Burst firing in sensory systems

Compact Artificial Synapse-Neuron Module with Chemically Mediated Spiking Behaviors

Neuromorphic electronic devices mimicking the structure and functionality of biological counterparts have shown promising applications in biorealistic computing and bioelectronic interfaces. However, current neuromorphic systems comprising synapses and neurons typically exhibit complex integrated structures and lack chemically mediated characteristics, hindering them from direct biointerfacing. Here, we report a compact artificial synapse-neuron module (ASNM) by seamlessly integrating an organic electrochemical synaptic transistor and a niobium dioxide Mott memristor, showing the chemically mediated synaptic plasticity and highly stable spiking characteristics (>1010 cycles). Sodium ions and dopamine neurotransmitter induce the short-term and long-term plasticity of synaptic transistors, respectively, thus enabling temporary and long-term modulation of the ASNM's firing frequency in a bioplausible range (0-100 Hz). Furthermore, we construct a chemically mediated artificial neuromuscular system based on the ASNM, which could replicate the learning processes of a shooting basketball. These results demonstrate that our ASNM could achieve multiple biorealistic functionalities including sensing, synaptic plasticity, and spiking in a compact structure, providing a promising way for direct biointerfacing.

Context-Sensitive Processing in a Model Neocortical Pyramidal Cell With Two Sites of Input Integration

Neocortical layer 5 thick-tufted pyramidal cells are prone to exhibiting burst firing on receipt of coincident basal and apical dendritic inputs. These inputs carry different information, with basal inputs coming from feedforward sensory pathways and apical inputs coming from diverse sources that provide context in the cortical hierarchy. We explore the information processing possibilities of this burst firing using computer simulations of a noisy compartmental cell model. Simulated data on stochastic burst firing due to brief, simultaneously injected basal and apical currents allow estimation of burst firing probability for different stimulus current amplitudes. Information-theory-based partial information decomposition (PID) is used to quantify the contributions of the apical and basal input streams to the information in the cell output bursting probability. Four different operating regimes are apparent, depending on the relative strengths of the input streams, with output burst probability carrying more or less information that is uniquely contributed by either the basal or apical input, or shared and synergistic information due to the combined streams. We derive and fit transfer functions for these different regimes that describe burst probability over the different ranges of basal and apical input amplitudes. The operating regimes can be classified into distinct modes of information processing, depending on the contribution of apical input to output bursting: apical cooperation, in which both basal and apical inputs are required to generate a burst; apical amplification, in which basal input alone can generate a burst but the burst probability is modulated by apical input; apical drive, in which apical input alone can produce a burst; and apical integration, in which strong apical or basal inputs alone, as well as their combination, can generate bursting. In particular, PID and the transfer function clarify that the apical amplification mode has the features required for contextually modulated information processing.

Effect of burst spikes on linear and nonlinear signal transmission in spiking neurons

We study the impact of bursts on spike statistics and neural signal transmission. We propose a stochastic burst algorithm that is applied to a burst-free spike train and adds a random number of temporally-jittered burst spikes to each spike. This simple algorithm ignores any possible stimulus-dependence of bursting but allows to relate spectra and signal-transmission characteristics of burst-free and burst-endowed spike trains. By averaging over the various statistical ensembles, we find a frequency-dependent factor connecting the linear and also the second-order susceptibility of the spike trains with and without bursts. The relation between spectra is more complicated: besides a frequency-dependent multiplicative factor it also involves an additional frequency-dependent offset. We confirm these relations for the (burst-free) spike trains of a stochastic integrate-and-fire neuron and identify frequency ranges in which the transmission is boosted or diminished by bursting. We then consider bursty spike trains of electroreceptor afferents of weakly electric fish and approach the role of burst spikes as follows. We compare the spectral statistics of the bursty spike train to (i) that of a spike train with burst spikes removed and to (ii) that of the spike train in (i) endowed by bursts according to our algorithm. Significant spectral features are explained by our signal-independent burst algorithm, e.g. the burst-induced boosting of the nonlinear response. A difference is seen in the information transfer for the original bursty spike train and our burst-endowed spike train. Our algorithm is thus helpful to identify different effects of bursting.

Toward autonomous event-based sensorimotor control with supervised gait learning and obstacle avoidance for robot navigation

Miniature robots are useful during disaster response and accessing remote or unsafe areas. They need to navigate uneven terrains without supervision and under severe resource constraints such as limited compute, storage and power budget. Event-based sensorimotor control in edge robotics has potential to enable fully autonomous and adaptive robot navigation systems capable of responding to environmental fluctuations by learning new types of motion and real-time decision making to avoid obstacles. This work presents a novel bio-inspired framework with a hierarchical control system to address these limitations, utilizing a tunable multi-layer neural network with a hardware-friendly Central Pattern Generator (CPG) as the core coordinator to govern the precise timing of periodic motion. Autonomous operation is managed by a Dynamic State Machine (DSM) at the top of the hierarchy, providing the necessary adaptability to handle environmental challenges such as obstacles or uneven terrain. The multi-layer neural network uses a nonlinear neuron model which employs mixed feedback at multiple timescales to produce rhythmic patterns of bursting events to control the motors. A comprehensive study of the architecture's building blocks is presented along with a detailed analysis of network equations. Finally, we demonstrate the proposed framework on the Petoi robot, which can autonomously learn walk and crawl gaits using supervised Spike-Time Dependent Plasticity (STDP) learning algorithm, transition between the learned gaits stored as new states, through the DSM for real-time obstacle avoidance. Measured results of the system performance are summarized and compared with other works to highlight our unique contributions.

Bio-plausible reconfigurable spiking neuron for neuromorphic computing

Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behaviors due to high cost of emulating these biological spike patterns. Here, we propose a compact reconfigurable neuron design using the intrinsic dynamics of a NbO2-based spiking unit and excellent tunability in an electrochemical memory (ECRAM) to emulate the fast-slow dynamics in a bio-plausible neuron. The resistance of the ECRAM was effective in tuning the temporal dynamics of the membrane potential, contributing to flexible reconfiguration of various bio-plausible firing modes, such as phasic and burst spiking, and exhibiting adaptive spiking behaviors in changing environment. We used the bio-plausible neuron model to build spiking neural networks with bursting neurons and demonstrated improved classification accuracies over simplified models, showing great promises for use in more bio-plausible neuromorphic computing systems.

