupts network stability and identified key mechanisms regulating recovery. Blast overpressure exposure destabilized small-world network (SWN) organization, increasing network fragmentation and reducing overall integration. Merged SWNs, which exhibit high connectivity, were particularly vulnerable, while fragmented and single SWNs persisted for extended periods, indicating a shift toward less resilient network states. Optogenetic stimulation promoted network recovery, reducing the persistence of fragile states and facilitating transitions toward more cohesive network structures. GABAergic signaling emerged as a critical regulator of network resilience, with pharmacological inhibition exacerbating fragmentation and impairing network reorganization. These findings reveal fundamental principles of how inhibitory networks regulate circuit stability, with implications extending beyond mechanical injury to broader conditions characterized by network dysfunction, including anxiety, depression, PTSD, and neurodegenerative disorders. Understanding the mechanisms governing network adaptation and resilience could inform new therapeutic strategies aimed at stabilizing disrupted neural circuits across a range of neurological conditions. Significance statementNeuronal networks must dynamically adapt to maintain function in the face of disruption, yet the mechanisms that govern network resilience remain poorly understood. Using human cerebral organoids, we demonstrate that primary blast overpressure destabilizes small-world networks, increasing fragmentation and reducing overall connectivity. Critically, GABAergic signaling emerges as a key stabilizer, with inhibition of GABA receptors amplifying network fragmentation and impairing recovery. These findings provide fundamental insight into how neural circuits resist and recover from mechanical stress, bridging gaps between basic neuroscience, injury pathology, and potential therapeutic interventions. By identifying inhibitory signaling as a regulator of network resilience, our work informs not only traumatic brain injury treatment strategies but also broader efforts to restore functional connectivity in neurological disorders, from epilepsy to neurodegeneration.

Thalamocortical circuits play a fundamental role in cognitive functions, and neural synchronization, with disruptions implicated in disorders. Here, we investigated the neural dynamics of thalamocortical connectivity using computational modeling of rodent and primate thalamocortical loops. We incorporated distinct projections and varying network configurations and examined their impact on circuit synchrony, spiking patterns, and sleep spindle generation. Circuits included distinct core and matrix thalamocortical projections, with core pathways providing focal, driving input to middle cortical layers, while matrix pathways mediate widespread, modulatory signaling across superficial layers, and the presence of thalamic interneurons, which are scarce in rodents but comprise up to a third of the thalamic neurons in primates. In our simulations, these distinctions produced clear species-and loop architecture-dependent effects: rodent circuits were markedly more sensitive to parameter changes in core and matrix thalamocortical connectivity strength, while primate circuits maintained relatively stable spatiotemporal patterns across parameter variations, exhibiting greater stability and synchrony. Sleep spindle analysis likewise revealed species differences. Overall, across all thalamocortical configurations, rodent simulations produced spindles with greater spatiotemporal variability, showing irregular event structure and timing. In contrast, primate spindles were more uniform and coherent, with clearer and more consistent organization across neurons and time. These findings provide insights into species-specific differences in thalamocortical dynamics and have implications for modeling sensory and cognitive disruptions in disorders such as autism and schizophrenia. By incorporating distinct configurations, and interspecies differences, our model contributes to understanding how thalamocortical dysregulation may differentially impact spindle generation, network synchrony, and information processing across species.

AO_SCPLOWBSTRACTC_SCPLOWThe salamander is a key limbed vertebrate from which many major scientific questions can be addressed in the fields of motor control, evolutionary biology, and regeneration biology. An important gap of knowledge is the description of the electrophysiological properties of the neurons constituting their central nervous system. To our knowledge, some patch-clamp electrophysiological recordings were done in the spinal cord and recently in hindbrain slices, but not in any higher brain region. Here, we present a method to obtain patch-clamp recordings in slices of the telencephalon, diencephalon and rhombencephalon of salamanders. The method includes dissection of the brain, brain slice preparation, visual identification of neurons and patch-clamp recordings. We provide single cell recordings in the rhombencephalon, diencephalon and telencephalon of salamanders. This method should open new avenues to dissect the operation of salamander brain circuits at the cellular level. HO_SCPLOWIGHLIGHTSC_SCPLOW- Salamander brain slices of telencephalon, diencephalon, and rhombencephalon - Patch-clamp recordings in salamander brain slices - The salamander as a model to decipher tetrapod neural microcircuits

