Detecting pairwise correlations in spike trains: an objective comparison of methods and application to the study of retinal waves

Effectiveness of aperiodic retinal stimulation in improving temporal visual cortical response

Objective.Visual prostheses can provide partial visual function in patients with retinal degenerative diseases. However, in clinical trials, patients implanted with retinal prostheses have reported perceptual fading, which is thought to be related to response desensitization. Additionally, natural stimuli consist of aperiodic events across a short temporal span, whereas periodic stimulation (fixed inter-pulse intervals (IPIs)) is the standard approach in retinal prosthesis research. In this study, we investigated how aperiodic stimulation of the epiretinal surface affects electrically evoked responses in the primary visual cortex (V1) compared with periodic stimulation.Approach. In vivoexperiments were conducted in healthy and retinal-degenerated rats. Periodic stimulation consisted of constant IPIs, whereas aperiodic stimulation was provided by mixed IPIs. We calculated the spike time tiling coefficient to assess response consistency across trials, the significant response ratio, and the spike rate to analyze response desensitization.Main results.The results showed a significantly lower consistency of cortical responses in retinal degenerated rats than in healthy rats at 5 Hz. The consistency of the response to periodic stimulation decreased considerably as the frequency was increased to 10 Hz and 20 Hz in both groups and was greatly improved by applying aperiodic stimulation. In addition, aperiodic stimulation evoked a significantly higher spike rate in response to continuous stimulation at high frequencies (e.g. 10 and 20 Hz).Significance. By applying electrical stimulation with varying IPIs directly on the epiretinal surface, we observed promising results in terms of enhancing cortical response consistency and reducing desensitization. This finding presents a potential approach to enhance the effectiveness of retinal prostheses.

Retinal degeneration increases inter-trial variabilities of light-evoked spiking activities in ganglion cells

Retinal ganglion cells (RGCs) transmit visual information to the brain in the form of spike trains, which form visual perception. The reliabilities of spike timing and count are thought to play a crucial role in generating stable percepts. However, the effect of retinal degeneration on spike reproducibility remains underexplored. In this study, we examined longitudinal changes of both spike timing and count across different RGC types in response to repeated presentations of an identical light stimulus in retinal degeneration 10 (rd10) mice (B6.CXBl-Pde6brd10/J), a well-established model of retinitis pigmentosa (RP). We recorded the spiking responses of RGC populations to repeated white flashes using 256-channel multi-electrode array (MEA) at four rd10 age groups representing various stages of retinal degeneration. Our experimental results revealed a significant reduction in both spike timing and count consistencies compared to those in wild-type RGC recordings. Furthermore, the inter-trial variability patterns of different RGC types were found to differ throughout the degeneration process. For instance, when the spike time tiling coefficient (STTC) was used to evaluate inter-trial spike timing consistency, contrast-sensitive RGCs (ON, OFF, and ON-OFF types) exhibited a systematic decrease in spike timing consistency as degeneration progressed, whereas the remaining units did not show similar trends. Thus, we concluded that light-evoked spike trains become less consistent as degeneration progresses, with variability in spike timing and spike count varying across cell types. Given the critical role of spiking reliability in visual perception, our findings highlight the importance of accounting for cell type-specific degeneration patterns and inter-trial spiking inconsistencies when developing visual rehabilitation therapies to achieve enhanced performance. The underlying mechanism(s) driving the inter-trial spiking inconsistencies warrant further investigation.

Pathological microcircuits and epileptiform events in patient hippocampal slices

How seizures begin at the level of microscopic neuronal circuits remains unknown. Advancements in high-density CMOS-based microelectrode arrays can be harnessed to study neuronal network activity with unprecedented spatial and temporal resolution. We use high-density electrophysiology recordings to probe the network activity of human hippocampal brain slices from six patients with mesial temporal lobe epilepsy. Two slices from the dentate gyrus exhibited epileptiform activity in the presence of low magnesium media with kainic acid. Both slices exhibit network oscillations indicative of a reciprocally connected circuit, which is unexpected under normal physiological conditions. Future studies may apply this approach to elucidate the network signals that underlie seizure initiation.

Insulative Compression of Neuronal Tissues on Microelectrode Arrays by Perfluorodecalin Enhances Electrophysiological Measurements

Microelectrode array (MEA) techniques provide a powerful method for exploration of neural network dynamics. A critical challenge is to interface 3D neural tissues including neural organoids with the flat MEAs surface, as it is essential to place neurons near to the electrodes for recording weak extracellular signals of neurons. To enhance performance of MEAs, most research have focused on improving their surface treatment, while little attention has been given to improve the tissue-MEA interactions from the medium side. Here, a strategy is introduced to augment MEA measurements by overlaying perfluorodecalin (PFD), a biocompatible fluorinated solvent, over neural tissues. Laying PFD over cerebral organoids insulates and compresses the tissues on MEA, which significantly enhances electrophysiological recordings. Even subtle signals such as the propagation of action potentials in bundled axons of motor nerve organoids can be detected with the technique. Moreover, PFD stabilizes tissues in acute recordings and its transparency allows optogenetic manipulations. This research highlights the potential of PFD as a tool for refining electrophysiological measurements of in vitro neuronal cultures. This can open new avenues to leverage precision of neuroscientific investigations and expanding the toolkit for in vitro studies of neural function and connectivity.

STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity

BACKGROUND

In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods.

NEW METHOD

Four time-scale adaptive performance and similarity measures are proposed and implemented in the STSimM (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains.

RESULTS

The proposed ST-measures demonstrate enhanced sensitivity over Spike-contrast and SPIKE-distance in detecting spike train similarity, aligning closely with SPIKE-synchronization. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization.

COMPARISON OF EXISTING METHOD

The STSimM measures are compared with SPIKE-distance, SPIKE-synchronization and Spike-contrast using four spike train datasets with varying similarity levels.

CONCLUSION

ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures - ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore - capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods.

Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes

We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity by contrasting its higher-order structure to a random control and confirmed its relevance in several openly available connectomes. Using a biologically detailed cortical model and an electron microscopic dataset, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neural networks, due to the heterogeneity of their connectivity.

Emotions in multi-brain dynamics: A promising research frontier

Emotions drive and influence social interactions. Actions and reactions driven by emotions are dynamically modulated by continuous feedback loops between all interacting subjects. In this framework, interacting brains operate as an integrated system, with neural dynamics coevolving over time. Neuronal synchronization across brains has been observed in a range of species, including humans, monkeys, bats, and mice. This inter-neural synchrony (INS) has been proposed as a potential mechanism facilitating social interaction by enabling the functional integration of multiple brains. However, the role of emotions in modulating these processes remains underexplored and warrants further investigation. Here we provide a brief overview of studies on inter-neural synchrony in humans and other species, emphasizing the critical role that emotions might play in shaping multibrain dynamics.

SLC35A2 loss of function variants affect glycomic signatures, neuronal fate, and network dynamics

SLC35A2 encodes a UDP-galactose transporter essential for glycosylation of proteins and galactosylation of lipids and glycosaminoglycans. Germline genetic SLC35A2 variants have been identified in congenital disorders of glycosylation and somatic SLC35A2 variants have been linked to intractable epilepsy associated with malformations of cortical development. However, the functional consequences of these pathogenic variants on brain development and network integrity remain elusive. In this study, we use an isogenic human induced pluripotent stem cell-derived neuron model to comprehensively interrogate the functional impact of loss of function variants in SLC35A2 through the integration of cellular and molecular biology, protein glycosylation analysis, neural network dynamics, and single cell electrophysiology. We show that loss of function variants in SLC35A2 result in disrupted glycomic signatures and precocious neurodevelopment, yielding hypoactive, asynchronous neural networks. This aberrant network activity is attributed to an inhibitory/excitatory imbalance as characterization of neural composition revealed preferential differentiation of SLC35A2 loss of function variants towards the GABAergic fate. Additionally, electrophysiological recordings of synaptic activity reveal a shift in excitatory/inhibitory balance towards increased inhibitory drive, indicating changes occurring specifically at the pre-synaptic terminal. Our study is the first to provide mechanistic insight regarding the early development and functional connectivity of SLC35A2 loss of function variant harboring human neurons, providing important groundwork for future exploration of potential therapeutic interventions.

Multiscale Cloud-Based Pipeline for Neuronal Electrophysiology Analysis and Visualization

Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.

Goal-Directed Learning in Cortical Organoids

Experimental neuroscience techniques are advancing rapidly, with major recent developments in high-density electrophysiology and targeted electrical stimulation. In combination with these techniques, cortical organoids derived from pluripotent stem cells show great promise as in vitro models of brain development and function. Although sensory input is vital to neurodevelopment in vivo , few studies have explored the effect of meaningful input to in vitro neural cultures over time. In this work, we demonstrate the first example of goal-directed learning in brain organoids. We developed a closed-loop electrophysiology framework to embody mouse cortical organoids into a simulated dynamical task (the inverted pendulum problem known as 'Cartpole') and evaluate learning through high-frequency training signals. Longitudinal experiments enabled by this framework illuminate how different methods of selecting training signals enable improvement on the tasks. We found that for most organoids, training signals chosen by artificial reinforcement learning yield better performance on the task than randomly chosen training signals or the absence of a training signal. This systematic approach to studying learning mechanisms in vitro opens new possibilities for therapeutic interventions and biological computation.