Chondroitin Sulfate and Proteinoids in Neuron Models

This study examines the relationship between chondroitin sulfate, proteinoids, and computational neuron models, with a specific emphasis on the Izhikevich neuron model. We investigate the effect of chondroitin sulfate-proteinoid complexes on the behavior and dynamics of simulated neurons. Through the use of computational simulations, we provide evidence that these biomolecular components have the power to regulate the responsiveness of neurons, the patterns of their firing, and the ability of their synapses to change within the Izhikevich architecture. The findings suggest that the interactions between chondroitin sulfate and proteinoid cause notable alterations in the dynamics of membrane potential and the timing of spikes. We detect adjustments in the features of neuronal responses, such as shifts in the thresholds for firing, alterations in spike frequency adaptation, and changes to bursting patterns. The findings indicate that chondroitin sulfate and proteinoids may have a role in precisely adjusting neuronal information processing and network behavior. This study offers valuable information about the complex connection between the many components of the extracellular matrix, protein-based structures, and the functioning of neurons. In addition, our analysis of the proteinoid-chondroitine system using game theory uncovers a significant Prisoner's Dilemma scenario. The system's inclination toward defection, due to the appeal of cheating and the significant penalty for cooperation, with a mean voltage of -9.19 mV, indicates that defective behaviors may prevail in the long term dynamics of these neuronal interactions.

Cross-modal sensitivities to auditory and visual stimulations in the first-order somatosensory thalamic nucleus

The ventral posterolateral nucleus (VPL), being categorized as the first-order thalamic nucleus, is considered to be dedicated to uni-modal somatosensory processing. Cross-modal sensory interactions on thalamic reticular nucleus cells projecting to the VPL, on the other hand, suggest that VPL cells are subject to cross-modal sensory influences. To test this possibility, the effects of auditory or visual stimulation on VPL cell activities were examined in anaesthetized rats, using juxta-cellular recording and labelling techniques. Recordings were obtained from 70 VPL cells, including 65 cells responsive to cutaneous electrical stimulation of the hindpaw. Auditory or visual alone stimulation did not elicit cell activity except in three bi-modal cells and one auditory cell. Cross-modal alterations of somatosensory response by auditory and/or visual stimulation were recognized in 61 cells with regard to the response magnitude, latency (time and jitter) and/or burst spiking properties. Both early (onset) and late responses were either suppressed or facilitated, and de novo cell activity was also induced. Cross-modal alterations took place depending on the temporal interval between the preceding counterpart and somatosensory stimulations, the intensity and frequency of sound. Alterations were observed mostly at short intervals (< 200 ms) and up to 800 ms intervals. Sounds of higher intensities and lower frequencies were more effective for modulation. The susceptibility to cross-modal influences was related to cell location and/or morphology. These and previously reported similar findings in the auditory and visual thalamic nuclei suggest that cross-modal sensory interactions pervasively take place in the first-order sensory thalamic nuclei.

Kappa opioids inhibit spinal output neurons to suppress itch

Itch is a protective sensation that drives scratching. Although specific cell types have been proposed to underlie itch, the neural basis for itch remains unclear. Here, we used two-photon Ca2+ imaging of the dorsal horn to visualize neuronal populations that are activated by itch-inducing agents. We identify a convergent population of spinal interneurons recruited by diverse itch-causing stimuli that represents a subset of neurons that express the gastrin-releasing peptide receptor (GRPR). Moreover, we find that itch is conveyed to the brain via GRPR-expressing spinal output neurons that target the lateral parabrachial nuclei. We then show that the kappa opioid receptor agonist nalfurafine relieves itch by selectively inhibiting GRPR spinoparabrachial neurons. These experiments provide a population-level view of the spinal neurons that respond to pruritic stimuli, pinpoint the output neurons that convey itch to the brain, and identify the cellular target of kappa opioid receptor agonists for the inhibition of itch.

Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex

Visual landmarks provide powerful reference signals for efficient navigation by altering the activity of spatially tuned neurons, such as place cells, head direction cells, and grid cells. To understand the neural mechanism by which landmarks exert such strong influence, it is necessary to identify how these visual features gain spatial meaning. In this study, we characterized visual landmark representations in mouse retrosplenial cortex (RSC) using chronic two-photon imaging of the same neuronal ensembles over the course of spatial learning. We found a pronounced increase in landmark-referenced activity in RSC neurons that, once established, remained stable across days. Changing behavioral context by uncoupling treadmill motion from visual feedback systematically altered neuronal responses associated with the coherence between visual scene flow speed and self-motion. To explore potential underlying mechanisms, we modeled how burst firing, mediated by supralinear somatodendritic interactions, could efficiently mediate context- and coherence-dependent integration of landmark information. Our results show that visual encoding shifts to landmark-referenced and context-dependent codes as these cues take on spatial meaning during learning.

Investigating Communication Dynamics in Neuronal Network using 3D Gold Microelectrode Arrays

Although in vitro neuronal network models hold great potential for advancing neuroscience research, with the capacity to provide fundamental insights into mechanisms underlying neuronal functions, the dynamics of cell communication within such networks remain poorly understood. Here, we develop a customizable, polymer modified three-dimensional gold microelectrode array with sufficient stability for high signal-to-noise, long-term, neuronal recording of cultured networks. By using directed spatial and temporal patterns of electrical stimulation of cells to explore synaptic-based communication, we monitored cell network dynamics over 3 weeks, quantifying communication capability using correlation heatmaps and mutual information networks. Analysis of synaptic delay and signal speed between cells enabled us to establish a communication connectivity model. We anticipate that our discoveries of the dynamic changes in communication across the neuronal network will provide a valuable tool for future studies in understanding health and disease as well as in developing effective platforms for evaluating therapies.

Network motifs in cellular neurophysiology

Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.

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.

Dorsal root ganglion: a key to understanding the therapeutic effects of the erector spinae plane (ESP) and other intertransverse process blocks?