Levy flights (LF), a concept originating in statistical physics, describe random walks in which the step lengths follow a heavy-tailed probability distribution, often a power law. Unlike Brownian motion, where step lengths are constrained within a narrow range, LF are characterized by the coexistence of many short steps interspersed with occasional long jumps. Applying advanced computational techniques, we looked for LF-like patterns in high-resolution histological images of Macaca mulatta (Rhesus macaque) cortical area 4 from BrainMaps.org. Step-length distributions, derived from pairwise distances between neuronal somata, exhibited heavy-tailed behavior consistent with power-law models across all samples. Maximum likelihood estimation of power-law exponents ( values: 0.87-1.08) strongly supported the heavy-tailed nature of these patterns, showing a better fit with power-law models compared to exponential or normal distributions. Connectivity analyses revealed a dual organizational structure within cortical layers: densely interconnected local clusters coexisting with sparse long-range connections. k-Nearest neighbors graphs demonstrated small-world network properties, with average clustering coefficients ranging from 0.622 to 0.630 across samples. This consistent structural organization aligns with LF principles, wherein local processing is optimized alongside global integration for efficiency and functionality. The implications extend to developmental biology, as the emergence of LF-like patterns likely reflects intrinsic self-organizing processes during embryonic and fetal development. This LF-like organization provides a natural framework for designing artificial networks that optimize performance in tasks requiring both localized specialization and global integration. Moreover, understanding the developmental origins of these patterns could guide strategies for neural repair and regeneration in stroke or neurodegenerative diseases.

Cerebellar function, known to be important for motor learning and motor coordination, is mediated by efferent neurons that project to diverse motor areas. To understand cerebellar function, it is imperative to study how these efferent neurons integrate inputs from the principal neurons of the cerebellar cortex, the inhibitory Purkinje neurons (PNs). In zebrafish, PNs are bistable and we show here that bistability influences spike synchrony among PNs. Bistability also alters spike correlation with motor bouts. We asked how PN population synchrony influences Eurydendroid cells (ECs), which are postsynaptic targets of PNs and are the cerebellar efferent cells in zebrafish. Using optogenetics, we artificially modulated population synchrony of PNs over millisecond time scales and showed that under conditions of high synchrony, EC firing is briefly suppressed and entrained by PN spiking. However, the magnitude of such modulation is relatively small and indicates a strong combined influence of other synaptic inputs on EC spiking. Key PointsO_LICerebellar Purkinje neurons (PN) in larval zebrafish alter simple spike correlations with each other based on cellular state. C_LIO_LIThey also alter simple spike correlations with motor bouts as a function of state. C_LIO_LIWe altered PN population synchrony in a graded manner using optogenetics. C_LIO_LIPN targets are cerebellar efferent neurons, which in teleosts are called eurydendroid cells. C_LIO_LIWhen PN population is firing with high synchrony, eurydendroid cells are entrained better than when the PN input is asynchronous. C_LIO_LIThis can explain how PNs use bistability to modulate their influence on cerebellar output and ultimately, motor behavior. C_LI

