Neural Coding With Bursts-Current State and Future Perspectives

miR-7 controls glutamatergic transmission and neuronal connectivity in a Cdr1as-dependent manner

The circular RNA (circRNA) Cdr1as is conserved across mammals and highly expressed in neurons, where it directly interacts with microRNA miR-7. However, the biological function of this interaction is unknown. Here, using primary cortical murine neurons, we demonstrate that stimulating neurons by sustained depolarization rapidly induces two-fold transcriptional upregulation of Cdr1as and strong post-transcriptional stabilization of miR-7. Cdr1as loss causes doubling of glutamate release from stimulated synapses and increased frequency and duration of local neuronal bursts. Moreover, the periodicity of neuronal networks increases, and synchronicity is impaired. Strikingly, these effects are reverted by sustained expression of miR-7, which also clears Cdr1as molecules from neuronal projections. Consistently, without Cdr1as, transcriptomic changes caused by miR-7 overexpression are stronger (including miR-7-targets downregulation) and enriched in secretion/synaptic plasticity pathways. Altogether, our results suggest that in cortical neurons Cdr1as buffers miR-7 activity to control glutamatergic excitatory transmission and neuronal connectivity important for long-lasting synaptic adaptations.

Anesthetic Effect on the Subthalamic Nucleus in Microelectrode Recording and Local Field Potential of Parkinson's Disease

OBJECTIVES

Anesthetic agents used during deep brain stimulation (DBS) surgery might interfere with microelectrode recording (MER) and local field potential (LFP) and thus affect the accuracy of surgical target localization. This review aimed to identify the effects of different anesthetic agents on neuronal activity of the subthalamic nucleus (STN) during the MER procedure.

MATERIALS AND METHODS

We used Medical Subject Heading terms to search the PubMed, EMBASE, EBSCO, and ScienceDirect data bases. MER characteristics were sorted into quantitative and qualitative data types. Quantitative data included the burst index, pause index, firing rate (FR), and interspike interval. Qualitative data included background activity, burst discharge (BD), and anesthetic agent effect. We also categorized the reviewed manuscripts into those describing local anesthesia with sedation (LAWS) and those describing general anesthesia (GA) and compiled the effects of anesthetic agents on MER and LFP characteristics.

RESULTS

In total, 26 studies on MER were identified, of which 12 used LAWS and 14 used GA. Three studies on LFP also were identified. We found that the FR was preserved under LAWS but tended to be lower under GA, and BD was reduced in both groups. Individually, propofol enhanced BD but was better used for sedation, or the dosage should be minimized in GA. Similarly, low-dose dexmedetomidine sedation did not disturb MER. Opioids could be used as adjunctive anesthetic agents. Volatile anesthesia had the least adverse effect on MER under GA, with minimal alveolar concentration at 0.5. Dexmedetomidine anesthesia did not affect LFP, whereas propofol interfered with the power of LFP.

CONCLUSIONS

The effects of the tested anesthetics on the STN in MER and LFP of Parkinson's disease varied; however, identifying the STN and achieving a good clinical outcome are possible under controlled anesthetic conditions. For patient comfort, anesthesia should be considered in STN-DBS.

Neuronal Subtypes and Connectivity of the Adult Mouse Paralaminar Amygdala

The paralaminar nucleus of the amygdala (PL) comprises neurons that exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. These late-maturing PL neurons contribute to the increase in size and cell number of the amygdala between birth and adulthood. However, the function of the PL upon maturation is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult mouse PL; the intrinsic morpho-electric properties of its neurons, and its input and output circuit connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to these inputs and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult mouse PL is centrally placed to play a major role in the integration of olfactory sensory information, to coordinate affective and autonomic behavioral responses to salient odor stimuli.

Spiral attractors in a reduced mean-field model of neuron-glial interaction

This paper investigates various bifurcation scenarios of the appearance of bursting activity in the phenomenological mean-field model of neuron-glial interactions. In particular, we show that the homoclinic spiral attractors in this system can be the source of several types of bursting activity with different properties.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems.

Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture

Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.

A machine-learning tool to identify bistable states from calcium imaging data

Mapping neuronal activation using calcium imaging in vivo during behavioural tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities because action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. Although a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In the present study, we develop a tool, CaMLSort, which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analysing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviours. KEY POINTS: Calcium imaging, compriising the gold standard of inferring neuronal activity, does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort (Calcium imaging and Machine Learning based tool to sort intracellular state) also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool can facilitate our understanding of how these neurons carry out their functions in a circuit.

Electrophysiological Properties of the Medial Mammillary Bodies across the Sleep-Wake Cycle

The medial mammillary bodies (MBs) play an important role in the formation of spatial memories; their dense inputs from hippocampal and brainstem regions makes them well placed to integrate movement-related and spatial information, which is then extended to the anterior thalamic nuclei and beyond to the cortex. While the anatomical connectivity of the medial MBs has been well studied, much less is known about their physiological properties, particularly in freely moving animals. We therefore carried out a comprehensive characterization of medial MB electrophysiology across arousal states by concurrently recording from the medial MB and the CA1 field of the hippocampus in male rats. In agreement with previous studies, we found medial MB neurons to have firing rates modulated by running speed and angular head velocity, as well as theta-entrained firing. We extended the characterization of MB neuron electrophysiology in three key ways: (1) we identified a subset of neurons (25%) that exhibit dominant bursting activity; (2) we showed that ∼30% of theta-entrained neurons exhibit robust theta cycle skipping, a firing characteristic that implicates them in a network for prospective coding of position; and (3) a considerable proportion of medial MB units showed sharp-wave ripple (SWR) responsive firing (∼37%). The functional heterogeneity of MB electrophysiology reinforces their role as an integrative node for mnemonic processing and identifies potential roles for the MBs in memory consolidation through propagation of SWR-responsive activity to the anterior thalamus and prospective coding in the form of theta cycle skipping.

Calcium-Dependent Hyperexcitability in Human Stem Cell-Derived Rett Syndrome Neuronal Networks

Background

Mutations in MECP2 predominantly cause Rett syndrome and can be modeled in vitro using human stem cell-derived neurons. Patients with Rett syndrome have signs of cortical hyperexcitability, such as seizures. Human stem cell-derived MECP2 null excitatory neurons have smaller soma size and reduced synaptic connectivity but are also hyperexcitable due to higher input resistance. Paradoxically, networks of MECP2 null neurons show a decrease in the frequency of network bursts consistent with a hypoconnectivity phenotype. Here, we examine this issue.

