Frontiers in Neural Circuits

Neuronal populations connected by gap junctions can be revealed via dye coupling of small molecules like neurobiotin and lucifer yellow. However, the extent of dye diffusion between neurons varies with connexin subtype, loading method, and neuromodulation. Due to the increasing availability of GCaMP transgenic animals, we explore the possibility of revealing gap junctional coupling using Ca2+ imaging in the Xenopus laevis tadpole motor system. Reliable axo-axonal electrical coupling was previously found in excitatory descending interneurons (dINs) using paired recordings but not with neurobiotin dye coupling. Here, we made whole-cell patch-clamp recordings with Ca2+-supplemented intracellular solution to load Ca2+ into GCaMP6s-expressing neurons, followed by Ca2+ imaging to detect potential Ca2+ diffusion across coupled neurons. Successful membrane breakthroughs led to transient fluorescence increases in the patched neuron. However, increasing the Ca2+ concentration promoted membrane resealing and rapid loss of whole-cell recordings. Regardless of recording duration, loading-triggered fluorescence only lasted up to three minutes, suggesting rapid Ca2+ clearance. Pharmacologically blocking sarcoplasmic /endoplasmic reticulum Ca2+-ATPases and plasma membrane Na+/Ca2+ exchangers did not prolong fluorescence, although sustained fluorescence was achieved with positive current injections. Counter to our expectations, fluorescence increases in Ca2+-loaded dINs did not spread to neighboring dINs. Robust intracellular Ca2+ regulation mechanisms, membrane resealing, and long dIN axons likely hindered intercellular Ca2+ diffusion. Therefore, this approach is not appropriate for revealing electrical coupling within this system.

Longitudinal brain imaging is essential for understanding neural mechanisms. Here, we present a saline-free, chronic preparation for repeated neural recording in adult Drosophila over multiple days. We describe steps for mounting flies, performing manual surgery on the head cuticle without external saline, and resealing the opening to create a transparent optical window. We demonstrate the utility of this approach by tracking single-neuron spiking and neuronal calcium dynamics over 7-10 days. This protocol is potentially applicable to other insect species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/706199v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@abeb34org.highwire.dtl.DTLVardef@deaf93org.highwire.dtl.DTLVardef@1d8fc24org.highwire.dtl.DTLVardef@91a696_HPS_FORMAT_FIGEXP M_FIG C_FIG

Understanding neuron subclasses and their functional consequences can contribute to understanding brain circuits. A scheme long used to classify GABAergic neurons in the neocortex is based on expression of three calcium-binding proteins (CBPs): parvalbumin (PV), calbindin D-28K (CB), and calretinin (CR). Because CB and CR are frequently co-expressed by individual neurons in rodents, this scheme has been replaced by one based on PV and two signaling peptides: somatostatin (SST) and vasoactive intestinal peptide (VIP). In macaques, however, CBPs are generally not co-expressed, and so their use has persisted despite suggestions that the underlying populations are not, in fact, entirely GABAergic. We set out to quantitatively evaluate CBPs as a classification scheme for GABAergic neurons in early and mid-level visual regions in macaque cortex. Combining immunohistochemistry and in situ hybridization, we find that up to half of neurons expressing CBPs are likely not GABAergic. Furthermore, contrary to what has been previously suggested, the GABAergic subpopulations cannot be distinguished based on staining intensity. Thus, the CBP-based classification scheme is not valid, at least as it has traditionally been used. Instead, we find support for co-labeling CB and CR neurons with SST and VIP, an approach that can identify GABAergic subpopulations within the CBP classes; or simply adopting the PV/SST/VIP scheme. We discuss the functional implications of expressing these various cell type markers, and how consideration of marker functions can support proper selection of a classification scheme for a given experimental purpose.