Quantitative analyses of how optimally heterogeneous neural codes maximize neural information in jittery transmission environments

Various spike patterns from sensory/motor neurons provide information about the dynamic sensory stimuli. Based on the information theory, neuroscientists have revealed the influence of spike variables on information transmission. Among diverse spike variables, inter-trial heterogeneity, known as jitter, has been observed in physiological neuron activity and responses to artificial stimuli, and it is recognized to contribute to information transmission. However, the relationship between inter-trial heterogeneity and information remains unexplored. Therefore, understanding how jitter impacts the heterogeneity of spiking activities and information encoding is crucial, as it offers insights into stimulus conditions and the efficiency of neural systems. Here, we systematically explored how neural information is altered by number of neurons as well as by each of three fundamental spiking characteristics: mean firing rate (MFR), duration, and cross-correlation (spike time tiling coefficient; STTC). First, we generated groups of spike trains to have specific average values for those characteristics. Second, we quantified the transmitted information rate as a function of each parameter. As population size, MFR, and duration increased, the information rate was enhanced but gradually saturated with further increments in number of cells and MFR. Regarding the cross-correlation level, homogeneous and heterogeneous spike trains (STTCAVG = 0.9 and 0.1) showed the lowest and highest information transmission, respectively. Interestingly however, when jitters were added to mimic physiological noisy environment, the information was reduced by ~ 46% for the spike trains with STTCAVG = 0.1 but rather substantially increased by ~ 63% for the spike trains with STTCAVG = 0.9. Our study suggests that optimizing various spiking characteristics may enhance the robustness and amount of neural information transmitted.

Modeling early phenotypes of Parkinson's disease by age-induced midbrain-striatum assembloids

Parkinson's disease, an aging-associated neurodegenerative disorder, is characterised by nigrostriatal pathway dysfunction caused by the gradual loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain. Human in vitro models are enabling the study of the dopaminergic neurons' loss, but not the dysregulation within the dopaminergic network in the nigrostriatal pathway. Additionally, these models do not incorporate aging characteristics which potentially contribute to the development of Parkinson's disease. Here we present a nigrostriatal pathway model based on midbrain-striatum assembloids with inducible aging. We show that these assembloids can develop characteristics of the nigrostriatal connectivity, with catecholamine release from the midbrain to the striatum and synapse formation between midbrain and striatal neurons. Moreover, Progerin-overexpressing assembloids acquire aging traits that lead to early neurodegenerative phenotypes. This model shall help to reveal the contribution of aging as well as nigrostriatal connectivity to the onset and progression of Parkinson's disease.

MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.

Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavor. Here, specially designed graphene microelectrode arrays (G-MEAs) are used, which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, an easy-to-implement machine learning algorithm is developed to efficiently process the large datasets collected from MEA recordings. It is demonstrated that the combined use of G-MEAs, machine learning (ML) spike analysis, and 4D structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which are treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signaling capacity.

Nanomagnetic Guidance Shapes the Structure-Function Relationship of Developing Cortical Networks

In this study, we implement large-scale nanomagnetic guidance on cortical neurons to guide dissociated neuronal networks during development. Cortical networks cultured over microelectrode arrays were exposed to functionalized magnetic nanoparticles, followed by magnetic field exposure to guide neurites over 14 days in vitro. Immunofluorescence of the axonal protein Tau revealed a greater number of neurites that were longer and aligned with the nanomagnetic force relative to nonguided networks. This was further confirmed through brightfield imaging on the microelectrode arrays during development. Spontaneous electrophysiological recordings revealed that the guided networks exhibited increased firing rates and frequency in force-aligned connectivity identified through Granger Causality. Applying this methodology across networks with nonuniform force directions increased local activity in target regions, identified as regions in the direction of the nanomagnetic force. Altogether, these results demonstrate that nanomagnetic forces guide the structure and function of dissociated cortical neuron networks at the millimeter scale.

Engineered modular neuronal networks-on-chip represent structure-function relationship

Brain function is substantially linked to the highly organized modular structure of neuronal networks. However, the structure of in vitro assembled neuronal circuits often exhibits variability, complicating the consistent recording of network functional output and its correlation to network structure. Therefore, engineering neuronal structures with predefined geometry and reproducible functional features is essential to precisely model in vivo neuronal circuits. Here, we engineered microchannel devices to assemble 2D and 3D modular networks. The microchannel devices were coupled with a multi-electrode array (MEA) electrophysiology system to enable recordings from circuits. Each network consisted of 64 modules connected to their adjacent modules by micron-sized channels. Modular circuits within microchannel devices showed enhanced activity and functional connectivity traits. This includes metrics such as connection weights, clustering coefficient, global efficiency, and the number of hub neurons with higher betweenness centrality. In addition, modular networks demonstrated an increased functional modularity score compared to the randomly formed circuits. Neurons within individual modules displayed uniform network characteristics and predominantly participated in their respective functional communities within the same or neighboring physical modules. These observations highlight that the modular network structure promotes the development of segregated functional connectivity traits while simultaneously enhancing the efficiency of overall network connectivity. Our findings emphasize the significant impact of physical constraints on the activity patterns and functional organization within engineered modular networks. These circuits, characterized by stable modular architecture and intricate functional dynamics-key features of the brain networks-offer a robust in vitro model for advancing neuroscience research.