Since its description in 2016, the erector spinae plane block (ESPB) has become a widely employed regional anesthetic technique and kindled interest in a range of related techniques, collectively termed intertransverse process blocks. There has been ongoing controversy over mechanism of action of the ESPB, mainly due to incongruities between results of cutaneous sensory testing, clinical efficacy studies, and investigations into the neural structures that are reached by injected local anesthetic (LA). This paper reviews the spread of LA to the paravertebral and epidural space and the cutaneous anesthesia in ESPB, with specific emphasis on the dorsal root ganglion (DRG). We hypothesize that the DRG, due to its unique and complex microarchitecture, represents a key therapeutic target for modulation of nociceptive signaling in regional anesthesia. This paper discusses how the anatomical and physiological characteristics of the DRG may be one of the factors underpinning the clinical analgesia observed in ESPB and other intertransverse process blocks.

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.

Neonatal di-(2-ethylhexyl)phthalate exposure induces permanent alterations in secretory CRH neuron characteristics in the hypothalamus paraventricular region of adult male rats

Corticotrophin-releasing hormone (CRH) neurons in the hypothalamic paraventricular nucleus (PVN) play a critical role in the modulation of the hypothalamic-pituitary-adrenal (HPA) axis. Early-life exposure to di-(2-ethylhexyl) phthalate (DEHP) has been associated with an increased risk of developing psychiatric disorders in adulthood. The present work was designed to explore the impact of neonatal exposure to DEHP on adult PVN CRH neuronal activity. DEHP or vehicle was given to male rat pups from PND16 to PND22. Then, anxiety-like behaviors, serum corticosterone and testosterone, immunohistochemistry, western blotting, fluorescence in situ hybridization and acute ex vivo slice electrophysiological recordings were used to evaluate the influence of DEHP on adult PVN secretory CRH neurons. Neonatal DEHP-exposed rats exhibited enhanced anxiety-like behaviors in adults, with an increase in CORT. Secretory CRH neurons showed higher spontaneous firing activity but could be inhibited by GABAAR blockers. CRH neurons displayed fewer firing spikes, prolonged first-spike latency, depolarizing shifts in GABA reversal potential and strengthened GABAergic inputs, as indicated by increases in the frequency and amplitude of sIPSCs. Enhancement of GABAergic transmission was accompanied by upregulated expression of GAD67 and downregulated expression of GABABR1, KCC2 and GAT1. These findings suggest that neonatal exposure to DEHP permanently altered the characteristics of secretory CRH neurons in the PVN, which may contribute to the development of psychiatric disorders later in life.

Holistic bursting cells store long-term memory in auditory cortex

The sensory neocortex has been suggested to be a substrate for long-term memory storage, yet which exact single cells could be specific candidates underlying such long-term memory storage remained neither known nor visible for over a century. Here, using a combination of day-by-day two-photon Ca2+ imaging and targeted single-cell loose-patch recording in an auditory associative learning paradigm with composite sounds in male mice, we reveal sparsely distributed neurons in layer 2/3 of auditory cortex emerged step-wise from quiescence into bursting mode, which then invariably expressed holistic information of the learned composite sounds, referred to as holistic bursting (HB) cells. Notably, it was not shuffled populations but the same sparse HB cells that embodied the behavioral relevance of the learned composite sounds, pinpointing HB cells as physiologically-defined single-cell candidates of an engram underlying long-term memory storage in auditory cortex.

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models

Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of observed data points according to another Poisson process. This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i.e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data. While many specialized algorithms have been developed for DPMMs, comparatively fewer works have focused on inference in NSPs. Here, we present novel connections between NSPs and DPMMs, with the key link being a third class of Bayesian mixture models called mixture of finite mixture models (MFMMs). Leveraging this connection, we adapt the standard collapsed Gibbs sampling algorithm for DPMMs to enable scalable Bayesian inference on NSP models. We demonstrate the potential of Neyman-Scott processes on a variety of applications including sequence detection in neural spike trains and event detection in document streams.

Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes

Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.

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.

Differential Effects of Clozapine and Haloperidol on the 40 Hz Auditory Steady State Response-mediated Phase Resetting in the Prefrontal Cortex of the Female Sprague Dawley Rat

BACKGROUND

Neural synchrony at gamma frequency (~40 Hz) is important for information processing and is disrupted in schizophrenia. From a drug development perspective, molecules that can improve local gamma synchrony are promising candidates for therapeutic development.

HYPOTHESIS

Given their differentiated clinical profile, clozapine, and haloperidol may have distinct effects on local gamma synchrony engendered by 40 Hz click trains, the so-called auditory steady-state response (ASSR).

STUDY DESIGN

Clozapine and haloperidol at doses known to mimic clinically relevant D2 receptor occupancy were evaluated using the ASSR in separate cohorts of female SD rats.

RESULTS

Clozapine (2.5-10 mg/kg, sc) robustly increased intertrial phase coherence (ITC), across all doses. Evoked response increased but less consistently. Background gamma activity, unrelated to the stimulus, showed a reduction at all doses. Closer scrutiny of the data indicated that clozapine accelerated gamma phase resetting. Thus, clozapine augmented auditory information processing in the gamma frequency range by reducing the background gamma, accelerating the gamma phase resetting and improving phase precision and signal power. Modest improvements in ITC were seen with Haloperidol (0.08 and 0.24 mg/kg, sc) without accelerating phase resetting. Evoked power was unaffected while background gamma was reduced at high doses only, which also caused catalepsy.

CONCLUSIONS

Using click-train evoked gamma synchrony as an index of local neural network function, we provide a plausible neurophysiological basis for the superior and differentiated profile of clozapine. These observations may provide a neurophysiological template for identifying new drug candidates with a therapeutic potential for treatment-resistant schizophrenia.

Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes

We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking pattern. The SNN includes excitatory and inhibitory neurons in some proportion, sustaining the excitation-inhibition balance of autonomous firing. The astrocytes accompanying each excitatory synapse provide a slow modulation of synaptic transmission strength. An information image was uploaded to the network in the form of excitatory stimulation pulses distributed in time reproducing the shape of the image. We found that astrocytic modulation prevented stimulation-induced SNN hyperexcitation and non-periodic bursting activity. Such homeostatic astrocytic regulation of neuronal activity makes it possible to restore the image supplied during stimulation and lost in the raster diagram of neuronal activity due to non-periodic neuronal firing. At a biological point, our model shows that astrocytes can act as an additional adaptive mechanism for regulating neural activity, which is crucial for sensory cortical representations.

Sparse spike trains and the limitation of rate codes underlying rapid behaviours

Animals live in dynamic worlds where they use sensorimotor circuits to rapidly process information and drive behaviours. For example, dragonflies are aerial predators that react to movements of prey within tens of milliseconds. These pursuits are likely controlled by identified neurons in the dragonfly, which have well-characterized physiological responses to moving targets. Predominantly, neural activity in these circuits is interpreted in context of a rate code, where information is conveyed by changes in the number of spikes over a time period. However, such a description of neuronal activity is difficult to achieve in real-world, real-time scenarios. Here, we contrast a neuroscientists' post-hoc view of spiking activity with the information available to the animal in real-time. We describe how performance of a rate code is readily overestimated and outline a rate code's significant limitations in driving rapid behaviours.

Calbindin-Expressing CA1 Pyramidal Neurons Encode Spatial Information More Efficiently

Hippocampal pyramidal neurons (PNs) are traditionally conceptualized as homogeneous population. For the past few years, cumulating evidence has revealed the structural and functional heterogeneity of hippocampal pyramidal neurons. But the in vivo neuronal firing pattern of molecularly identified pyramidal neuron subclasses is still absent. In this study, we investigated the firing patterns of hippocampal PNs based on different expression profile of Calbindin (CB) during a spatial shuttle task in free moving male mice. We found that CB+ place cells can represent spatial information more efficiently than CB- place cells, albeit lower firing rates during running epochs. Furthermore, a subset of CB+ PNs shifted their theta firing phase during rapid-eye movement (REM) sleep states compared with running states. Although CB- PNs are more actively engaged in ripple oscillations, CB+ PNs showed stronger ripple modulation during slow-wave sleep (SWS). Our results pointed out the heterogeneity in neuronal representation between hippocampal CB+ and CB- PNs. Particularly, CB+ PNs encode spatial information more efficiently, which might be contributed by stronger afferents from the lateral entorhinal cortex to CB+ PNs.

Dynamical properties of neuromorphic Josephson junctions

Neuromorphic computing exploits the dynamical analogy between many physical systems and neuron biophysics. Superconductor systems, in particular, are excellent candidates for neuromorphic devices due to their capacity to operate at great speeds and with low energy dissipation compared to their silicon counterparts. In this paper, we revisit a prior work on Josephson Junction-based neurons to identify the exact dynamical mechanisms underlying the system's neuronlike properties and reveal complex behaviors which are relevant for neurocomputation and the design of superconducting neuromorphic devices. Our paper lies at the intersection of superconducting physics and theoretical neuroscience, both viewed under a common framework-that of nonlinear dynamics theory.

Psilocybin reduces low frequency oscillatory power and neuronal phase-locking in the anterior cingulate cortex of awake rodents

Psilocybin is a hallucinogenic compound that is showing promise in the ability to treat neurological conditions such as depression and post-traumatic stress disorder. There have been several investigations into the neural correlates of psilocybin administration using non-invasive methods, however, there has yet to be an invasive study of the mechanism of action in awake rodents. Using multi-unit extracellular recordings, we recorded local field potential and spiking activity from populations of neurons in the anterior cingulate cortex of awake mice during the administration of psilocybin (2 mg/kg). The power of low frequency bands in the local field potential was found to significantly decrease in response to psilocybin administration, whilst gamma band activity trended towards an increase. The population firing rate was found to increase overall, with just under half of individual neurons showing a significant increase. Psilocybin significantly decreased the level of phase modulation of cells with each neural frequency band except high-gamma oscillations, consistent with a desynchronization of cortical populations. Furthermore, bursting behavior was altered in a subset of cells, with both positive and negative changes in the rate of bursting. Neurons that increased their burst firing following psilocybin administration were highly likely to transition from a phase-modulated to a phase unmodulated state. Taken together, psilocybin reduces low frequency oscillatory power, increases overall firing rates and desynchronizes local neural activity. These findings are consistent with dissolution of the default mode network under psilocybin, and may be indicative of disruption of top-down processing in the acute psychedelic state.

The burst gap is a peripheral temporal code for pitch perception that is shared across audition and touch

When tactile afferents were manipulated to fire in periodic bursts of spikes, we discovered that the perceived pitch corresponded to the inter-burst interval (burst gap) in a spike train, rather than the spike rate or burst periodicity as previously thought. Given that tactile frequency mechanisms have many analogies to audition, and indications that temporal frequency channels are linked across the two modalities, we investigated whether there is burst gap temporal encoding in the auditory system. To link this putative neural code to perception, human subjects (n = 13, 6 females) assessed pitch elicited by trains of temporally-structured acoustic pulses in psychophysical experiments. Each pulse was designed to excite a fixed population of cochlear neurons, precluding place of excitation cues, and to elicit desired temporal spike trains in activated afferents. We tested periodicities up to 150 Hz using a variety of burst patterns and found striking deviations from periodicity-predicted pitch. Like the tactile system, the duration of the silent gap between successive bursts of neural activity best predicted perceived pitch, emphasising the role of peripheral temporal coding in shaping pitch. This suggests that temporal patterning of stimulus pulses in cochlear implant users might improve pitch perception.