Methods

We reanalyzed multielectrode array data from 3 isogenic MECP2 cell line pairs recorded over 6 weeks (n = 144). We used a custom burst detection algorithm to analyze network events and isolated a phenomenon that we termed reverberating super bursts (RSBs). To probe potential mechanisms of RSBs, we conducted pharmacological manipulations using bicuculline, EGTA-AM, and DMSO on 1 cell line (n = 34).

Results

RSBs, often misidentified as single long-duration bursts, consisted of a large-amplitude initial burst followed by several high-frequency, low-amplitude minibursts. Our analysis revealed that MECP2 null networks exhibited increased frequency of RSBs, which produced increased bursts compared with isogenic controls. Bicuculline or DMSO treatment did not affect RSBs. EGTA-AM selectively eliminated RSBs and rescued network burst dynamics.

Conclusions

During early development, MECP2 null neurons are hyperexcitable and produce hyperexcitable networks. This may predispose them to the emergence of hypersynchronic states that potentially translate into seizures. Network hyperexcitability depends on asynchronous neurotransmitter release that is likely driven by presynaptic Ca2+ and can be rescued by EGTA-AM to restore typical network dynamics.

Value-Based Search Efficiency Is Encoded in the Substantia Nigra Reticulata Firing Rate, Spiking Irregularity and Local Field Potential

Recent results show that valuable objects can pop out in visual search, yet its neural mechanisms remain unexplored. Given the role of substantia nigra reticulata (SNr) in object value memory and control of gaze, we recorded its single-unit activity while male macaque monkeys engaged in efficient or inefficient search for a valuable target object among low-value objects. The results showed that efficient search was concurrent with stronger inhibition and higher spiking irregularity in the target-present (TP) compared with the target-absent (TA) trials in SNr. Importantly, the firing rate differentiation of TP and TA trials happened within ∼100 ms of display onset, and its magnitude was significantly correlated with the search times and slopes (search efficiency). Time-frequency analyses of local field potential (LFP) after display onset revealed significant modulations of the gamma band power with search efficiency. The greater reduction of SNr firing in TP trials in efficient search can create a stronger disinhibition of downstream superior colliculus, which in turn can facilitate saccade to obtain valuable targets in competitive environments.

Neuronal subtypes and connectivity of the adult mouse paralaminar amygdala

The paralaminar nucleus of the amygdala (PL) is comprised of neurons which exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. The PL is prominent in the adult amygdala, contributing to its increased neuron number and relative size compared to childhood. However, the function of the PL is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult PL; the intrinsic morpho-electric properties of its neurons, and its input and output connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to major inputs, and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult PL is centrally placed to play a major role in the integration of olfactory sensory information, likely coordinating affective and autonomic behavioral responses to salient odor stimuli.

Significance Statement

Mammalian amygdala development includes a growth period from childhood to adulthood, believed to support emotional and social learning. This amygdala growth is partly due to the maturation of neurons during adolescence in the paralaminar amygdala. However, the functional properties of these neurons are unknown. In our recent studies, we characterized the paralaminar amygdala in the mouse. Here, we investigate the properties of the adult PL in the mouse, revealing the existence of two neuronal subtypes that may play distinct functional roles in the adult brain. We further reveal the brain-wide input and output connectivity of the PL, indicating that the PL combines olfactory cues for emotional processing and delivers information to regions associated with reward and autonomic states.

Burst and Memory-aware Transformer: capturing temporal heterogeneity

Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model, designed to explicitly address temporal heterogeneity. The BMT model embeds the burstiness and memory coefficient into the self-attention module, enhancing the learning process with insights derived from the bursty patterns. Furthermore, we employed a novel loss function designed to optimize the burstiness and memory coefficient values, as well as their corresponding discretized one-hot vectors, both individually and jointly. Numerical experiments conducted on diverse synthetic and real-world datasets demonstrated the outstanding performance of the BMT model in terms of accurately predicting event times and intensity functions compared to existing models and control groups. In particular, the BMT model exhibits remarkable performance for temporally heterogeneous data, such as those with power-law inter-event time distributions. Our findings suggest that the incorporation of burst-related parameters assists the Transformer in comprehending heterogeneous event sequences, leading to an enhanced predictive performance.

In vivo spontaneous activity and coital-evoked inhibition of mouse accessory olfactory bulb output neurons

Little is known about estrous effects on brain microcircuits. We examined the accessory olfactory bulb (AOB) in vivo, in anesthetized naturally cycling females, as model microcircuit receiving coital somatosensory information. Whole-cell recordings demonstrate that output neurons are relatively hyperpolarized in estrus and unexpectedly fire high frequency bursts of action potentials. To mimic coitus, a calibrated artificial vagino-cervical stimulation (aVCS) protocol was devised. aVCS evoked stimulus-locked local field responses in the interneuron layer independent of estrous stage. The response is sensitive to α1-adrenergic receptor blockade, as expected since aVCS increases norepinephrine release in AOB. Intriguingly, only in estrus does aVCS inhibit AOB spike output. Estrus-specific output reduction coincides with prolonged aVCS activation of inhibitory interneurons. Accordingly, in estrus the AOB microcircuit sets the stage for coital stimulation to inhibit the output neurons, possibly via high frequency bursting-dependent enhancement of reciprocal synapse efficacy between inter- and output neurons.

The QBIT theory of consciousness: Entropy and qualia

The QBIT theory is a recently introduced multi-disciplinary approach to the problem of consciousness. One of the main axioms of the theory is that when information-theoretic certainty of an observer about a stimulus goes beyond a certain threshold, the observer becomes conscious of that stimulus. This axiom could provide an explanation for how the brain generates consciousness.In short, the QBIT theory suggests that the brain generates consciousness by reducing the entropy of its internal representations below a critical threshold. This paper explains how the brain gradually minimizes the entropy of its internal representations and consequently generate minimum-entropy representations (also known as conscious representations or qualia). The paper also explores the consequences of this entropy-minimization process in the context of quantum information theory.