Octopus vulgaris and other cephalopods are of increasing interest as neurobiological model organisms. This protocol describes a method to record calcium activity from individual cells in acute brain slices from Octopus vulgaris hatchlings during exogenous application of neurotransmitters. Using this protocol, we characterized single-cell responses to specific neurotransmitters in the optic lobes, which process visual information. The approach is readily adaptable to other cephalopods and small invertebrate species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/711860v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1564eaeorg.highwire.dtl.DTLVardef@147b682org.highwire.dtl.DTLVardef@11f3b85org.highwire.dtl.DTLVardef@17c9d70_HPS_FORMAT_FIGEXP M_FIG C_FIG

The mosquito Aedes aegypti is an important vector of viral pathogens and serves as a model for other vector species. Pathogens are transmitted when a mosquito bites a host animal, but the neural circuits that control seeking and biting behavior are not known. Here, we detail methods and protocols for the manipulation of neural activity in the mosquito using optogenetics, a key technique to determine the causal relationship between neural circuits and behavior. These methods include rearing mosquitoes for optogenetics and three assays that are designed to measure different steps in the sequence of arousal, attraction, proboscis probing, and engorgement on the blood of host animals. These behaviors occur at different spatial scales and in response to different sensory stimuli. Each behavioral assay is outfitted with red (625 nm) LEDs for optogenetic activation. To detect arousal in response to olfactory stimuli, flight and walking are measured in all three assays. To assay attraction or landing, mosquitoes are presented with a heated blood meal in a large arena. Proboscis probing and engorgement are assayed with video resolution that enables measurement of appendages and abdomen size. The protocol describes machine vision models to enable high-resolution temporal quantification of behavior as well as endpoint measurements of feeding. These methods can be used to test the function of any population of neurons in mosquito biting behavior and can be extended to additional behaviors.

Point process generalized linear models (GLMs) have been a major tool for studying coordinated activity across populations of neurons. These models typically quantify how the spiking of a single neuron depends on the past activity of other neurons at multiple time lags, and the resulting neuron-to-neuron interactions are then aggregated to obtain population-coupling effects. However, when neurons within the same population exhibit similar spiking patterns, explicitly modeling individual interactions can be redundant and can unnecessarily increase model complexity. In such cases, population-level formulations may offer a more efficient alternative. For example, biophysical population models often characterize circuit dynamics using the average firing rate across neurons within a population, and recent data-driven approaches have similarly demonstrated the utility of population-level statistics for capturing cross-population interactions. Motivated by this consideration, we reformulate the GLM framework to operate directly at the population level. The resulting model, which we call pop-GLM, provides a computationally efficient method for estimating coupling between populations. In a simulated dataset, we show that pop-GLM achieves greater sensitivity in detecting coupling effects and can account for trial-to-trial variation in stimulus drive, which would otherwise introduce bias. We also note that moving from single-neuron to population-level modeling requires a specific modification of the traditional GLM framework. We then apply pop-GLM to real data and find reduced functional connectivity from primary visual cortex (V1) to a higher visual area during locomotion, a change not detected by single-neuron GLMs. Author summaryA central goal of systems neuroscience is to understand how multiple populations of neurons across different brain areas interact as a coordinated circuit to produce perception and behavior. We formulated and investigated a new method for estimating functional interactions between two populations of spiking neurons, and we show that it can be more sensitive and robust than previous approaches. To illustrate, we discovered decreased interaction between two mouse visual areas during locomotion, a result that previous techniques did not detect. The method should aid investigators in searching for important functional relationships across populations of neurons, with precise time scale resolution.

Anatomically and biophysically detailed models of neurons have been widely used to study information processing in these cells. Most studies focused on understanding specific phenomena, while more general models that aim to capture various cellular processes simultaneously remain rare even though such models are required to predict neuronal behavior under more complex, natural conditions. In this study, we aimed to develop a detailed, data-driven, general-purpose biophysical model of hippocampal CA1 pyramidal neurons. We leveraged extensive morphological, biophysical and physiological data available for this cell type, and established a systematic workflow for model construction and validation that relies on our recently developed software tools. The model is based on a high-quality morphological reconstruction and includes a diverse curated set of ion channel models. After incorporating the available constraints on the distribution of ion channels, the remaining free parameters were optimized using the Neuroptimus tool to fit a variety of electrophysiological features extracted from somatic whole-cell recordings. Validation using HippoUnit confirmed the models ability to replicate key electrophysiological features, including somatic voltage responses to current input, the attenuation of synaptic potentials and backpropagating action potentials, and nonlinear synaptic integration in oblique dendrites. Our model also included active dendritic spines, modeled either explicitly or by merging their biophysical mechanisms into those of the parent dendrite. We found that many aspects of neuronal behavior were unaffected by the level of detail in modeling spines, but modeling nonlinear synaptic integration accurately required the explicit modeling of spines. Our data-driven model of CA1 pyramidal cells matching diverse experimental constraints is a general tool for the investigation of the activity and plasticity of these cells and can also be a reliable component of detailed models of the hippocampal network. Our systematic approach to building and validating general-purpose models should apply to other cell types as well. Author SummaryThe brain processes information through the activity of billions of individual neurons. To understand how these cells work, scientists build detailed computer models that reproduce their electrical behavior. These models make it possible to explore situations that are difficult or impossible to test experimentally. However, many existing neuron models were designed to explain only a few specific phenomena, which limits their usefulness in more complex settings. In this study, we developed a comprehensive computer model of a hippocampal CA1 pyramidal neuron, a cell type that plays a central role in learning and memory. We built the model using extensive experimental data and applied automated methods to ensure that it reproduces a broad range of observed neuronal behaviors. We also examined how small structures called dendritic spines--tiny protrusions where most synaptic communication occurs--affect how neurons combine incoming signals. We found that even simplified models without individual spines can capture many aspects of neuronal activity, but understanding more complex forms of signal integration requires modeling spines explicitly. Our work also supports the development of more realistic simulations of brain circuits.