State-dependent activity dynamics of hypothalamic stress effector neurons

The stress response necessitates an immediate boost in vital physiological functions from their homeostatic operation to an elevated emergency response. However, the neural mechanisms underlying this state-dependent change remain largely unknown. Using a combination of in vivo and ex vivo electrophysiology with computational modeling, we report that corticotropin releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN), the effector neurons of hormonal stress response, rapidly transition between distinct activity states through recurrent inhibition. Specifically, in vivo optrode recording shows that under non-stress conditions, CRH_PVN neurons often fire with rhythmic brief bursts (RB), which, somewhat counterintuitively, constrains firing rate due to long (~2 s) interburst intervals. Stressful stimuli rapidly switch RB to continuous single spiking (SS), permitting a large increase in firing rate. A spiking network model shows that recurrent inhibition can control this activity-state switch, and more broadly the gain of spiking responses to excitatory inputs. In biological CRH_PVN neurons ex vivo, the injection of whole-cell currents derived from our computational model recreates the in vivo-like switch between RB and SS, providing direct evidence that physiologically relevant network inputs enable state-dependent computation in single neurons. Together, we present a novel mechanism for state-dependent activity dynamics in CRH_PVN neurons.

Early lock-in of structured and specialised information flows during neural development

The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for spiking data. Here, we leverage recent advances in this estimation task in order to quantify the changes in transfer entropy during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows exhibits a tendency to lock-in at the point when they arise. We also characterise the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes tend to undertake specialised computational roles as either transmitters, mediators, or receivers of information, with these roles tending to align with their average spike ordering. Further, we find that these roles are regularly locked-in when the information flows are established. Finally, we compare these results to information flows in a model network developing according to a spike-timing-dependent plasticity learning rule. Similar temporal patterns in the development of information flows were observed in these networks, hinting at the broader generality of these phenomena.

The neural hierarchy of consciousness

The chief undertaking in the studies of consciousness is that of unravelling "the minimal set of neural processes that are together sufficient for the conscious experience of a particular content - the neural correlates of consciousness". To this day, this crusade remains at an impasse, with a clash of two main theories: consciousness may arise either in a graded and cortically-localised fashion, or in an all-or-none and widespread one. In spite of the long-lasting theoretical debates, neurophysiological theories of consciousness have been mostly dissociated from them. Herein, a theoretical review will be put forth with the aim to change that. In its first half, we will cover the hard available evidence on the neurophysiology of consciousness, whereas in its second half we will weave a series of considerations on both theories and substantiate a novel take on conscious awareness: the levels of processing approach, partitioning the conscious architecture into lower- and higher-order, graded and nonlinear.

An electrophysiological perspective on Parkinson's disease: symptomatic pathogenesis and therapeutic approaches

Parkinson's disease (PD), or paralysis agitans, is a common neurodegenerative disease characterized by dopaminergic deprivation in the basal ganglia because of neuronal loss in the substantia nigra pars compacta. Clinically, PD apparently involves both hypokinetic (e.g. akinetic rigidity) and hyperkinetic (e.g. tremor/propulsion) symptoms. The symptomatic pathogenesis, however, has remained elusive. The recent success of deep brain stimulation (DBS) therapy applied to the subthalamic nucleus (STN) or the globus pallidus pars internus indicates that there are essential electrophysiological abnormalities in PD. Consistently, dopamine-deprived STN shows excessive burst discharges. This proves to be a central pathophysiological element causally linked to the locomotor deficits in PD, as maneuvers (such as DBS of different polarities) decreasing and increasing STN burst discharges would decrease and increase the locomotor deficits, respectively. STN bursts are not so autonomous but show a "relay" feature, requiring glutamatergic synaptic inputs from the motor cortex (MC) to develop. In PD, there is an increase in overall MC activities and the corticosubthalamic input is enhanced and contributory to excessive burst discharges in STN. The increase in MC activities may be relevant to the enhanced beta power in local field potentials (LFP) as well as the deranged motor programming at the cortical level in PD. Moreover, MC could not only drive erroneous STN bursts, but also be driven by STN discharges at specific LFP frequencies (~ 4 to 6 Hz) to produce coherent tremulous muscle contractions. In essence, PD may be viewed as a disorder with deranged rhythms in the cortico-subcortical re-entrant loops, manifestly including STN, the major component of the oscillating core, and MC, the origin of the final common descending motor pathways. The configurations of the deranged rhythms may play a determinant role in the symptomatic pathogenesis of PD, and provide insight into the mechanism underlying normal motor control. Therapeutic brain stimulation for PD and relevant disorders should be adaptively exercised with in-depth pathophysiological considerations for each individual patient, and aim at a final normalization of cortical discharge patterns for the best ameliorating effect on the locomotor and even non-motor symptoms.

Burst control: Synaptic conditions for burst generation in cortical layer 5 pyramidal neurons

The output of neocortical layer 5 pyramidal cells (L5PCs) is expressed by a train of single spikes with intermittent bursts of multiple spikes at high frequencies. The bursts are the result of nonlinear dendritic properties, including Na+, Ca2+, and NMDA spikes, that interact with the ~10,000 synapses impinging on the neuron's dendrites. Output spike bursts are thought to implement key dendritic computations, such as coincidence detection of bottom-up inputs (arriving mostly at the basal tree) and top-down inputs (arriving mostly at the apical tree). In this study we used a detailed nonlinear model of L5PC receiving excitatory and inhibitory synaptic inputs to explore the conditions for generating bursts and for modulating their properties. We established the excitatory input conditions on the basal versus the apical tree that favor burst and show that there are two distinct types of bursts. Bursts consisting of 3 or more spikes firing at < 200 Hz, which are generated by stronger excitatory input to the basal versus the apical tree, and bursts of ~2-spikes at ~250 Hz, generated by prominent apical tuft excitation. Localized and well-timed dendritic inhibition on the apical tree differentially modulates Na+, Ca2+, and NMDA spikes and, consequently, finely controls the burst output. Finally, we explored the implications of different burst classes and respective dendritic inhibition for regulating synaptic plasticity.

A synaptic learning rule for exploiting nonlinear dendritic computation

Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.