Network Bursts in 3D Neuron Clusters Cultured on Microcontact-Printed Substrates

Microcontact printing (CP) is widely used to guide neurons to form 2D networks for neuroscience research. However, it is still difficult to establish 3D neuronal cultures on the CP substrate even though 3D neuronal structures are able to recapitulate critical aspects of native tissue. Here, we demonstrate that the reduced cell-substrate adhesion caused by the CP substrate could conveniently facilitate the aggregate formation of large-scale 3D neuron cluster networks. Furthermore, based on the quantitative analysis of the calcium activity of the resulting cluster networks, the effect of cell seeding density and local restriction of the CP substrate on network dynamics was investigated in detail. The results revealed that cell aggregation degree, rather than cell number, could take on the main role of the generation of synchronized network-wide calcium oscillation (network bursts) in the 3D neuron cluster networks. This finding may provide new insights for easy and cell-saving construction of in vitro 3D pathological models of epilepsy, and into deciphering the onset and evolution of network bursts in developmental nerve systems.

ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network

Recent developments in artificial neural networks and their learning algorithms have enabled new research directions in computer vision, language modeling, and neuroscience. Among various neural network algorithms, spiking neural networks (SNNs) are well-suited for understanding the behavior of biological neural circuits. In this work, we propose to guide the training of a sparse SNN in order to replace a sub-region of a cultured hippocampal network with limited hardware resources. To verify our approach with a realistic experimental setup, we record spikes of cultured hippocampal neurons with a microelectrode array (in vitro). The main focus of this work is to dynamically cut unimportant synapses during SNN training on the fly so that the model can be realized on resource-constrained hardware, e.g., implantable devices. To do so, we adopt a simple STDP learning rule to easily select important synapses that impact the quality of spike timing learning. By combining the STDP rule with online supervised learning, we can precisely predict the spike pattern of the cultured network in real-time. The reduction in the model complexity, i.e., the reduced number of connections, significantly reduces the required hardware resources, which is crucial in developing an implantable chip for the treatment of neurological disorders. In addition to the new learning algorithm, we prototype a sparse SNN hardware on a small FPGA with pipelined execution and parallel computing to verify the possibility of real-time replacement. As a result, we can replace a sub-region of the biological neural circuit within 22 μs using 2.5 × fewer hardware resources, i.e., by allowing 80% sparsity in the SNN model, compared to the fully-connected SNN model. With energy-efficient algorithms and hardware, this work presents an essential step toward real-time neuroprosthetic computation.

Exploring retinal ganglion cells encoding to multi-modal stimulation using 3D microelectrodes arrays

Microelectrode arrays (MEA) are extensively utilized in encoding studies of retinal ganglion cells (RGCs) due to their capacity for simultaneous recording of neural activity across multiple channels. However, conventional planar MEAs face limitations in studying RGCs due to poor coupling between electrodes and RGCs, resulting in low signal-to-noise ratio (SNR) and limited recording sensitivity. To overcome these challenges, we employed photolithography, electroplating, and other processes to fabricate a 3D MEA based on the planar MEA platform. The 3D MEA exhibited several improvements compared to planar MEA, including lower impedance (8.73 ± 1.66 kΩ) and phase delay (-15.11° ± 1.27°), as well as higher charge storage capacity (CSC = 10.16 ± 0.81 mC/cm2), cathodic charge storage capacity (CSCc = 7.10 ± 0.55 mC/cm2), and SNR (SNR = 8.91 ± 0.57). Leveraging the advanced 3D MEA, we investigated the encoding characteristics of RGCs under multi-modal stimulation. Optical, electrical, and chemical stimulation were applied as sensory inputs, and distinct response patterns and response times of RGCs were detected, as well as variations in rate encoding and temporal encoding. Specifically, electrical stimulation elicited more effective RGC firing, while optical stimulation enhanced RGC synchrony. These findings hold promise for advancing the field of neural encoding.

Neural population dynamics reveal that motor-targeted intraspinal microstimulation preferentially depresses nociceptive transmission in spinal cord injury-related neuropathic pain

The purpose of this study is to determine whether intraspinal microstimulation (ISMS) intended to enhance voluntary motor output after spinal cord injury (SCI) modulates neural population-level spinal responsiveness to nociceptive sensory feedback. The study was conducted in vivo in three cohorts of rats: neurologically intact, chronic SCI without behavioral signs of neuropathic pain, and chronic SCI with SCI-related neuropathic pain (SCI-NP). Nociceptive sensory feedback was induced by application of graded mechanical pressure to the plantar surface of the hindpaw before, during, and after periods of sub-motor threshold ISMS delivered within the motor pools of the L5 spinal segment. Neural population-level responsiveness to nociceptive feedback was recorded throughout the dorso-ventral extent of the L5 spinal segment using dense multi-channel microelectrode arrays. Whereas motor-targeted ISMS reduced nociceptive transmission across electrodes in neurologically intact animals both during and following stimulation, it was not associated with altered nociceptive transmission in rats with SCI that lacked behavioral signs of neuropathic pain. Surprisingly, nociceptive transmission was reduced both during and following motor-targeted ISMS in rats with SCI-NP, and to an extent comparable to that of neurologically intact animals. The mechanisms underlying the differential anti-nociceptive effects of motor-targeted ISMS are unclear, although they may be related to differences in the intrinsic active membrane properties of spinal neurons across the cohorts. Nevertheless, the results of this study support the notion that it may be possible to purposefully engineer spinal stimulation-based therapies that afford multi-modal rehabilitation benefits, and specifically that it may be possible to do so for the individuals most in need - i.e., those with SCI-related movement impairments and SCI-NP.

Activity-dependent tau cleavage by caspase-3 promotes neuronal dysfunction and synaptotoxicity

Tau-mediated toxicity is associated with cognitive decline and Alzheimer's disease (AD) progression. In particular, tau post-translational modifications (PTMs) are thought to generate aberrant tau species resulting in neuronal dysfunction. Despite being well characterized in postmortem AD brain, it is unclear how caspase-mediated C-terminal tau cleavage promotes neurodegeneration, as few studies have developed the models to dissect this pathogenic mechanism. Here, we show that proteasome impairment results in cleaved tau accumulation at the post-synaptic density (PSD), a process that is modulated by neuronal activity. Cleaved tau (at residue D421) impairs neuronal firing and causes inefficient initiation of network bursts, consistent with reduced excitatory drive. We propose that reduced neuronal activity, or silencing, is coupled to proteasome dysfunction, which drives cleaved tau accumulation at the PSD and subsequent synaptotoxicity. Our study connects three common themes in the progression of AD: impaired proteostasis, caspase-mediated tau cleavage, and synapse degeneration.