BackgroundFlexibility and robustness of neuronal function are closely linked to degeneracy, the ability of distinct structural or parametric configurations to produce similar functional outcomes. At the cellular level, this often manifests as ion-channel degeneracy, in which multiple combinations of intrinsic conductances yield comparable electrophysiological phenotypes. MethodologyWe used a population-based, data-driven modelling framework to generate large ensembles of biophysically detailed CA1 pyramidal neuron models constrained by somatic electrophysiological features extracted from patch-clamp recordings in acute slices from early-birth rats. 10 reconstructed morphologies were incorporated, and model populations were analyzed using parameter correlation analysis, principal component analysis, and generalization tests to assess robustness, degeneracy, and morphology dependence of intrinsic properties. ConclusionsAcross the model population, similar somatic firing behaviours emerged from widely different combinations of intrinsic parameters, demonstrating robust two-level ion channel degeneracy both within and across morphologies. Each morphology occupied a distinct region of parameter space, indicating morphology-specific compensatory effects, while weak pairwise parameter correlations suggested distributed compensation rather than tight parameter dependencies. Even with a fixed morphology, multiple parameter subspaces supported comparable electrophysiological phenotypes. Generalization across morphologies was structure-dependent and non-reciprocal, with successful parameter similarity occurring preferentially between structurally similar neurons. Interestingly, to accurately simulate spike-frequency adaptation, it was important to retain some kinetic properties of the ion channel models as free parameters during optimization. Together, these findings show that dendrite morphology shapes the valid parameter space, and similar electrophysiology of CA1 pyramidal neurons arises from the interplay between structural variability and ion-channel diversity. This work highlights the importance of population-based modelling for capturing biological variability and provides insights into how neuronal robustness might be maintained despite substantial heterogeneity, and offers a scalable pipeline for generating biophysically realistic CA1 neuron populations for use in network simulations. Author summaryNeurons must reliably process information even though their internal components, such as ion channels and cellular shape, can vary widely from cell to cell. How stable behaviour emerges from such variability is a fundamental question in neuroscience. In this study, we explored this problem using detailed computer models of early-birth rat hippocampal CA1 pyramidal neurons, a cell type that plays a central role in learning and memory. Instead of building a single "average" neuron model, we created large populations of models that all reproduced key experimental recordings but differed in their internal parameters. We found that neurons with different shapes and different combinations of ion channels could nevertheless generate similar electrical activity. This phenomenon, known as ion channel degeneracy, allows neurons to remain functional despite biological variability or perturbations. Our results show that neuronal shape strongly influences which parameter combinations are viable, but that multiple solutions exist even for the same morphology. The population of models we provide offers a resource for future studies of early-birth CA1 pyramidal cell function and dysfunction.