Neural burst codes disguised as rate codes

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

Burst activity plays no role in the field-to-field variability and rate remapping of grid cells

Grid cells in rodent medial entorhinal cortex are thought to play a key role for spatial navigation. When the animal is freely moving in an open arena the firing fields of each grid cell tend to form a highly regular, hexagonal lattice spanning the environment. However, firing rates vary from field to field and change under contextual modifications, whereas the field locations shift at most by a small amount under such "rate remapping." The observed differences in firing rate could reflect overall activity changes or changes in the detailed spike-train statistics. As these two alternatives imply distinct neural coding schemes, we investigated whether temporal firing patterns vary from field to field and whether they change under rate remapping. Focusing on short time scales, we found that the proportion of bursts compared to all discharge events is similar in all firing fields of a given grid cell and does not change under rate remapping. For each cell, mean firing rates with bursts are proportional to mean firing rates without bursts. However, this ratio varies across cells. Additionally, we looked at how rate remapping relates to entorhinal theta-frequency oscillations. Theta-phase coding was preserved despite firing-rate changes from rate remapping but we did not observe differences between the first and second half of the theta cycle, as had been reported for CA1. Our results indicate that both, the heterogeneity between firing fields and rate remapping, are not due to altered firing patterns on short time scales but reflect location-specific changes at the firing-rate level.

Neurophysiological basis of the N400 deflection, from Mismatch Negativity to Semantic Prediction Potentials and late positive components

The first theoretical model on the neurophysiological basis of the N400: the deflection reflects layer I dendritic plateaus on a preparatory state of synaptic integration that precedes layer V somatic burst firing for conscious identification of the higher-order features of the stimulus (a late positive shift). Plateaus ensue from apical disinhibition by vasoactive intestinal polypeptide-positive interneurons (VIPs) through suppression of Martinotti cells, opening the gates for glutamatergic feedback to trigger dendritic regenerative potentials. Cholinergic transients contribute to these dynamics directly, holding a central role in the N400 deflection. The stereotypical timing of the (frontal) glutamatergic feedback and the accompanying cholinergic transients account for the enigmatic "invariability" of the peak latency in the face of a gamut of different stimuli and paradigms. The theoretical postulations presented here may bring about unprecedented level of detail for the N400 deflection to be used in the study of schizophrenia, Alzheimer's disease and other higher-order pathologies. The substrates of a late positive component, the Mismatch Negativity and the Semantic Prediction Potentials are also surveyed.

Attention reduces the burstiness of V1 neurons involved in attended target enhancement

Attention-dependent reduction in the tendency for neurons to fire bursts (burstiness) is widely observed in the visual cortex. However, the underlying mechanism and the functional role of this phenomenon remain unclear. We recorded well-isolated single-unit activities in primary visual cortex (V1) from two primates (Macaca mulatta) while they performed a detection task engaging spatial attention with two levels of difficulty (hard/easy). We found that attention modulated burstiness of V1 neurons in a cell-type specific manner. For neurons whose net response enhanced with the increase of task difficulty (difficulty-enhanced neuron), representing their involvement in boosting the signal of the attended stimulus, attention led to a reduction in burstiness during hard task but not during easy task. In contrast, regardless of the level of task difficulty, attention-dependent reduction in burstiness was not observed in neurons that showed a net suppression in firing rate with the increase of task difficulty (difficulty-suppressed neuron), indicating their commitment in filtering out the interference of distractor. This differentiation in the effects of attentional modulation on burstiness among the cells with distinct functional roles in attention suggests that the reduction in burstiness by attention is linked to target enhancement and is not associated with distractor suppression.

Involvement of the thalamic reticular nucleus in prepulse inhibition of acoustic startle

Thalamic reticular nucleus (TRN) is a group of inhibitory neurons surrounding the thalamus. Due to its important role in sensory information processing, TRN is considered as the target nucleus for the pathophysiological investigation of schizophrenia and autism spectrum disorder (ASD). Prepulse inhibition (PPI) of acoustic startle response, a phenomenon that strong stimulus-induced startle reflex is reduced by a weaker prestimulus, is always found impaired in schizophrenia and ASD. But the role of TRN in PPI modulation remains unknown. Here, we report that parvalbumin-expressing (PV+) neurons in TRN are activated by sound stimulation of PPI paradigm. Chemogenetic inhibition of PV+ neurons in TRN impairs PPI performance. Further investigations on the mechanism suggest a model of burst-rebound burst firing in TRN-auditory thalamus (medial geniculate nucleus, MG) circuitry. The burst firing is mediated by T-type calcium channel in TRN, and rebound burst firing needs the participation of GABAB receptor in MG. Overall, these findings support the involvement of TRN in PPI modulation.

Involvement of NMDA and GABA(A) receptors in modulation of spontaneous activity in hippocampal culture: Interrelations between burst firing and intracellular calcium signal

Spontaneous burst firing is a hallmark attributed to the neuronal network activity. It is known to be accompanied by intracellular calcium [Са²⁺]_i oscillations within the bursting neurons. Studying mechanisms underlying regulation of burst firing is highly relevant, since impairment in neuronal bursting accompanies different neurological disorders. In the present study, the contribution of NMDA and GABA(A) receptors to the shape formation of spontaneous burst -was studied in cultured hippocampal neurons. A combination of inhibitory analysis with simultaneous registration of neuronal bursting by whole-cell patch clamp and calcium imaging was used to assess spontaneous burst firing and [Са²⁺]_i level. Using bicuculline and D-AP5 we showed that GABA(A) and NMDA receptors effectively modulate burst plateau phase and [Са²⁺]_i transient spike which can further affect action potential (AP) amplitudes and firing frequency within a burst. Bicuculline significantly elevated the amplitude and reduced the duration of both burst plateau phase and [Са²⁺]_i spike resulting in an increase of AP firing frequency and shortening of AP amplitudes within a burst. D-AP5 significantly decreases the amplitude of both plateau phase and [Са²⁺]_i spike along with a burst duration that correlated with an increase in AP amplitudes and reduced firing frequency within a burst. The effect of bicuculline was occluded by co-addition of D-AP5 revealing modulatory role of GABA(A) receptors to the NMDA receptor-mediated formation of the burst. Our results provide new evidence on importance of NMDA and GABA(A) receptors in shaping burst firing and Ca²⁺transient spikes in cultured hippocampal neurons.