Spontaneous activity in cortical neurons is stereotyped and non-Poisson

Neurons fire even in the absence of sensory stimulation or task demands. Numerous theoretical studies have modeled this spontaneous activity as a Poisson process with uncorrelated intervals between successive spikes and a variance in firing rate equal to the mean. Experimental tests of this hypothesis have yielded variable results, though most have concluded that firing is not Poisson. However, these tests say little about the ways firing might deviate from randomness. Nor are they definitive because many different distributions can have equal means and variances. Here, we characterized spontaneous spiking patterns in extracellular recordings from monkey, cat, and mouse cerebral cortex neurons using rate-normalized spike train autocorrelation functions (ACFs) and a logarithmic timescale. If activity was Poisson, this function should be flat. This was almost never the case. Instead, ACFs had diverse shapes, often with characteristic peaks in the 1-700 ms range. Shapes were stable over time, up to the longest recording periods used (51 min). They did not fall into obvious clusters. ACFs were often unaffected by visual stimulation, though some abruptly changed during brain state shifts. These behaviors may have their origin in the intrinsic biophysics and dendritic anatomy of the cells or in the inputs they receive.

Diverse role of NMDA receptors for dendritic integration of neural dynamics

Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.

Asymmetric adaptivity induces recurrent synchronization in complex networks

Rhythmic activities that alternate between coherent and incoherent phases are ubiquitous in chemical, ecological, climate, or neural systems. Despite their importance, general mechanisms for their emergence are little understood. In order to fill this gap, we present a framework for describing the emergence of recurrent synchronization in complex networks with adaptive interactions. This phenomenon is manifested at the macroscopic level by temporal episodes of coherent and incoherent dynamics that alternate recurrently. At the same time, the dynamics of the individual nodes do not change qualitatively. We identify asymmetric adaptation rules and temporal separation between the adaptation and the dynamics of individual nodes as key features for the emergence of recurrent synchronization. Our results suggest that asymmetric adaptation might be a fundamental ingredient for recurrent synchronization phenomena as seen in pattern generators, e.g., in neuronal systems.

Alpha-synuclein oligomers alter the spontaneous firing discharge of cultured midbrain neurons

The aim of this work was to monitor the effects of extracellular α-synuclein on the firing activity of midbrain neurons dissociated from substantia nigra TH-GFP mice embryos and cultured on microelectrode arrays (MEA). We monitored the spontaneous firing discharge of the network for 21 days after plating and the role of glutamatergic and GABAergic inputs in regulating burst generation and network synchronism. Addition of GABA A , AMPA and NMDA antagonists did not suppress the spontaneous activity but allowed to identify three types of neurons that exhibited different modalities of firing and response to applied L-DOPA: high-rate (HR) neurons, low-rate pacemaking (LR-p), and low-rate non-pacemaking (LR-np) neurons. Most HR neurons were insensitive to L-DOPA, while the majority of LR-p neurons responded with a decrease of the firing discharge; less defined was the response of LR-np neurons. The effect of exogenous α-synuclein (α-syn) on the firing discharge of midbrain neurons was then studied by varying the exposure time (0-48 h) and the α-syn concentration (0.3-70 μM), while the formation of α-syn oligomers was monitored by means of AFM. Independently of the applied concentration, acute exposure to α-syn monomers did not exert any effect on the spontaneous firing rate of HR, LR-p, and LR-np neurons. On the contrary, after 48 h exposure, the firing activity was drastically altered at late developmental stages (14 days in vitro, DIV, neurons): α-syn oligomers progressively reduced the spontaneous firing discharge (IC50 = 1.03 μM), impaired burst generation and network synchronism, proportionally to the increased oligomer/monomer ratio. Different effects were found on early-stage developed neurons (9 DIV), whose firing discharge remained unaltered, regardless of the applied α-syn concentration and the exposure time. Our findings unravel, for the first time, the variable effects of exogenous α-syn at different stages of midbrain network development and provide new evidence for the early detection of neuronal function impairment associated to aggregated forms of α-syn.

A functional network of highly pure enteric neurons in a dish

The enteric nervous system (ENS) is the intrinsic nervous system that innervates the entire digestive tract and regulates major digestive functions. Recent evidence has shown that functions of the ENS critically rely on enteric neuronal connectivity; however, experimental models to decipher the underlying mechanisms are limited. Compared to the central nervous system, for which pure neuronal cultures have been developed for decades and are recognized as a reference in the field of neuroscience, an equivalent model for enteric neurons is lacking. In this study, we developed a novel model of highly pure rat embryonic enteric neurons with dense and functional synaptic networks. The methodology is simple and relatively fast. We characterized enteric neurons using immunohistochemical, morphological, and electrophysiological approaches. In particular, we demonstrated the applicability of this culture model to multi-electrode array technology as a new approach for monitoring enteric neuronal network activity. This in vitro model of highly pure enteric neurons represents a valuable new tool for better understanding the mechanisms involved in the establishment and maintenance of enteric neuron synaptic connectivity and functional networks.

Spike timing-dependent plasticity under imbalanced excitation and inhibition reduces the complexity of neural activity

Excitatory and inhibitory neurons are fundamental components of the brain, and healthy neural circuits are well balanced between excitation and inhibition (E/I balance). However, it is not clear how an E/I imbalance affects the self-organization of the network structure and function in general. In this study, we examined how locally altered E/I balance affects neural dynamics such as the connectivity by activity-dependent formation, the complexity (multiscale entropy) of neural activity, and information transmission. In our simulation, a spiking neural network model was used with the spike-timing dependent plasticity rule to explore the above neural dynamics. We controlled the number of inhibitory neurons and the inhibitory synaptic weights in a single neuron group out of multiple neuron groups. The results showed that a locally increased E/I ratio strengthens excitatory connections, reduces the complexity of neural activity, and decreases information transmission between neuron groups in response to an external input. Finally, we argued the relationship between our results and excessive connections and low complexity of brain activity in the neuropsychiatric brain disorders.