Functional Neural Networks in Human Brain Organoids

Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological, psychiatric, and developmental disorders. While the molecular and cellular aspects of human brain organoids have been intensively studied, their functional properties such as organoid neural networks (ONNs) are largely understudied. Here, we summarize recent research advances in understanding, characterization, and application of functional ONNs in human brain organoids. We first discuss the formation of ONNs and follow up with characterization strategies including microelectrode array (MEA) technology and calcium imaging. Moreover, we highlight recent studies utilizing ONNs to investigate neurological diseases such as Rett syndrome and Alzheimer's disease. Finally, we provide our perspectives on the future challenges and opportunities for using ONNs in basic research and translational applications.

Modeling seizure networks in neuron-glia cultures using microelectrode arrays

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 % of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks in vitro. As such, we sought to develop a novel in vitro MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2 days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

Cellular and circuit features distinguish dentate gyrus semilunar granule cells and granule cells activated during contextual memory formation

The dentate gyrus is critical for spatial memory formation and shows task related activation of cellular ensembles considered as memory engrams. Semilunar granule cells (SGCs), a sparse dentate projection neuron subtype distinct from granule cells (GCs), were recently reported to be enriched among behaviorally activated neurons. However, the mechanisms governing SGC recruitment during memory formation and their role in engram refinement remains unresolved. By examining neurons labeled during contextual memory formation in TRAP2 mice, we empirically tested competing hypotheses for GC and SGC recruitment into memory ensembles. In support of the proposal that more excitable neurons are preferentially recruited into memory ensembles, SGCs showed greater sustained firing than GCs. Additionally, SGCs labeled during memory formation showed less adapting firing than unlabeled SGCs. Our recordings did not reveal glutamatergic connections between behaviorally labeled SGCs and GCs, providing evidence against SGCs driving local circuit feedforward excitation in ensemble recruitment. Contrary to a leading hypothesis, there was little evidence for individual SGCs or labeled neuronal ensembles supporting lateral inhibition of unlabeled neurons. Instead, pairs of GCs and SGCs within labeled neuronal cohorts received more temporally correlated spontaneous excitatory synaptic inputs than labeled-unlabeled neuronal pairs, validating a role for correlated afferent inputs in neuronal ensemble selection. These findings challenge the proposal that SGCs drive dentate GC ensemble refinement, while supporting a role for intrinsic active properties and correlated inputs in preferential SGC recruitment to contextual memory engrams.

Impact Statement

Evaluation of semilunar granule cell involvement in dentate gyrus contextual memory processing supports recruitment based on intrinsic and input characteristics while revealing limited contribution to ensemble refinement.

Transcranial focused ultrasound activates feedforward and feedback cortico-thalamo-cortical pathways by selectively activating excitatory neurons

Transcranial focused ultrasound stimulation (tFUS) has been proven capable of altering focal neuronal activities and neural circuits non-invasively in both animals and humans. The abilities of tFUS for cell-type selection within the targeted area like somatosensory cortex have been shown to be parameter related. However, how neuronal subpopulations across neural pathways are affected, for example how tFUS affected neuronal connections between brain areas remains unclear. In this study, multi-site intracranial recordings were used to quantify the neuronal responses to tFUS stimulation at somatosensory cortex (S1), motor cortex (M1) and posterior medial thalamic nucleus (POm) of cortico-thalamo-cortical (CTC) pathway. We found that when targeting at S1 or POm, only regular spiking units (RSUs, putative excitatory neurons) responded to specific tFUS parameters (duty cycle: 6%-60% and pulse repetition frequency: 1500 and 3000 Hz ) during sonication. RSUs from the directly connected area (POm or S1) showed a synchronized response, which changed the directional correlation between RSUs from POm and S1. The tFUS induced excitation of RSUs activated the feedforward and feedback loops between cortex and thalamus, eliciting delayed neuronal responses of RSUs and delayed activities of fast spiking units (FSUs) by affecting local network. Our findings indicated that tFUS can modulate the CTC pathway through both feedforward and feedback loops, which could influence larger cortical areas including motor cortex.

Preconfigured architecture of the developing mouse brain

In the adult brain, structural and functional parameters, such as synaptic sizes and neuronal firing rates, follow right-skewed and heavy-tailed distributions. While this organization is thought to have significant implications, its development is still largely unknown. Here, we address this knowledge gap by investigating a large-scale dataset recorded from the prefrontal cortex and the olfactory bulb of mice aged 4-60 postnatal days. We show that firing rates and spike train interactions have a largely stable distribution shape throughout the first 60 postnatal days and that the prefrontal cortex displays a functional small-world architecture. Moreover, early brain activity exhibits an oligarchical organization, where high-firing neurons have hub-like properties. In a neural network model, we show that analogously right-skewed and heavy-tailed synaptic parameters are instrumental to consistently recapitulate the experimental data. Thus, functional and structural parameters in the developing brain are already extremely distributed, suggesting that this organization is preconfigured and not experience dependent.