Glutamatergic reticulospinal neurons in the gigantocellular reticular nucleus (GRN) of the medullary reticular formation can function as command neurons, transmitting motor commands to spinal cord circuits. Recent advances in our understanding of this neuron-dense region have been facilitated by the discovery of expression of the transcriptional regulator, Chx10, in excitatory reticulospinal neurons. Here, we address the capacity of local circuitry in the GRN to contribute to reticulospinal output. We define two sub-populations of Chx10-expressing neurons in this region, based on distinct electrophysiological properties and somata size (small and large), and show that these correspond to local interneurons and reticulospinal neurons, respectively. Using focal release of caged-glutamate combined with patch clamp recordings, we demonstrated that Chx10 neurons form microcircuits in which the Chx10 interneurons project to and facilitate the firing of Chx10 reticulospinal neurons. We discuss the implications of these microcircuits in terms of movement selection. SIGNIFICANCE STATEMENTReticulospinal neurons in the medullary reticular formation play a key role in movement. The transcriptional regulator Chx10 defines a population of glutamatergic neurons in this region, a proportion of which have been shown to be involved in stopping, steering, and modulating locomotion. While it has been shown that these neurons integrate descending inputs, we asked whether local processing also ultimately contributes to reticulospinal outputs. Here, we define Chx10-expressing medullary reticular formation interneurons and reticulospinal neurons, and demonstrate how the former modulate the output of the latter. The results shed light on the internal organization and microcircuit formation of reticular formation neurons.

Heterosynaptic plasticity, along with Hebbian homosynaptic plasticity, is an important mechanism ensuring stable operation of learning neuronal networks. However, whether heterosynaptic plasticity occurs in the whole brain in vivo, and what role(s) in brain function in vivo it could play, remains unclear. Here, we used an optogenetics approach to apply a model of intracellular tetanization, which was established and employed to study heterosynaptic plasticity in brain slices, to study plasticity of response properties of neurons in mouse visual cortex in vivo. We show that optogenetically evoked high-frequency bursts of action potentials (optogenetic tetanization) in principal neurons of the visual cortex induce long-term changes of responses to visual stimuli. Optogenetic tetanization had distinct effects on responses to different stimuli: responses to optimal and orthogonal orientations decreased, response to null direction did not change, and responses to oblique orientations increased. As a result, direction selectivity of the neurons decreased, and orientation tuning became broader. Since optogenetic tetanization was a purely postsynaptic protocol, applied in the absence of sensory stimulation, and thus without association of presynaptic activity with bursts of action potentials, the observed changes were mediated by mechanisms of heterosynaptic plasticity. We conclude that heterosynaptic plasticity can be induced in vivo and propose that it may play important homeostatic roles in operation of neural networks by helping to prevent runaway dynamics of responses to visual stimuli and to keep the tuning of neuronal responses within the range optimized for encoding of multiple features in population activity.

The vast majority of cortical synapses are found in the neuropil which is implicated in multiple and diverse functions underlying brain computation. Unraveling the organizing principles of the cortical neuropil requires an intricate characterization of synaptic connections established by excitatory and inhibitory axon terminals, of intrinsic and extrinsic origin and from ascending projections that govern the function of cortical microcircuits through the release of neuromodulators either through point-to-point chemical synapses or diffuse volume transmission (VT). Even though neuromodulatory release has been studied for almost a century it is still not clear if one modality prevails upon the other. The hindlimb representation of the somatosensory cortex (HLS1) of two-week old Wistar rats has served as a model system to dissect the microcircuitry of neurons and their synaptic connections. In the present study, we quantified the fiber length per cortical volume and the density of varicosities for cholinergic, catecholaminergic and serotonergic neuromodulatory systems in the cortical neuropil using immunocytochemical staining and stereological techniques. Acquired data were integrated into a novel computational framework to reconcile the specific modalities and predict the effects of neuromodulatory release in shaping neocortical network activity. We predict that acetylcholine (ACh), dopamine (DA), serotonin (5-HT) release desynchronizes cortical activity by inhibiting slow oscillations (delta range), and that 5-HT triggers faster oscillations (theta). Moreover, we found that high levels (>40%) of neuromodulatory VT are sufficient to induce network desynchronization, but also that combining volume release with synaptic inputs leads to more robust and stable effects, meaning that lower levels of VT are needed to achieve the same outcome (10%).