A central question in neuroscience is how neural processing generates or encodes behavior. Caenorhabditis elegans is well suited to addressing this question, given its compact nervous system and near-complete structural connectome. Despite this, findings from previous studies remain inconclusive. While some have shown that the connectome can robustly encode specific behaviors such as locomotion, others report that functional connectivity can be reconfigured across behaviors. We aim to understand the relationship between structural connectivity, functional connectivity and biological behavior in silico by using an experimentally motivated computational model leveraging the structural connectome. Stimulation of specific neurons in the model induces oscillatory neural responses, enabling us to infer neuronal functional connectivity. Functional connectivity is found to be stronger among some neurons, allowing us to identify functional communities. We find that electrical synapses play a critical role in determining functional communities, and the resulting mesoscale functional architecture is predominantly gap junctionally assortative. Furthermore, comparison with behavioral circuits shows that locomotion circuits are largely segregated into distinct functional communities while other circuits are more distributed across multiple functional communities. We also observe that stimulation of neurons belonging to these distributed circuits elicits a more synchronized neuronal response compared to stimulation of neurons within the more segregated circuits. This is consistent with the presence of behavioral patterns that originate in one circuit and terminate in another (e.g., chemosensation leading to locomotion), such that stimulation of one circuit can activate the other and eventually result in a synchronized response. We also find a large repertoire of chimera-like synchronization patterns upon stimulation of certain behavioral circuits (chemosensation, mechanosensation) indicating high dynamical flexibility. Overall, our results demonstrate that while certain behaviors are governed by functionally segregated circuits, others emerge from the synchronization of multiple functional communities, which are, to begin with, influenced by the underlying structural connectivity. Author summaryAnimals constantly transform sensory inputs into actions, but it is still unclear how this mapping from neural activity to behavior is implemented in a real nervous system. Caenorhabditis elegans offers a unique testbed for this question because its entire wiring diagram is nearly completely mapped. Yet, previous works have reached mixed conclusions about how well this anatomical circuit diagram predicts actual patterns of activity and behavior. Here, we use a biologically inspired computational model of the C. elegans nervous system to bridge this gap between structure, function, and behavior. By virtually stimulating individual neurons and observing the resulting network-wide oscillations, we infer how strongly different pairs and groups of neurons interact in functional terms. We then use network analysis tools to identify groups of neurons that tend to co-activate, and relate these functional communities to known behavioral circuits for locomotion and sensory processing. We find that gap junctions play a key role in shaping functional communities, and that locomotion-related neurons are more functionally segregated than neurons involved in other behaviors, which are more functionally distributed. Our results suggest that some behaviors rely on specialized, functionally isolated circuits, whereas others emerge from the coordinated activity of multiple functional communities.

The functional organization of the teleost telencephalic pallium remains poorly understood, particularly regarding the presence of modality-specific sensory domains and their topographic arrangement. Here, we used in vivo wide-field voltage-sensitive dye imaging to map sensory-evoked neural activity across the dorsal surface of the telencephalic pallium of adult goldfish. Somatosensory, auditory, gustatory, and visual stimulation revealed distinct, modality-specific domains primarily located within the dorsomedial (Dm) and dorsolateral (Dl) pallium. Within Dm, somatosensory and auditory stimuli activated partially overlapping territories in the caudal subregion (Dm4), exhibiting clear somatotopic and tonotopic organization along the mediolateral axis. Gustatory stimulation selectively engaged Dm3, where different tastants activated spatially distinct but partially overlapping domains. A more rostral subregion (Dm2) responded only to high-intensity somatosensory stimulation, suggesting involvement in processing negatively valenced inputs. Visual stimulation activated a circumscribed area within the dorsolateral pallium (Dld2),that closely matched cytoarchitectural boundaries. Pharmacological blockade of ionotropic glutamate receptors markedly reduced sensory-evoked responses, indicating that these maps depend on glutamatergic synaptic transmission. Together, these findings show that the goldfish pallium contains distinct, spatially organized sensory representations and a refined internal functional architecture. This organization suggests that pallial topographic sensory maps may not be exclusive to mammals and birds. Based on these results, we propose that dorsomedial and dorsolateral pallial regions may be functionally comparable to components of the mammalian mesocortical network, more than to the pallial amygdala or the neocortex. This framework provides a new perspective on pallial organization in teleosts and contributes to understanding the evolutionary origins of the vertebrate pallium. HIGHLIGHTSO_LIVoltage-sensitive dye imaging was used to map sensory responses in the goldfish pallium. C_LIO_LIDistinct sensory areas for somatosensory, auditory, gustatory, and visual modalities were identified. C_LIO_LISome sensory regions in Dm show topographically organized maps. C_LIO_LIFunctional segregation suggests a complex, non-diffuse pallial organization. C_LIO_LIFindings support a novel hypothesis linking Dm and Dld to mammalian mesocortical regions. C_LI

Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come from soft-science backgrounds (like medicine, biology, or psychology) and do not have enough quantitative skills to understand the inner workings of A/CNNs. To avoid undesirable black boxes, neuroscientists should acquire some rudiments of computational neuroscience and machine learning. However, most researchers do not have the time nor the resources to make big learning investments, and self-study materials are hardly tailored to people with little mathematical background. This paper aims to fill this gap by providing a concise but accurate introduction to CNNs and their use in neuroscience -- using the minimum required mathematics, neuroscientific analogies, and Python code examples. A companion Jupyter Notebook guides readers through code examples, translating theory into practice and providing visual outputs. The paper is organised in three sections: The Concepts, The Implementation, and The Biological Plausibility of A/CNNs. The three sections are largely independent, so readers can either go through the entire paper or select a section of interest.

The primate retina dissects visual scenes into multiple retinocortical streams. The most numerous retinal ganglion cell (GC) types, midget and parasol cells, are further divided into ON and OFF subtypes. These four GC populations have anatomical and physiological asymmetries, which are reflected in the spike trains received by downstream circuits. Computational models of the visual cortex, however, rarely take GC signal processing into account. We have built a macaque retina simulator with the aim of providing biologically plausible spike trains for downstream visual cortex simulations. The simulator is based on realistic sampling density and receptive field size as a function of eccentricity, as well as on two distinct spatial and three temporal receptive field models. Starting from data from literature and earlier receptive field measurements, we synthetize distributions for receptive field parameters, from which the synthetic units are sampled. The models are restricted for monocular and monochromatic stimuli and follow data from the temporal hemiretina which is more isotropic. We show that the model patches conform to anatomical data not used in the reconstruction process and characterize the responses with respect to spatial and temporal contrast sensitivity functions. This simulator allows starting from a stimulus video and provides biologically plausible spike trains for the distinct unit types. This supports development of thalamocortical primate model systems of vision. In addition, it can provide a reference for more biophysical retina models. The independent parameters are housed in text files supporting reparameterization for particular macaque data or other primate species. Author summaryVisual environment provides a rich source of information, and the visual system structure and function has been studied for decades in many species, including humans. The most complex data in mammalian species are processed in the cerebral cortex, but to date we are still missing a functioning model of cortical computations. While the earlier anatomical and physiological data describe many details of the visual system, to understand the functional logic we need to numerically simulate the complex interactions within this system. To pave the way for simulating visual cortex computations, we have developed a functioning model for macaque retina. The neuroinformatics comprises a review and re-digitized existing retina data from literature, as well as statistics of earlier macaque receptive field data. Finally, we provide software which brings the collected neuroinformatics to life and allows researchers to convert visual input into biologically feasible spike trains for simulation experiments of visual cortex.

We propose a quality measure for spatio-temporal spike patterns (STPs) in multiple-neuron recordings. In such recordings, repeating STPs or pattern repetitions (PRs) are often found, with many of these generated by chance. To rule those out, statistical tests have been developed to discriminate the unlikely from the more likely PRs. This statistical problem is complicated by the fact that there are several obvious quality criteria for a PR, such as the size (the number of spikes) of the pattern and the number of its occurrences. Here, we propose a canonical way of combining several criteria (which we collect in the so-called signature of the pattern) into a single quality measure, based on the unlikeliness of the pattern. This measure is defined mathematically, and a formula for its computation is derived for stationary spike trains. It can be used to compare PRs. Since spike trains are not stationary in practice, we discuss, for two experimental data sets, how well the stationary formula correlates with the defined quality measure as determined from simulations. The results encourage the use of the stationary formula or also some simpler, related formulas as proxies for the quality, for the comparison of PRs and also for statistical tests that avoid the multiple testing problem incurred by using several quality criteria. Based on our results, we propose a few test statistics, i.e., random variables on the space of multi-unit spike trains with an appropriate null-hypothesis distribution, to evaluate STPs with less computational and sampling efforts.