Developmental effect of RASopathy mutations on neuronal network activity on a chip

RASopathies are a group of genetic disorders caused by mutations in genes encoding components and regulators of the RAS/MAPK signaling pathway, resulting in overactivation of signaling. RASopathy patients exhibit distinctive facial features, cardiopathies, growth and skeletal abnormalities, and varying degrees of neurocognitive impairments including neurodevelopmental delay, intellectual disabilities, or attention deficits. At present, it is unclear how RASopathy mutations cause neurocognitive impairment and what their neuron-specific cellular and network phenotypes are. Here, we investigated the effect of RASopathy mutations on the establishment and functional maturation of neuronal networks. We isolated cortical neurons from RASopathy mouse models, cultured them on multielectrode arrays and performed longitudinal recordings of spontaneous activity in developing networks as well as recordings of evoked responses in mature neurons. To facilitate the analysis of large and complex data sets resulting from long-term multielectrode recordings, we developed MATLAB-based tools for data processing, analysis, and statistical evaluation. Longitudinal analysis of spontaneous network activity revealed a convergent developmental phenotype in neurons carrying the gain-of-function Noonan syndrome-related mutations Ptpn11 D61Y and Kras V14l. The phenotype was more pronounced at the earlier time points and faded out over time, suggesting the emergence of compensatory mechanisms during network maturation. Nevertheless, persistent differences in excitatory/inhibitory balance and network excitability were observed in mature networks. This study improves the understanding of the complex relationship between genetic mutations and clinical manifestations in RASopathies by adding insights into functional network processes as an additional piece of the puzzle.

Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models

Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

Cortical cell assemblies and their underlying connectivity: An in silico study

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.

A neurophysiological basis for aperiodic EEG and the background spectral trend

Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol's known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.

DeePhys: A machine learning-assisted platform for electrophysiological phenotyping of human neuronal networks

Reproducible functional assays to study in vitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of in vitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.

Reorganization of adolescent prefrontal cortex circuitry is required for mouse cognitive maturation

Most cognitive functions involving the prefrontal cortex emerge during late development. Increasing evidence links this delayed maturation to the protracted timeline of prefrontal development, which likely does not reach full maturity before the end of adolescence. However, the underlying mechanisms that drive the emergence and fine-tuning of cognitive abilities during adolescence, caused by circuit wiring, are still unknown. Here, we continuously monitored prefrontal activity throughout the postnatal development of mice and showed that an initial activity increase was interrupted by an extensive microglia-mediated breakdown of activity, followed by the rewiring of circuit elements to achieve adult-like patterns and synchrony. Interfering with these processes during adolescence, but not adulthood, led to a long-lasting microglia-induced disruption of prefrontal activity and neuronal morphology and decreased cognitive abilities. These results identified a nonlinear reorganization of prefrontal circuits during adolescence and revealed its importance for adult network function and cognitive processing.

Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.

NTS-105 decreased cell death and preserved long-term potentiation in an in vitro model of moderate traumatic brain injury

Traumatic brain injury (TBI) is a major cause of hospitalization and death. To mitigate these human costs, the search for effective drugs to treat TBI continues. In the current study, we evaluated the efficacy of the novel neurosteroid, NTS-105, to reduce post-traumatic pathobiology in an in vitro model of moderate TBI that utilizes an organotypic hippocampal slice culture. NTS-105 inhibited activation of the androgen receptor and the mineralocorticoid receptor, partially activated the progesterone B receptor and was not active at the glucocorticoid receptor. Treatment with NTS-105 starting one hour after injury decreased post-traumatic cell death in a dose-dependent manner, with 10 nM NTS-105 being most effective. Post-traumatic administration of 10 nM NTS-105 also prevented deficits in long-term potentiation (LTP) without adversely affecting neuronal activity in naïve cultures. We propose that the high potency pleiotropic action of NTS-105 beneficial effects at multiple receptors (e.g. androgen, mineralocorticoid and progesterone) provides significant mechanistic advantages over native neurosteroids such as progesterone, which lacked clinical success for the treatment of TBI. Our results suggest that this pleiotropic pharmacology may be a promising strategy for the effective treatment of TBI, and future studies should test its efficacy in pre-clinical animal models of TBI.

Female Preferences for More Elaborate Signals Are an Emergent Outcome of Male Chorusing Interactions in Túngara Frogs

AbstractIn chorusing species, conspecific interference exerts strong selection on signal form and timing to maximize conspicuousness and attractiveness within the signaling milieu. We investigated how túngara frog calling strategies were influenced by varied social environments and male phenotypes and how calling interactions influenced female preferences. When chorusing, túngara frog calls consist of a whine typically followed by one to three chucks. In experimental choruses we saw that as chorus size increased, calls increasingly had their chucks overlapped by the high-amplitude beginning section of other callers' whines. Playback experiments revealed that such overlap reduced the attractiveness of calls to females but that appending additional chucks mitigated this effect. Thus, more elaborate calls were preferred when calls suffered overlap, although they were not preferred when overlap was absent. In response to increasing risk of overlap in larger choruses, males increased call elaboration. However, males overwhelmingly produced two-chuck calls in even the largest choruses, despite our results suggesting that additional chucks would more effectively safeguard calls. Furthermore, aspects of male phenotypes predicted to limit call elaboration had negligible or uncertain effects, suggesting that other constraints are operating. These results highlight how complex interrelations among signal form, signaling interactions, and the social environment shape the evolution of communication in social species.