Multi-electrode recording of neural activity in cultures offer opportunities for understanding how the structure of a network gives rise to function. Although it is hypothesized that network size is critical for determining the dynamics of activity, this relationship in human neural cultures remains largely unexplored. By applying new methods for analyzing neural activity to human iPSC derived cultures at either low-densities or high-densities, we uncovered the significant impacts that neuron number has on the individual neurophysiological properties of cells (such as firing rates), the collective behavior of the networks these cultures formed (as measured by entropy), and the relationship between the two. As a result, simply changing the densities of neurons generated dynamics and network behavior that differed not just in degree, but in kind. Beyond revealing the relationship between network structure and function, our findings provide a novel analytical framework to study diseases where network level activity is affected.

Distinct computations are performed at multiple brain regions during encoding of the spatial environments. Neural representations in the hippocampal, entorhinal and head direction (HD) networks during spatial navigation have been clearly documented, while the representational properties of the Subicular Complex (SC) network is rather unexplored, even though it has extensive anatomical connections with various brain regions involved in spatial information processing. Here, we report a global cue controlled highly coherent representation of the cue-conflict environment in the SC network, along with strong coupling between HD cells and Spatial cells. We propose that the attractor dynamics in the SC network might play a critical role in orientation of the spatial representations, thus providing a "reference map" of the environment for further processing at other networks.

Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal was to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software is released as open source to enable broad community reuse and encourage novel applications.

Gymnotiform fish emit electric organ discharges (EODs) for both active electroreception and electrocommunication. EOD waveform and rhythm can be modified to cope with diverse environmental challenges. In pulse-type species, EODs are generated by a hierarchical electromotor network controlled by a medullary pacemaker nucleus (PN), which comprises intrinsic pacemaker cells (PM-cells) and projecting relay cells (R-cells). Active electroreception requires the emission of stereotyped EODs, an electromotor output that implies a functional PN configuration in which PM-cells rhythmically time EODs and R-cells transmit coordinated commands to downstream components of the electromotor system. To test whether electrical coupling (EC) between PN neurons supports this functional organization, intrinsic connectivity of the PN in Gymnotus omarorum was examined in brainstem slices using electrophysiology, immunohistochemistry, and dye-coupling analysis. Homotypic connections (PM-PM and R-R) exhibited low-magnitude, bidirectional EC with symmetrical, low-pass filter properties, supporting synchronous yet adaptable pacemaker activity and coordinated descending commands. Heterotypic connections (PM-R) also displayed bidirectional, symmetrical coupling but revealed direction-dependent filtering: an apparent high-pass behavior from PM- to R-cells and a low-pass behavior in the opposite direction. Together with precise PM-to-R discharge timing, direction-dependent filtering suggests a role of PM-cell axons in shaping signal flow. Dye coupling and immunohistochemical evidence further indicate that PN neurons are interconnected via gap junctions, likely formed by connexin 35. Thus, EC-based connectivity endows the PN with crucial functional attributes of its exploration mode of operation while preserving the capacity to organize communication signals under the influence of descending inputs, revealing remarkable functional versatility. Summary statementGap junction-mediated intrinsic connections within the electromotor nucleus of electric fish may sustain the emission of signals essential for sensory sampling as well as those supporting communication.

Accumulating evidence suggest that general anesthetics with diverse chemical structure reduce cerebral metabolism with consequent reduction of intracellular adenosine triphosphate (ATP) levels. How cerebral hypometabolism is associated with the typical electroencephalographic (EEG) changes under general anesthesia remains largely unknown.. We hypothesized that the deficit in ATP production would reduce high-frequency activity, increase low-frequency activity, and cause burst suppression, which are common dose-dependent anesthetic effects on the EEG. To test the hypothesis, we developed a novel neural network model consisting of leaky integrate-and-fire neurons with additional dependency on ATP dynamics. The effect of varying rate of ATP production on neuronal and population activity patterns was simulated under various excitatory/inhibitory balance conditions. A decrease of ATP production suppressed neuronal spiking and enhanced synchronization of neurons over a range of excitatory/inhibitory synaptic strength ratios. As anticipated, the initially asynchronous fast activity was replaced by globally desynchronized slow oscillation and, on further decrease of ATP production, changed into burst suppression with enhanced global synchronization. This study substantiates a novel biophysical mechanism for anesthetic-induced EEG changes through a relationship between energy production and synchronization of neural network.