Left-right asymmetry of the brain is well recognized in various animals including C. elegans, drosophila and zebrafish. In primates, most of the brain studies describe side of the brain. However, in spite of huge amounts of accumulating rodent studies on neuroscience, most of rodent studies do not distinguish the brain side. The pig brain is considered to occupy an intermediate position between primates and rodents in terms of structural complexity and brain function. Moreover, the numbers of studies using genetic manipulation of pigs are drastically increasing. So, we investigated microminipig (MMP) brain mesoscopic anatomy focusing on left-right differences of its morphology. Here, we show the anterior cingulate cortex, perirhinal cortex, and cerebellum of male and female MMPs, are structurally asymmetrical. The cerebellar vermis, paravermis is tilted from the midline and the consequently the cerebellar cortex exhibits asymmetrical morphology. The anterior cingulate gurus exhibited protrusion and invagination toward the midline on the right and left side, respectively. The left perirhinal lobe exhibited distinct patterns of cortical gyration between left and right side. These data demonstrate that MMPs are one of the suitable model animals for investigating cerebral and cerebellar asymmetry.

Identifying the location of a sound source in a complex environment and assessing its importance can be crucial for survival. The superior colliculus (SC), a midbrain structure involved in sensorimotor functions, contributes to sound localization and contains auditory responsive neurons that have spatially restricted receptive fields (RFs) that are organized into a topographic map along the azimuth. However, individual auditory SC neurons have large spatial RFs, are noisy, and do not respond to the same stimulus at each trial. Therefore, when an animal is presented with a "single trial" sound, and it needs to rely on a single neuron to locate the sound source direction, the location measurement may be erroneous, missing, or have poor spatial resolution. It is expected that a more reliable and accurate determination of the sound source location will come from a population of neurons. We therefore built a population pattern Maximum Likelihood Estimation (MLE) decoder to build a model that can accurately predict the location of a stimulus given the population response. We compared three models that use either strict firing rate (FR), weighting based on equal (EW) or mutual information (MIW) and show that the MIW model works best, needing only 92 neurons to localize a stimulus with behaviorally relevant precision. Furthermore, by comparing the models fit using the responses from non-RF and RF auditory neurons, we show that only RF neurons contain the information needed to localize a sound source. These results are consistent with the hypothesis that the SC uses a population of RF neurons to determine sound source location. Author SummaryBeing able to tell where a sound is coming from and how important it is can be critical for survival. The superior colliculus, a midbrain region involved in orienting behaviors, contains neurons that respond best to sounds coming from specific locations. This suggests that the combined activity of many neurons in the SC is used to determine sound location from a single sound event. To test this idea, we modeled responses from mouse SC neurons while sounds were played from different positions in space, both along the elevation and horizon. A model that weighted the most informative neurons performed best in both directions needing only 92 neurons to localize a stimulus with behaviorally relevant precision along the azimuth. Comparing the models fit using the responses from non-RF and RF auditory neurons, we show that only RF neurons contain the information needed to localize a sound source Overall, our findings show that the SC can accurately locate sounds in both horizontal and vertical space using a population-based strategy, providing a simple and effective solution for rapid sound localization.

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

Seizure propagation - how epileptogenic brain regions recruit less excitable regions - is poorly understood. Previous studies have used dynamical modeling to study seizure propagation and to create patient-specific whole-brain models of seizure spread. However, these studies focused on seizures of a single dynamotype (onset and offset bifurcation pair). Here, we implement a novel coupling method to investigate seizure propagation in a diverse array of dynamotypes. We utilize the Multiclass Epileptor, a recently proposed model that captures a wide range of seizure dynamotypes in a cortical mass ("node"). We consider two nodes: the seizure onset zone (node 1), which bursts autonomously, and the potential propagation zone (node 2), which is not independently epileptogenic but can be recruited by node 1. We examine the impact of intrinsic and coupling factors on the likelihood and speed of recruitment, with particular attention to the onset bifurcation of node 1. We also measure the range of onset behaviors observed in node 2 with respect to the onset behavior of node 1. The model predicted that seizures that display baseline shifts at onset are less likely to spread, and spread more slowly, compared to seizures that do not exhibit baseline shifts at onset. Seizures that present with amplitude scaling at onset were unlikely to propagate. Further, the model predicted the potential for unusual combinations of onset dynamics, such as a baseline shift in node 2 but not node 1. We confirmed the possibility for several of these unusual recruitment behaviors in humans using intracranial electroencephalography data. The results of the study provide a theoretical framework for seizure propagation, establishing a basis for innovations in characterization of patients seizure networks and identification of the seizure onset zone. Author SummaryIn this work, we examined how a seizure spreads from one part of the brain to another using a computational model. We modeled two brain regions using the Multiclass Epileptor, which reproduces a range of brain activity patterns associated with seizures. In the model, the first brain node was able to recruit the second brain node into a seizure. The model predicted that the likelihood and speed of seizure spread differ depending on the pattern of brain activity observed at the start of the seizure. We also found that the pattern of brain activity at seizure onset is not necessarily the same pattern seen when the seizure spreads. We confirmed this possibility for mismatched patterns in recordings from human brain. The findings of the study improve our understanding of seizure spread, which lays the groundwork for development of tools to quantify seizure spread and may inform future work in patient-specific brain modeling.