Intrinsic Excitability in Layer IV-VI Anterior Insula to Basolateral Amygdala Projection Neurons Correlates with the Confidence of Taste Valence Encoding

Avoiding potentially harmful, and consuming safe food is crucial for the survival of living organisms. However, the perceived valence of sensory information can change following conflicting experiences. Pleasurability and aversiveness are two crucial parameters defining the perceived valence of a taste and can be impacted by novelty. Importantly, the ability of a given taste to serve as the conditioned stimulus (CS) in conditioned taste aversion (CTA) is dependent on its valence. Activity in anterior insula (aIC) Layer IV-VI pyramidal neurons projecting to the basolateral amygdala (BLA) is correlated with and necessary for CTA learning and retrieval, as well as the expression of neophobia toward novel tastants, but not learning taste familiarity. Yet, the cellular mechanisms underlying the updating of taste valence representation in this specific pathway are poorly understood. Here, using retrograde viral tracing and whole-cell patch-clamp electrophysiology in trained mice, we demonstrate that the intrinsic properties of deep-lying Layer IV-VI, but not superficial Layer I-III aIC-BLA neurons, are differentially modulated by both novelty and valence, reflecting the subjective predictability of taste valence arising from prior experience. These correlative changes in the profile of intrinsic properties of LIV-VI aIC-BLA neurons were detectable following both simple taste experiences, as well as following memory retrieval, extinction learning, and reinstatement.

Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks

Neurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.

Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro-electrode arrays

Comprehensive electrophysiological characterizations of human induced pluripotent stem cell (hiPSC)-derived neuronal networks are essential to determine to what extent these in vitro models recapitulate the functional features of in vivo neuronal circuits. High-density micro-electrode arrays (HD-MEAs) offer non-invasive recording with the best spatial and temporal resolution possible to date. For 3 months, we tracked the morphology and activity features of developing networks derived from a transgenic hiPSC line in which neurogenesis is inducible by neurogenic transcription factor overexpression. Our morphological data revealed large-scale structural changes from homogeneously distributed neurons in the first month to the formation of neuronal clusters over time. This led to a constant shift in position of neuronal cells and clusters on HD-MEAs and corresponding changes in spatial distribution of the network activity maps. Network activity appeared as scarce action potentials (APs), evolved as local bursts with longer duration and changed to network-wide synchronized bursts with higher frequencies but shorter duration over time, resembling the emerging burst features found in the developing human brain. Instantaneous firing rate data indicated that the fraction of fast spiking neurons (150–600 Hz) increases sharply after 63 days post induction (dpi). Inhibition of glutamatergic synapses erased burst features from network activity profiles and confirmed the presence of mature excitatory neurotransmission. The application of GABAergic receptor antagonists profoundly changed the bursting profile of the network at 120 dpi. This indicated a GABAergic switch from excitatory to inhibitory neurotransmission during circuit development and maturation. Our results suggested that an emerging GABAergic system at older culture ages is involved in regulating spontaneous network bursts. In conclusion, our data showed that long-term and continuous microscopy and electrophysiology readouts are crucial for a meaningful characterization of morphological and functional maturation in stem cell-derived human networks. Most importantly, assessing the level and duration of functional maturation is key to subject these human neuronal circuits on HD-MEAs for basic and biomedical applications.

Brain inspired neuronal silencing mechanism to enable reliable sequence identification

Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes. Existing methods apply recurrent neural networks, which suffer from training difficulties; however, performing this function without feedback loops remains a challenge. Here, we present an experimental neuronal long-term plasticity mechanism for high-precision feedforward sequence identification networks (ID-nets) without feedback loops, wherein input objects have a given order and timing. This mechanism temporarily silences neurons following their recent spiking activity. Therefore, transitory objects act on different dynamically created feedforward sub-networks. ID-nets are demonstrated to reliably identify 10 handwritten digit sequences, and are generalized to deep convolutional ANNs with continuous activation nodes trained on image sequences. Counterintuitively, their classification performance, even with a limited number of training examples, is high for sequences but low for individual objects. ID-nets are also implemented for writer-dependent recognition, and suggested as a cryptographic tool for encrypted authentication. The presented mechanism opens new horizons for advanced ANN algorithms.

Single-neuron bursts encode pathological oscillations in subcortical nuclei of patients with Parkinson's disease and essential tremor

Deep brain stimulation procedures offer an invaluable opportunity to study disease through intracranial recordings from awake patients. Here, we address the relationship between single-neuron and aggregate-level (local field potential; LFP) activities in the subthalamic nucleus (STN) and thalamic ventral intermediate nucleus (Vim) of patients with Parkinson's disease (n = 19) and essential tremor (n = 16), respectively. Both disorders have been characterized by pathologically elevated LFP oscillations, as well as an increased tendency for neuronal bursting. Our findings suggest that periodic single-neuron bursts encode both pathophysiological beta (13 to 33 Hz; STN) and tremor (4 to 10 Hz; Vim) LFP oscillations, evidenced by strong time-frequency and phase-coupling relationships between the bursting and LFP signals. Spiking activity occurring outside of bursts had no relationship to the LFP. In STN, bursting activity most commonly preceded the LFP oscillation, suggesting that neuronal bursting generated within STN may give rise to an aggregate-level LFP oscillation. In Vim, LFP oscillations most commonly preceded bursting activity, suggesting that neuronal firing may be entrained by periodic afferent inputs. In both STN and Vim, the phase-coupling relationship between LFP and high-frequency oscillation (HFO) signals closely resembled the relationships between the LFP and single-neuron bursting. This suggests that periodic single-neuron bursting is likely representative of a higher spatial and temporal resolution readout of periodic increases in the amplitude of HFOs, which themselves may be a higher resolution readout of aggregate-level LFP oscillations. Overall, our results may reconcile "rate" and "oscillation" models of Parkinson's disease and shed light on the single-neuron basis and origin of pathophysiological oscillations in movement disorders.

The intersection of astrocytes and the endocannabinoid system in the lateral habenula: on the fast-track to novel rapid-acting antidepressants

Despite attaining significant advances toward better management of depressive disorders, we are still facing several setbacks. Developing rapid-acting antidepressants with sustained effects is an aspiration that requires thinking anew to explore possible novel targets. Recently, the lateral habenula (LHb), the brain's "anti-reward system", has been shown to go awry in depression in terms of various molecular and electrophysiological signatures. Some of the presumed contributors to such observed aberrations are astrocytes. These star-shaped cells of the brain can alter the firing pattern of the LHb, which keeps the activity of the midbrain's aminergic centers under tight control. Astrocytes are also integral parts of the tripartite synapses, and can therefore modulate synaptic plasticity and leave long-lasting changes in the brain. On the other hand, it was discovered that astrocytes express cannabinoid type 1 receptors (CB₁R), which can also take part in long-term plasticity. Herein, we recount how the LHb of a depressed brain deviates from the "normal" one from a molecular perspective. We then try to touch upon the alterations of the endocannabinoid system in the LHb, and cast the idea that modulation of astroglial CB₁R may help regulate habenular neuronal activity and synaptogenesis, thereby acting as a new pharmacological tool for regulation of mood and amelioration of depressive symptoms.

Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as bursting abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying spiking irregularity arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach reveals previously unnoticed changes in neural response variability and constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.

Mounting evidence suggest neural response variability to be important for understanding and constraining the underlying neural mechanisms in a given brain area. Here, by analyzing responses across multiple brain areas and by using a principled method for parsing variability components into rate variability and spiking irregularity, we show that unlike what was previously thought, event-related quench of variability is not a brain-wide phenomenon and that point process variability and nonrenewal bursting can enhance post-stimulus spiking irregularity across certain cortical and subcortical regions. We propose possible presynaptic mechanisms that may underlie the observed heterogeneities in spiking variability across the brain.

Layer-specific pyramidal neuron properties underlie diverse anterior cingulate cortical motor and limbic networks

The laminar cellular and circuit mechanisms by which the anterior cingulate cortex (ACC) exerts flexible control of motor and affective information for goal-directed behavior have not been elucidated. Using multimodal tract-tracing, in vitro patch-clamp recording and computational approaches in rhesus monkeys (M. mulatta), we provide evidence that specialized motor and affective network dynamics can be conferred by layer-specific biophysical and structural properties of ACC pyramidal neurons targeting two key downstream structures -the dorsal premotor cortex (PMd) and the amygdala (AMY). AMY-targeting neurons exhibited significant laminar differences, with L5 more excitable (higher input resistance and action potential firing rates) than L3 neurons. Between-pathway differences were found within L5, with AMY-targeting neurons exhibiting greater excitability, apical dendritic complexity, spine densities, and diversity of inhibitory inputs than PMd-targeting neurons. Simulations using a pyramidal-interneuron network model predict that these layer- and pathway-specific single-cell differences contribute to distinct network oscillatory dynamics. L5 AMY-targeting networks are more tuned to slow oscillations well-suited for affective and contextual processing timescales, while PMd-targeting networks showed strong beta/gamma synchrony implicated in rapid sensorimotor processing. These findings are fundamental to our broad understanding of how layer-specific cellular and circuit properties can drive diverse laminar activity found in flexible behavior.

Distinct neural codes in primate hippocampus and lateral prefrontal cortex during associative learning in virtual environments

The hippocampus (HPC) and the lateral prefrontal cortex (LPFC) are two cortical areas of the primate brain deemed essential to cognition. Here, we hypothesized that the codes mediating neuronal communication in the HPC and LPFC microcircuits have distinctively evolved to serve plasticity and memory function at different spatiotemporal scales. We used a virtual reality task in which animals selected one of the two targets in the arms of the maze, according to a learned context-color rule. Our results show that during associative learning, HPC principal cells concentrate spikes in bursts, enabling temporal summation and fast synaptic plasticity in small populations of neurons and ultimately facilitating rapid encoding of associative memories. On the other hand, layer II/III LPFC pyramidal cells fire spikes more sparsely distributed over time. The latter would facilitate broadcasting of signals loaded in short-term memory across neuronal populations without necessarily triggering fast synaptic plasticity.

Emergence of Integrated Information at Macro Timescales in Real Neural Recordings

How a system generates conscious experience remains an elusive question. One approach towards answering this is to consider the information available in the system from the perspective of the system itself. Integrated information theory (IIT) proposes a measure to capture this integrated information (Φ). While Φ can be computed at any spatiotemporal scale, IIT posits that it be applied at the scale at which the measure is maximised. Importantly, Φ in conscious systems should emerge to be maximal not at the smallest spatiotemporal scale, but at some macro scale where system elements or timesteps are grouped into larger elements or timesteps. Emergence in this sense has been demonstrated in simple example systems composed of logic gates, but it remains unclear whether it occurs in real neural recordings which are generally continuous and noisy. Here we first utilise a computational model to confirm that Φ becomes maximal at the temporal scales underlying its generative mechanisms. Second, we search for emergence in local field potentials from the fly brain recorded during wakefulness and anaesthesia, finding that normalised Φ (wake/anaesthesia), but not raw Φ values, peaks at 5 ms. Lastly, we extend our model to investigate why raw Φ values themselves did not peak. This work extends the application of Φ to simple artificial systems consisting of logic gates towards searching for emergence of a macro spatiotemporal scale in real neural systems.

Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons

The transformation of synaptic input into action potential output is a fundamental single-cell computation resulting from the complex interaction of distinct cellular morphology and the unique expression profile of ion channels that define the cellular phenotype. Experimental studies aimed at uncovering the mechanisms of the transfer function have led to important insights, yet are limited in scope by technical feasibility, making biophysical simulations an attractive complementary approach to push the boundaries in our understanding of cellular computation. Here we take a data-driven approach by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm to build two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. We evaluated the performance of these models and find that our approach quantitatively matches the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability in the data. Moreover, we confirm that the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. We then use these models to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input through an information-theoretic treatment of their respective transfer functions. Our simulations identify a host of cell type-specific biophysical mechanisms that define the morpho-functional phenotype to shape the cellular transfer function and place these findings in the context of a role for bursting in CA3 recurrent network synchronization dynamics.

The hippocampus is comprised of numerous types of neurons, which constitute the cellular substrate for its rich repertoire of network dynamics. Among these are sharp waves, sequential activations of ensembles of neurons that have been shown to be crucially involved in learning and memory. In the CA3 area of the hippocampus, two types of excitatory cells, thorny and a-thorny neurons, are preferentially active during distinct phases of a sharp wave, suggesting a differential role for these cell types in phenomena such as memory consolidation. Using a strictly data-driven approach, we built biophysically realistic models of both thorny and a-thorny cells and used them to investigate the integrative differences between these two cell types. We found that both neuron classes have the capability of integrating incoming synaptic inputs in a supralinear fashion, although only a-thorny cells respond with bursts of action potentials to spatially and temporally clustered synaptic inputs. Additionally, by using a computational approach based on information theory, we show that, owing to this propensity for bursting, a-thorny cells can encode more information in their spiking output than their thorny counterpart. These results shed new light on the computational capabilities of two types of excitatory neurons and suggest that thorny and a-thorny cells may play distinct roles in the generation of hippocampal network synchronization.