The zebrafish is increasingly being employed as an experimental platform to model neuropsychiatric diseases and to screen for novel neuro-active compounds. While the superb genetic and optical access that this system offers has long been recognized, these features have not been fully exploited to investigate disease mechanisms and possible therapeutic interventions. Here we introduce a light-sheet imaging and graph-theoretical analysis pipeline to determine the effects of the known or suspected antidepressant compounds fluoxetine, ketamine and cycloserine on brain-wide neural activity patterns. We imaged the brains of both wildtype fish and grs357 mutants, which harbor a missense mutation that abolishes glucocorticoid receptor transcriptional activity. The grs357 mutation results in a chronically elevated stress axis together with behavioral endophenotypes of depression. Consistent with broad expression of the glucocorticoid receptor throughout the brain, we show that the mutant fish exhibit an altered correlational structure of resting-state brain activity. Intriguingly, in grs357 mutant fish, an increased modularity, which represents the degree of segregation of the network into highly clustered modules, was restored by acute fluoxetine administration to wildtype levels. Ketamine and cycloserine also normalized specific parameters of the graph. Fluoxetine altered network function in the same direction in mutant and wildtype, while ketamine and cycloserine had effects that were opposite for the two genotypes. We propose that light-sheet imaging, followed by graph analysis, is a content-rich and scalable first-pass approach for studying the neural consequences of drug effects and drug x genotype interactions in zebrafish models of psychiatric disorders.

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.

Cultures of dissociated hippocampal neurons display a stereotypical development of network activity patterns within the first three weeks of maturation. During this process, network connections develop and the associated spiking patterns range from increasing levels of activity in the first two weeks to regular bursting activity in the third week of maturation. Characterization of network structure is important to examine the mechanisms underlying the emergent functional organization of neural circuits. To accomplish this, confocal microscopy techniques have been used and several automated synapse quantification algorithms based on (co)localization of synaptic structures have been proposed recently. However, these approaches suffer from the arbitrary nature of intensity thresholding and the lack of correction for random-chance colocalization. To address this problem, we developed and validated an automated synapse quantification algorithm that requires minimal operator intervention. Next, we applied our approach to quantify excitatory and inhibitory synaptogenesis using confocal images of dissociated hippocampal neuronal cultures captured at 5, 8, 14 and 20 days in vitro, the time period associated with the development of distinct neuronal activity patterns. As expected, we found that synaptic density increased with maturation, coinciding with increasing spiking activity in the network. Interestingly, the third week of the maturation exhibited a reduction in excitatory synaptic density suggestive of synaptic pruning that coincided with the emergence of regular bursting activity in the network.

bioRxiv has withdrawn this manuscript due to a duplicate posting of https://doi.org/10.1101/2022.04.05.487108.

Cephalopod molluscs are growing in popularity and use as comparative models of complex brains and behaviors. Although the gross anatomy of their central and peripheral nervous systems have been well characterized for decades, there is still very limited information about the diversity of cell types in each ganglion or lobe, their arrangement or their network properties. Unlike more standard neuroscience models, there are limited tools available for cephalopods and few validated techniques for imaging neural activity. Here, live calcium imaging in a reduced preparation of the stellate ganglion and mantle tissue reveals mechanosensory afferents and interneurons, which are arranged somatotopically in the ganglion. Retrograde labeling from stellate nerves confirms that neurons sending axonal projections to distinct dermatomes are organized in roughly oblong clusters along the dorsal side of the ganglion. This is the first demonstration of afferent somatotopy in cephalopods, and the first direct visualization of mechanoreceptive and mechano-nociceptive neurons that fire in response to localized, firm touch on the body surface. The methods and findings in this study open multiple new lines of enquiry related to sensory processing in the cephalopod nervous system.