Neurons couple to various degrees to the activity level of the local neighboring population whereby strongly coupled choristers and weakly coupled soloists have been identified as two extremes of a continuous spectrum. At the same time neuronal populations undergo coordinated ON and OFF cortical state activity fluctuations, which are locally modulated by attention. The population coupling of soloists and choristers suggests that soloists should show limited alignment with cortical state fluctuations, while choristers should exhibit profound alignment. To test this, we recorded neurons across cortical layers in macaque areas V1 and V4, while animals performed a feature based spatial attention task. As expected, we found a wide range of population coupling strength of neurons. In line with our prediction, coupling of choristers to cortical state changes (ON-OFF transitions) was generally stronger than that of soloists. The strength of population coupling of neurons was similar during spontaneous and stimulus driven activity. Allocation of attention to the receptive field reduced the population coupling strength. Attentional modulation of neurons was positively correlated with population coupling strength. While neurons on average retained their coupling strengths across conditions, some neurons change coupling strength condition dependent, thereby potentially enhancing the coding abilities of cortical circuits.

BackgroundHippocampal place cells (PCs) undergo representational drift, i.e., a gradual change in their place fields despite unaltered behavior. While Ca2+ imaging enables long-term tracking of PC populations, distinct PC detection methods have been shown to yield different subpopulations of PCs, with only a few systematic comparisons between methods, especially in open arenas. New MethodWe provide an analysis protocol for one-photon PC data obtained during free foraging in two-dimensional arenas that allows us to compare two widely used PC detection methods, significance of spatial information (SI), and split-half correlation (SHC), and their effect on representational drift. The analysis is demonstrated on previously published Ca2+ data from dorsal CA1 of freely foraging mice, with cells tracked for 10 consecutive days. ResultsBoth criteria, SI and SHC, yielded proportions of approx. 17% PCs with only 40% overlap. SI-identified PCs demonstrated higher stability, higher rate map correlations, and a slower rate of representational drift than SHC-PCs. Comparison with existing methodsPrevious studies comparing SI and SHC PC detection methods in Ca2+ data did not focus on either open field behavior or representational drift. ConclusionOur results indicate that the choice of PC detection method significantly affects the estimate of representational drift in Ca2+ imaging studies.

ContextGrid cells in the medial entorhinal cortex (MEC) of head-fixed mice exhibit ultraslow (<0.01 Hz) oscillations (USO) during walking in a 1D running wheel in darkness. It was proposed that these oscillations may have a connection with navigational behavior. ProblemThere is no clear link between the functional role of these oscillations and path integration, a fundamental navigation strategy used by animals to calculate their current position and orientation by continuously summing self-motion cues. HypothesisGiven the synaptic projections from MEC to the hippocampus, we hypothesized that ultraslow oscillations have a role in linking spatiotemporal memories acquired during navigation. MethodologyA realistic computational model of entorhinal-grid with ultraslow oscillations and hippocampal-place cells is proposed using synaptic plasticity between cell types, sustaining path integration of a rodent-like simulated animal. ResultsUltraslow oscillations induced persistent changes in the grid cell dynamics, represented as a positional drift of grid fields. Such drift resulted in position estimation errors but generated new grid-place cell associations when combined with synaptic plasticity. >DiscussionsUltraslow entorhinal oscillations were found to shape spatial memory through grid cell drifting, which could serve as a mechanism for flexibly accessing different spatial memories during navigation. HIGHLIGHTSO_LIPath integration dynamics hide ultraslow oscillations despite coexistence. C_LIO_LIUltraslow oscillations significantly degrade position estimation in path integration. C_LIO_LIGrid and place fields drift after the effect of ultraslow oscillations. C_LIO_LINew spatial memories were created as a result of the ultraslow oscillation drift. C_LIO_LIUltraslow oscillations enable dynamic access of different spatial memories C_LI