Ih blockade reduces cocaine-induced firing patterns of putative dopaminergic neurons of the ventral tegmental area in the anesthetized rat

The hyperpolarization-activated cation current (Ih) is a determinant of intrinsic excitability in various cells, including dopaminergic neurons (DA) of the ventral tegmental area (VTA). In contrast to other cellular conductances, Ih is activated by hyperpolarization negative to -55 mV and activating Ih produces a time-dependent depolarizing current. Our laboratory demonstrated that cocaine sensitization, a chronic cocaine behavioral model, significantly reduces Ih amplitude in VTA DA neurons. Despite this reduction in Ih, the spontaneous firing of VTA DA cells after cocaine sensitization remained similar to control groups. Although the role of Ih in controlling VTA DA excitability is still poorly understood, our hypothesis is that Ih reduction could play a role of a homeostatic controller compensating for cocaine-induced change in excitability. Using in vivo single-unit extracellular electrophysiology in isoflurane anesthetized rats, we explored the contribution of Ih on spontaneous firing patterns of VTA DA neurons. A key feature of spontaneous excitability is bursting activity; bursting is defined as trains of two or more spikes occurring within a short interval and followed by a prolonged period of inactivity. Burst activity increases the reliability of information transfer. To elucidate the contribution of Ih to spontaneous firing patterns of VTA DA neurons, we locally infused an Ih blocker (ZD 7288, 8.3 μM) and evaluated its effect. Ih blockade significantly reduced firing rate, bursting frequency, and percent of spikes within a burst. In addition, Ih blockade significantly reduced acute cocaine-induced spontaneous firing rate, bursting frequency, and percent of spikes within a burst. Using whole-cell patch-clamp, we determine the progressive reduction of Ih after acute and chronic cocaine administration (15 mg/k.g intraperitoneally). Our data show a significant reduction (~25%) in Ih amplitude after 24 but not 2 h of acute cocaine administration. These results suggest that a progressive reduction of Ih could serve as a homeostatic regulator of cocaine-induced spontaneous firing patterns related to VTA DA excitability.

MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data

Functional assessment of in vitro neuronal networks-of relevance for disease modelling and drug testing-can be performed using multi-electrode array (MEA) technology. However, the handling and processing of the large amount of data typically generated in MEA experiments remains a huge hurdle for researchers. Various software packages have been developed to tackle this issue, but to date, most are either not accessible through the links provided by the authors or only tackle parts of the analysis. Here, we present ''MEA-ToolBox'', a free open-source general MEA analytical toolbox that uses a variety of literature-based algorithms to process the data, detect spikes from raw recordings, and extract information at both the single-channel and array-wide network level. MEA-ToolBox extracts information about spike trains, burst-related analysis and connectivity metrics without the need of manual intervention. MEA-ToolBox is tailored for comparing different sets of measurements and will analyze data from multiple recorded files placed in the same folder sequentially, thus considerably streamlining the analysis pipeline. MEA-ToolBox is available with a graphic user interface (GUI) thus eliminating the need for any coding expertise while offering functionality to inspect, explore and post-process the data. As proof-of-concept, MEA-ToolBox was tested on earlier-published MEA recordings from neuronal networks derived from human induced pluripotent stem cells (hiPSCs) obtained from healthy subjects and patients with neurodevelopmental disorders. Neuronal networks derived from patient's hiPSCs showed a clear phenotype compared to those from healthy subjects, demonstrating that the toolbox could extract useful parameters and assess differences between normal and diseased profiles.

Cortical Representation of Touch in Silico

With its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents'. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex's granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.

A circuit of COCH neurons encodes social-stress-induced anxiety via MTF1 activation of Cacna1h

The hippocampus is a temporal lobe structure critical for cognition, such as learning, memory, and attention, as well as emotional responses. Hippocampal dysfunction can lead to persistent anxiety and/or depression. However, how millions of neurons in the hippocampus are molecularly and structurally organized to engage their divergent functions remains unknown. Here, we genetically target a subset of neurons expressing the coagulation factor c homolog (COCH) gene. COCH-expressing neurons or COCH neurons are topographically segregated in the distal region of the ventral CA3 hippocampus and express Mtf1 and Cacna1h. MTF1 activation of Cacna1h transcription in COCH neurons encodes the ability of COCH neurons to burst action potentials and cause social-stress-induced anxiety-like behaviors by synapsing directly with a subset of GABAergic inhibitory neurons in the lateral septum. Together, this study provides a molecular and circuitry-based framework for understanding how COCH neurons in the hippocampus are assembled to engage social behavior.

Increased burst coding in deep layers of the ventral anterior cingulate cortex during neuropathic pain

Neuropathic pain induces changes in neuronal excitability and synaptic connectivity in deep layers of the anterior cingulate cortex (ACC) that play a central role in the sensory, emotional and affective consequences of the disease. However, how this impacts ACC in vivo activity is not completely understood. Using a mouse model, we found that neuropathic pain caused an increase in ACC in vivo activity, as measured by the indirect activity marker c-Fos and juxtacellular electrophysiological recordings. The enhanced firing rate of ACC neurons in lesioned animals was based on a change in the firing pattern towards bursting activity. Despite the proportion of ACC neurons recruited by noxious stimuli was unchanged during neuropathic pain, responses to noxious stimuli were characterized by increased bursting. Thus, this change in coding pattern may have important implications for the processing of nociceptive information in the ACC and could be of great interest to guide the search for new treatment strategies for chronic pain.