Effective protection of photoreceptors using an inflammation-responsive hydrogel to attenuate outer retinal degeneration

Retinitis pigmentosa (RP) is an outer retinal degenerative disease that can lead to photoreceptor cell death and profound vision loss. Although effective regulation of intraretinal inflammation can slow down the progression of the disease, an efficient anti-inflammatory treatment strategy is still lacking. This study reports the fabrication of a hyaluronic acid-based inflammation-responsive hydrogel (IRH) and its epigenetic regulation effects on retinal degeneration. The injectable IRH was designed to respond to cathepsin overexpression in an inflammatory environment. The epigenetic drug, the enhancer of zeste homolog 2 (EZH2) inhibitors, was loaded into the hydrogel to attenuate inflammatory factors. On-demand anti-inflammatory effects of microglia cells via the drug-loaded IRH were verified in vitro and in vivo retinal degeneration 10 (rd10) mice model. Therefore, our IRH not only reduced intraretinal inflammation but also protected photoreceptors morphologically and functionally. Our results suggest the IRH reported here can be used to considerably delay vision loss caused by RP.

Internet-Connected Cortical Organoids for Project-Based Stem Cell and Neuroscience Education

The introduction of Internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell (PSC)-derived cortical organoids in two different settings: using microscopy to monitor organoid growth in an introductory tissue culture course and using high-density (HD) multielectrode arrays (MEAs) to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.

Progressive development of synchronous activity in the hippocampal neuronal network is modulated by GluK1 kainate receptors

Kainate receptors are potent modulators of circuit excitability and have been repeatedly implicated in pathophysiological synchronization of limbic networks. While the role of aberrant GluK2 subunit containing KARs in generation of epileptiform hypersynchronous activity is well described, the contribution of other KAR subtypes, including GluK1 subunit containing KARs remain less well understood. To investigate the contribution of GluK1 KARs in developmental and pathological synchronization of the hippocampal neural network, we used multielectrode array recordings on organotypic hippocampal slices that display first multi-unit activity and later spontaneous population discharges resembling ictal-like epileptiform activity (IEA). Chronic blockage of GluK1 activity using selective antagonist ACET or lentivirally delivered shRNA significantly delayed developmental synchronization of the hippocampal CA3 network and generation of IEA. GluK1 overexpression, on the other hand, had no significant effect on occurrence of IEA, but enhanced the size of the neuron population participating in the population discharges. Correlation analysis indicated that local knockdown of GluK1 locally in the CA3 neurons reduced their functional connectivity, while GluK1 overexpression increased the connectivity to both CA1 and DG. These data suggest that GluK1 KARs regulate functional connectivity between the excitatory neurons, possibly via morphological changes in glutamatergic circuit, affecting synchronization of neuronal populations. The significant effects of GluK1 manipulations on network activity call for further research on GluK1 KAR as potential targets for antiepileptic treatments, particularly during the early postnatal development when GluK1 KARs are strongly expressed in the limbic neural networks.

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.

Olfactory bulb activity shapes the development of entorhinal-hippocampal coupling and associated cognitive abilities

The interplay between olfaction and higher cognitive processing has been documented in the adult brain; however, its development is poorly understood. In mice, shortly after birth, endogenous and stimulus-evoked activity in the olfactory bulb (OB) boosts the oscillatory entrainment of downstream lateral entorhinal cortex (LEC) and hippocampus (HP). However, it is unclear whether early OB activity has a long-lasting impact on entorhinal-hippocampal function and cognitive processing. Here, we chemogenetically silenced the synaptic outputs of mitral/tufted cells, the main projection neurons in the OB, during postnatal days 8-10. The transient manipulation leads to a long-lasting reduction of oscillatory coupling and weaker responsiveness to stimuli within developing entorhinal-hippocampal circuits accompanied by dendritic sparsification of LEC pyramidal neurons. Moreover, the transient silencing reduces the performance in behavioral tests involving entorhinal-hippocampal circuits later in life. Thus, neonatal OB activity is critical for the functional LEC-HP development and maturation of cognitive abilities.