Distinct burst properties contribute to the functional diversity of thalamic nuclei

Thalamic neurons fire spikes in two modes, burst and tonic. The function of burst firing is unclear, but the evidence suggests that bursts are more effective at activating cortical cells, and that postinhibition rebound bursting contributes to thalamocortical oscillations during sleep. Bursts are considered stereotyped signals; however, there is limited evidence regarding how burst properties compare across thalamic nuclei of different functional or anatomical organization. Here, we used whole-cell patch clamp recordings and compartmental modeling to investigate the properties of bursts in six sensory thalamic nuclei, to study the mechanisms that can lead to different burst properties, and to assess the implications of different burst properties for thalamocortical transmission and oscillatory functions. We found that bursts in higher-order cells on average had higher number of spikes and longer latency to the first spike. Additionally, burst features in first-order neurons were determined by sensory modality. Shifting the voltage-dependence and density of the T-channel conductance in a compartmental model replicates the burst properties from the intracellular recordings, pointing to molecular mechanisms that can generate burst diversity. Furthermore, the model predicts that bursts with higher number of spikes will drastically reduce the effectiveness of thalamocortical transmission. In addition, the latency to burst limited the rebound oscillatory frequency in modeled cells. These results demonstrate that burst properties vary according to the thalamocortical hierarchy and with sensory modality. The findings imply that, while in burst mode, thalamocortical transmission and firing frequency will be determined by the number of spikes and latency to burst.

The unknown but knowable relationship between Presaccadic Accumulation of activity and Saccade initiation

The goal of this short review is to call attention to a yawning gap of knowledge that separates two processes essential for saccade production. On the one hand, knowledge about the saccade generation circuitry within the brainstem is detailed and precise - push-pull interactions between gaze-shifting and gaze-holding processes control the time of saccade initiation, which begins when omnipause neurons are inhibited and brainstem burst neurons are excited. On the other hand, knowledge about the cortical and subcortical premotor circuitry accomplishing saccade initiation has crystalized around the concept of stochastic accumulation - the accumulating activity of saccade neurons reaching a fixed value triggers a saccade. Here is the gap: we do not know how the reaching of a threshold by premotor neurons causes the critical pause and burst of brainstem neurons that initiates saccades. Why this problem matters and how it can be addressed will be discussed. Closing the gap would unify two rich but curiously disconnected empirical and theoretical domains.

Spike bursting in a dragonfly target-detecting neuron

Dragonflies visually detect prey and conspecifics, rapidly pursuing these targets via acrobatic flights. Over many decades, studies have investigated the elaborate neuronal circuits proposed to underlie this rapid behaviour. A subset of dragonfly visual neurons exhibit exquisite tuning to small, moving targets even when presented in cluttered backgrounds. In prior work, these neuronal responses were quantified by computing the rate of spikes fired during an analysis window of interest. However, neuronal systems can utilize a variety of neuronal coding principles to signal information, so a spike train’s information content is not necessarily encapsulated by spike rate alone. One example of this is burst coding, where neurons fire rapid bursts of spikes, followed by a period of inactivity. Here we show that the most studied target-detecting neuron in dragonflies, CSTMD1, responds to moving targets with a series of spike bursts. This spiking activity differs from those in other identified visual neurons in the dragonfly, indicative of different physiological mechanisms underlying CSTMD1’s spike generation. Burst codes present several advantages and disadvantages compared to other coding approaches. We propose functional implications of CSTMD1’s burst coding activity and show that spike bursts enhance the robustness of target-evoked responses.

3D high-density microelectrode array with optical stimulation and drug delivery for investigating neural circuit dynamics

Investigation of neural circuit dynamics is crucial for deciphering the functional connections among regions of the brain and understanding the mechanism of brain dysfunction. Despite the advancements of neural circuit models in vitro, technologies for both precisely monitoring and modulating neural activities within three-dimensional (3D) neural circuit models have yet to be developed. Specifically, no existing 3D microelectrode arrays (MEAs) have integrated capabilities to stimulate surrounding neurons and to monitor the temporal evolution of the formation of a neural network in real time. Herein, we present a 3D high-density multifunctional MEA with optical stimulation and drug delivery for investigating neural circuit dynamics within engineered 3D neural tissues. We demonstrate precise measurements of synaptic latencies in 3D neural networks. We expect our 3D multifunctional MEA to open up opportunities for studies of neural circuits through precise, in vitro investigations of neural circuit dynamics with 3D brain models.

Variable but not random: temporal pattern coding in a songbird brain area necessary for song modification

Practice of a complex motor gesture involves motor exploration to attain a better match to target, but little is known about the neural code for such exploration. We examine spiking in a premotor area of the songbird brain critical for song modification and quantify correlations between spiking and time in the motor sequence. While isolated spikes code for time in song during performance of song to a female bird, extended strings of spiking and silence, particularly bursts, code for time in song during undirected (solo) singing, or "practice." Bursts code for particular times in song with more information than individual spikes, and this spike-spike synergy is significantly higher during undirected singing. The observed pattern information cannot be accounted for by a Poisson model with a matched time-varying rate, indicating that the precise timing of spikes in both bursts in undirected singing and isolated spikes in directed singing code for song with a temporal code. Temporal coding during practice supports the hypothesis that lateral magnocellular nucleus of the anterior nidopallium neurons actively guide song modification at local instances in time.NEW & NOTEWORTHY This paper shows that bursts of spikes in the songbird brain during practice carry information about the output motor pattern. The brain's code for song changes with social context, in performance versus practice. Synergistic combinations of spiking and silence code for time in the bird's song. This is one of the first uses of information theory to quantify neural information about a motor output. This activity may guide changes to the song.

Engrams of Fast Learning

Fast learning designates the behavioral and neuronal mechanisms underlying the acquisition of a long-term memory trace after a unique and brief experience. As such it is opposed to incremental, slower reinforcement or procedural learning requiring repetitive training. This learning process, found in most animal species, exists in a large spectrum of natural behaviors, such as one-shot associative, spatial, or perceptual learning, and is a core principle of human episodic memory. We review here the neuronal and synaptic long-term changes associated with fast learning in mammals and discuss some hypotheses related to their underlying mechanisms. We first describe the variety of behavioral paradigms used to test fast learning memories: those preferentially involve a single and brief (from few hundred milliseconds to few minutes) exposures to salient stimuli, sufficient to trigger a long-lasting memory trace and new adaptive responses. We then focus on neuronal activity patterns observed during fast learning and the emergence of long-term selective responses, before documenting the physiological correlates of fast learning. In the search for the engrams of fast learning, a growing body of evidence highlights long-term changes in gene expression, structural, intrinsic, and synaptic plasticities. Finally, we discuss the potential role of the sparse and bursting nature of neuronal activity observed during the fast learning, especially in the induction plasticity mechanisms leading to the rapid establishment of long-term synaptic modifications. We conclude with more theoretical perspectives on network dynamics that could enable fast learning, with an overview of some theoretical approaches in cognitive neuroscience and artificial intelligence.