Silencing of Activity During Hypoxia Improves Functional Outcomes in Motor Neuron Networks in vitro

The effects of hypoxia, or reduced oxygen supply, to brain tissue can be disastrous, leading to extensive loss of function. Deoxygenated tissue becomes unable to maintain healthy metabolism, which leads to increased production of reactive oxygen species (ROS) and loss of calcium homoeostasis, with damaging downstream effects. Neurons are a highly energy demanding cell type, and as such they are highly sensitive to reductions in oxygenation and some types of neurons such as motor neurons are even more susceptible to hypoxic damage. In addition to the immediate deleterious effects hypoxia can have on neurons, there can be delayed effects which lead to increased risk of developing neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), even if no immediate consequences are apparent. Furthermore, impairment of the function of various hypoxia-responsive factors has been shown to increase the risk of developing several neurodegenerative disorders. Longitudinal assessment of electrophysiological network activity is underutilised in assessing the effects of hypoxia on neurons and how their activity and communication change over time following a hypoxic challenge. This study utilised multielectrode arrays and motor neuron networks to study the response to hypoxia and the subsequent development of the neuronal activity over time, as well as the effect of silencing network activity during the hypoxic challenge. We found that motor neuron networks exposed to hypoxic challenge exhibited a delayed fluctuation in multiple network activity parameters compared to normoxic networks. Silencing of activity during the hypoxic challenge leads to maintained bursting activity, suggesting that functional outcomes are better maintained in these networks and that there are activity-dependent mechanisms involved in the network damage following hypoxia.

Emergence and function of cortical offset responses in sound termination detection

Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here, we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining in vivo electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated de novo. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization.

Noise-induced network bursts and coherence in a calcium-mediated neural network

Noise-induced population bursting has been widely identified to play important roles in information processes. We construct a mathematical model for a random and sparse heterogeneous neural network where bursting can be induced from a resting state by a global stochastic stimulus. Importantly, the noise-induced bursting dynamics of this network are mediated by calcium conductance. We use two spectral measures to evaluate network coherence in the context of the network bursts, the spike trains of all neurons, and the individual bursts of all neurons. Our results show that the coherence of the network is optimized by an optimal level of the stochastic stimulus, which is known as coherence resonance (CR). We also demonstrate that the interplay of the calcium conductance and noise intensity can modify the degree of CR.

Synaptic Dynamics Convey Differential Sensitivity to Input Pattern Changes in Two Muscles Innervated by the Same Motor Neurons

Postsynaptic responses depend on input patterns as well as short-term synaptic plasticity, summation, and postsynaptic membrane properties, but the interactions of those dynamics with realistic input patterns are not well understood. We recorded the responses of the two pyloric dilator (PD) muscles, cpv2a and cpv2b, that are innervated by and receive identical periodic bursting input from the same two motor neurons in the lobster Homarus americanus. Cpv2a and cpv2b showed quantitative differences in membrane nonlinearities and synaptic summation. At a short timescale, responses in both muscles were dominated by facilitation, albeit with different frequency and time dependence. Realistic burst stimulations revealed more substantial differences. Across bursts, cpv2a showed transient depression, whereas cpv2b showed transient facilitation. Steady-state responses to bursting input also differed substantially. Neither muscle had a monotonic dependence on frequency, but cpv2b showed particularly pronounced bandpass filtering. Cpv2a was sensitive to changes in both burst frequency and intra-burst spike frequency, whereas, despite its much slower responses, cpv2b was largely insensitive to changes in burst frequency. Cpv2a was sensitive to both burst duration and number of spikes per burst, whereas cpv2b was sensitive only to the former parameter. Neither muscle showed consistent sensitivity to changes in the overall spike interval structure, but cpv2b was surprisingly sensitive to changes in the first intervals in each burst, a parameter known to be regulated by dopamine (DA) modulation of spike propagation of the presynaptic axon. These findings highlight how seemingly minor circuit output changes mediated by neuromodulation could be read out differentially at the two synapses.

Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity

Cell assemblies are thought to be the substrate of memory in the brain. Theoretical studies have previously shown that assemblies can be formed in networks with multiple types of plasticity. But how exactly they are formed and how they encode information is yet to be fully understood. One possibility is that memories are stored in silent assemblies. Here we used a computational model to study the formation of silent assemblies in a network of spiking neurons with excitatory and inhibitory plasticity. We found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals. We also found that this spiking irregularity could be read out with support of short-term plasticity, and that it could contribute to the longevity of memories.

Corticothalamic Pathways From Layer 5: Emerging Roles in Computation and Pathology

Large portions of the thalamus receive strong driving input from cortical layer 5 (L5) neurons but the role of this important pathway in cortical and thalamic computations is not well understood. L5-recipient "higher-order" thalamic regions participate in cortico-thalamo-cortical (CTC) circuits that are increasingly recognized to be (1) anatomically and functionally distinct from better-studied "first-order" CTC networks, and (2) integral to cortical activity related to learning and perception. Additionally, studies are beginning to elucidate the clinical relevance of these networks, as dysfunction across these pathways have been implicated in several pathological states. In this review, we highlight recent advances in understanding L5 CTC networks across sensory modalities and brain regions, particularly studies leveraging cell-type-specific tools that allow precise experimental access to L5 CTC circuits. We aim to provide a focused and accessible summary of the anatomical, physiological, and computational properties of L5-originating CTC networks, and outline their underappreciated contribution in pathology. We particularly seek to connect single-neuron and synaptic properties to network (dys)function and emerging theories of cortical computation, and highlight information processing in L5 CTC networks as a promising focus for computational studies.

Excitatory synapses and gap junctions cooperate to improve Pv neuronal burst firing and cortical social cognition in Shank2-mutant mice

NMDA receptor (NMDAR) and GABA neuronal dysfunctions are observed in animal models of autism spectrum disorders, but how these dysfunctions impair social cognition and behavior remains unclear. We report here that NMDARs in cortical parvalbumin (Pv)-positive interneurons cooperate with gap junctions to promote high-frequency (>80 Hz) Pv neuronal burst firing and social cognition. Shank2^–/– mice, displaying improved sociability upon NMDAR activation, show impaired cortical social representation and inhibitory neuronal burst firing. Cortical Shank2^–/– Pv neurons show decreased NMDAR activity, which suppresses the cooperation between NMDARs and gap junctions (GJs) for normal burst firing. Shank2^–/– Pv neurons show compensatory increases in GJ activity that are not sufficient for social rescue. However, optogenetic boosting of Pv neuronal bursts, requiring GJs, rescues cortical social cognition in Shank2^–/– mice, similar to the NMDAR-dependent social rescue. Therefore, NMDARs and gap junctions cooperate to promote cortical Pv neuronal bursts and social cognition.

How NMDAR and GABA neuronal dysfunctions result in impaired social behaviour is unclear. Here, the authors show that NMDARs and gap junctions in cortical PV interneurons modulate burst firing, affecting social behaviour.