PPy/SWCNTs-Modified Microelectrode Array for Learning and Memory Model Construction through Electrical Stimulation and Detection of In Vitro Hippocampal Neuronal Network

The learning and memory functions of the brain remain unclear, which are in urgent need for the detection of both a single cell signal with high spatiotemporal resolution and network activities with high throughput. Here, an in vitro microelectrode array (MEA) was fabricated and further modified with polypyrrole/carboxylated single-walled carbon nanotubes (PPy/SWCNTs) nanocomposites as the interface between biological and electronic systems. The deposition of the nanocomposites significantly improved the performance of microelectrodes including low impedance (60.3 ± 28.8 k Ω), small phase delay (-32.8 ± 4.4°), and good biocompatibility. Then the modified MEA was used to apply learning training and test on hippocampal neuronal network cultured for 21 days through electrical stimulation, and multichannel electrophysiological signals were recorded simultaneously. During the process of learning training, the stimulus/response ratio of the hippocampal learning population gradually increased and the response time gradually decreased. After training, the mean spikes in burst, number of bursts, and mean burst duration increased by 53%, 191%, and 52%, respectively, and the correlation of neurons in the network was significantly enhanced from 0.45 ± 0.002 to 0.78 ± 0.002. In addition, the neuronal network basically retained these characteristics for at least 5 h. These results indicated that we have successfully constructed a learning and memory model of hippocampal neurons on the in vitro MEA, contributing to understanding learning and memory based on synaptic plasticity. The proposed PPy/SWCNTs-modified in vitro MEA will provide a promising platform for the exploration of learning and memory mechanism and their applications in vitro.

Rate versus synchrony codes for cerebellar control of motor behavior

Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the code for information transmission from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellum of rhesus macaques reveal how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and insufficient to impact the firing of downstream vestibular nucleus neurons. Analysis of previous metrics that purported to reveal Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate rather than a synchrony code to drive the activity of downstream neurons and thus control motor behavior.

Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.

Internet-connected cortical organoids for project-based stem cell and neuroscience education

The introduction of internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell-derived cortical organoids in two different settings: Using microscopy to monitor organoid growth in an introductory tissue culture course, and using high density multielectrode arrays to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.

Activity-dependent local protection and lateral inhibition control synaptic competition in developing mitral cells in mice

In developing brains, activity-dependent remodeling facilitates the formation of precise neuronal connectivity. Synaptic competition is known to facilitate synapse elimination; however, it has remained unknown how different synapses compete with one another within a post-synaptic cell. Here, we investigate how a mitral cell in the mouse olfactory bulb prunes all but one primary dendrite during the developmental remodeling process. We find that spontaneous activity generated within the olfactory bulb is essential. We show that strong glutamatergic inputs to one dendrite trigger branch-specific changes in RhoA activity to facilitate the pruning of the remaining dendrites: NMDAR-dependent local signals suppress RhoA to protect it from pruning; however, the subsequent neuronal depolarization induces neuron-wide activation of RhoA to prune non-protected dendrites. NMDAR-RhoA signals are also essential for the synaptic competition in the mouse barrel cortex. Our results demonstrate a general principle whereby activity-dependent lateral inhibition across synapses establishes a discrete receptive field of a neuron.

Large-Area Field Potential Imaging Having Single Neuron Resolution Using 236 880 Electrodes CMOS-MEA Technology

The electrophysiological technology having a high spatiotemporal resolution at the single-cell level and noninvasive measurements of large areas provide insights on underlying neuronal function. Here, a complementary metal-oxide semiconductor (CMOS)-microelectrode array (MEA) is used that uses 236 880 electrodes each with an electrode size of 11.22 × 11.22 µm and 236 880 covering a wide area of 5.5 × 5.9 mm in presenting a detailed and single-cell-level neural activity analysis platform for brain slices, human iPS cell-derived cortical networks, peripheral neurons, and human brain organoids. Propagation pattern characteristics between brain regions changes the synaptic propagation into compounds based on single-cell time-series patterns, classification based on single DRG neuron firing patterns and compound responses, axonal conduction characteristics and changes to anticancer drugs, and network activities and transition to compounds in brain organoids are extracted. This detailed analysis of neural activity at the single-cell level using the CMOS-MEA provides a new understanding of the basic mechanisms of brain circuits in vitro and ex vivo, on human neurological diseases for drug discovery, and compound toxicity assessment.

Rate versus synchrony codes for cerebellar control of motor behavior

Control of movement requires the coordination of multiple brain areas, each containing populations of neurons that receive inputs, process these inputs via recurrent dynamics, and then relay the processed information to downstream populations. Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the nature of the code for transmission of signals to downstream areas from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellar flocculus of rhesus macaques revealed how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and likely insufficient to impact the firing of downstream neurons in the vestibular nucleus. Further, analysis of previous metrics for assaying Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate code rather than synchrony code to drive activity of downstream neurons and thus control motor behavior.

IMPACT STATEMENT

Information transmission in the brain can occur via changes in firing rate or via the precise timing of spikes. Simultaneous recordings from pairs of Purkinje cells in the floccular complex reveals that information transmission out of the cerebellar cortex relies almost exclusively on changes in firing rates rather than millisecond-scale coordination of spike timing across the Purkinje cell population.

Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays

Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network.