Glutamate transmission rather than cellular pacemaking propels excitatory-inhibitory resonance for ictogenesis in amygdala

Epileptic seizures are automatic, excessive, and synchronized neuronal activities originating from many brain regions especially the amygdala, the allocortices and neocortices. This may reflect a shared principle for network organization and signaling in these telencephalic structures. In theory, the automaticity of epileptic discharges may stem from spontaneously active "oscillator" neurons equipped with intrinsic pacemaking conductances, or from a group of synaptically-connected collaborating "resonator" neurons. In the basolateral amygdalar (BLA) network of pyramidal-inhibitory (PN-IN) neuronal resonators, we demonstrated that rhythmogenic currents are provided by glutamatergic rather than the classic intrinsic or cellular pacemaking conductances (namely the h currents). The excitatory output of glutamatergic neurons such as PNs presumably propels a novel network-based "relay burst mode" of discharges especially in INs, which precondition PNs into a state prone to burst discharges and thus further glutamate release. Also, selective activation of unilateral PNs, but never INs, readily drives bilateral BLA networks into reverberating discharges which are fully synchronized with the behavioral manifestations of seizures (e.g. muscle contractions). Seizures originating in BLA and/or the other structures with similar PN-IN networks thus could be viewed as glutamate-triggered erroneous network oscillations that are normally responsible for information relay.

Delta-Frequency Augmentation and Synchronization in Seizure Discharges and Telencephalic Transmission

Epileptic seizures constitute a common neurological disease primarily diagnosed by characteristic rhythms or waves in the local field potentials (LFPs) of cerebral cortices or electroencephalograms. With a basolateral amygdala (BLA) kindling model, we found that the dominant frequency of BLA oscillations is in the delta range (1-5 Hz) in both normal and seizure conditions. Multi-unit discharges are increased with higher seizure staging but remain phase-locked to the delta waves in LFPs. Also, the change in synchrony precedes and outlasts the changes in discharging units as well as behavioral seizures. One short train of stimuli readily drives the pyramidal-inhibitory neuronal networks in BLA slices into prolonged reverberating activities, where the burst and interburst intervals may concurrently set a "natural wavelength" for delta frequencies. Seizures thus could be viewed as erroneous temporospatial continuums to normal oscillations in a system with a built-in synchronizing and resonating nature for information relay.

Interspike intervals within retinal spike bursts combinatorially encode multiple stimulus features

Neurons in various regions of the brain generate spike bursts. While the number of spikes within a burst has been shown to carry information, information coding by interspike intervals (ISIs) is less well understood. In particular, a burst with k spikes has k−1 intraburst ISIs, and these k−1 ISIs could theoretically encode k−1 independent values. In this study, we demonstrate that such combinatorial coding occurs for retinal bursts. By recording ganglion cell spikes from isolated salamander retinae, we found that intraburst ISIs encode oscillatory light sequences that are much faster than the light intensity modulation encoded by the number of spikes. When a burst has three spikes, the two intraburst ISIs combinatorially encode the amplitude and phase of the oscillatory sequence. Analysis of trial-to-trial variability suggested that intraburst ISIs are regulated by two independent mechanisms responding to orthogonal oscillatory components, one of which is common to bursts with a different number of spikes. Therefore, the retina encodes multiple stimulus features by exploiting all degrees of freedom of burst spike patterns, i.e., the spike number and multiple intraburst ISIs.

Neurons in various regions of the brain generate spike bursts. Bursts are typically composed of a few spikes generated within dozens of milliseconds, and individual bursts are separated by much longer periods of silence (~hundreds of milliseconds). Recent evidence indicates that the number of spikes in a burst, the interspike intervals (ISIs), and the overall duration of a burst, as well as the timing of burst onset, encode information. However, it remains unknown whether multiple ISIs within a single burst encode multiple input features. Here we demonstrate that such combinatorial ISI coding occurs for spike bursts in the retina. We recorded ganglion cell spikes from isolated salamander retinae stimulated with computer-generated movies. Visual response analyses indicated that multiple ISIs within a single burst combinatorially encode the phase and amplitude of oscillatory light sequences, which are different from the stimulus feature encoded by the spike number. The result demonstrates that the retina encodes multiple stimulus features by exploiting all degrees of freedom of burst spike patterns, i.e., the spike number and multiple intraburst ISIs. Because synaptic transmission in the visual system is highly sensitive to ISIs, the combinatorial ISI coding must have a major impact on visual information processing.

Single-neuron representation of learned complex sounds in the auditory cortex

The sensory responses of cortical neuronal populations following training have been extensively studied. However, the spike firing properties of individual cortical neurons following training remain unknown. Here, we have combined two-photon Ca²⁺ imaging and single-cell electrophysiology in awake behaving mice following auditory associative training. We find a sparse set (~5%) of layer 2/3 neurons in the primary auditory cortex, each of which reliably exhibits high-rate prolonged burst firing responses to the trained sound. Such bursts are largely absent in the auditory cortex of untrained mice. Strikingly, in mice trained with different multitone chords, we discover distinct subsets of neurons that exhibit bursting responses specifically to a chord but neither to any constituent tone nor to the other chord. Thus, our results demonstrate an integrated representation of learned complex sounds in a small subset of cortical neurons.

Using a combination of two-photon imaging and single-cell electrophysiology, the authors discover that associative learning induces the emergence of a unique subset of neurons in the auditory cortex, exhibiting high-rate bursting responses to the learned complex sounds but not to any of the constituents.