Electrically-evoked responses for retinal prostheses are differentially altered depending on ganglion cell types in outer retinal neurodegeneration caused by Crb1 gene mutation

Background

Microelectronic prostheses for artificial vision stimulate neurons surviving outer retinal neurodegeneration such as retinitis pigmentosa (RP). Yet, the quality of prosthetic vision substantially varies across subjects, maybe due to different levels of retinal degeneration and/or distinct genotypes. Although the RP genotypes are remarkably diverse, prosthetic studies have primarily used retinal degeneration (rd) 1 and 10 mice, which both have Pde6b gene mutation. Here, we report the electric responses arising in retinal ganglion cells (RGCs) of the rd8 mouse model which has Crb1 mutation.

Methods

We first investigated age-dependent histological changes of wild-type (wt), rd8, and rd10 mice retinas by H&E staining. Then, we used cell-attached patch clamping to record spiking responses of ON, OFF and direction selective (DS) types of RGCs to a 4-ms-long electric pulse. The electric responses of rd8 RGCs were analyzed in comparison with those of wt RGCs in terms of individual RGC spiking patterns, populational characteristics, and spiking consistency across trials.

Results

In the histological examination, the rd8 mice showed partial retinal foldings, but the outer nuclear layer thicknesses remained comparable to those of the wt mice, indicating the early-stage of RP. Although spiking patterns of each RGC type seemed similar to those of the wt retinas, correlation levels between electric vs. light response features were different across the two mouse models. For example, in comparisons between light vs. electric response magnitudes, ON/OFF RGCs of the rd8 mice showed the same/opposite correlation polarity with those of wt mice, respectively. Also, the electric response spike counts of DS RGCs in the rd8 retinas showed a positive correlation with their direction selectivity indices (r = 0.40), while those of the wt retinas were negatively correlated (r = -0.90). Lastly, the spiking timing consistencies of late responses were largely decreased in both ON and OFF RGCs in the rd8 than the wt retinas, whereas no significant difference was found across DS RGCs of the two models.

Conclusion

Our results indicate the electric response features are altered depending on RGC types even from the early-stage RP caused by Crb1 mutation. Given the various degeneration patterns depending on mutation genes, our study suggests the importance of both genotype- and RGC type-dependent analyses for retinal prosthetic research.

Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks

Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.

Network instability dynamics drive a transient bursting period in the developing hippocampus in vivo

Spontaneous correlated activity is a universal hallmark of immature neural circuits. However, the cellular dynamics and intrinsic mechanisms underlying network burstiness in the intact developing brain are largely unknown. Here, we use two-photon Ca2+ imaging to comprehensively map the developmental trajectories of spontaneous network activity in the hippocampal area CA1 of mice in vivo. We unexpectedly find that network burstiness peaks after the developmental emergence of effective synaptic inhibition in the second postnatal week. We demonstrate that the enhanced network burstiness reflects an increased functional coupling of individual neurons to local population activity. However, pairwise neuronal correlations are low, and network bursts (NBs) recruit CA1 pyramidal cells in a virtually random manner. Using a dynamic systems modeling approach, we reconcile these experimental findings and identify network bi-stability as a potential regime underlying network burstiness at this age. Our analyses reveal an important role of synaptic input characteristics and network instability dynamics for NB generation. Collectively, our data suggest a mechanism, whereby developing CA1 performs extensive input-discrimination learning prior to the onset of environmental exploration.

Inferring the temporal evolution of synaptic weights from dynamic functional connectivity

How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.

Revisiting the involvement of tau in complex neural network remodeling: analysis of the extracellular neuronal activity in organotypic brain slice co-cultures

Objective.Tau ablation has a protective effect in epilepsy due to inhibition of the hyperexcitability/hypersynchrony. Protection may also occur in transgenic models of Alzheimer's disease by reducing the epileptic activity and normalizing the excitation/inhibition imbalance. However, it is difficult to determine the exact functions of tau, because tau knockout (tau^KO) brain networks exhibit elusive phenotypes. In this study, we aimed to further explore the physiological role of tau using brain network remodeling.Approach.The effect of tau ablation was investigated in hippocampal-entorhinal slice co-cultures during network remodeling. We recorded the spontaneous extracellular neuronal activity over 2 weeks in single-slice cultures and co-cultures from control andtau^KOmice. We compared the burst activity and applied concepts and analytical tools intended for the analysis of the network synchrony and connectivity.Main results.Comparison of the control andtau^KOco-cultures revealed that tau ablation had an anti-synchrony effect on the hippocampal-entorhinal two-slice networks at late stages of culture, in line with the literature. Differences were also found between the single-slice and co-culture conditions, which indicated that tau ablation had differential effects at the sub-network scale. For instance, tau ablation was found to have an anti-synchrony effect on the co-cultured hippocampal slices throughout the culture, possibly due to a reduction in the excitation/inhibition ratio. Conversely, tau ablation led to increased synchrony in the entorhinal slices at early stages of the co-culture, possibly due to homogenization of the connectivity distribution.Significance.The new methodology presented here proved useful for investigating the role of tau in the remodeling of complex brain-derived neural networks. The results confirm previous findings and hypotheses concerning the effects of tau ablation on neural networks. Moreover, the results suggest, for the first time, that tau has multifaceted roles that vary in different brain sub-networks.