Impact of correlated synaptic input on output firing rate and variability in simple neuronal models

Daily light-induced transcription in visual cortex neurons drives downward firing rate homeostasis and stabilizes sensory processing

Balancing plasticity and stability in neural circuits is essential for an animal's ability to learn from its environment while preserving proper processing and perception of sensory information. However, unlike the mechanisms that drive plasticity in neural circuits, the activity-induced molecular mechanisms that convey functional stability remain poorly understood. Focusing on the visual cortex of adult mice and combining transcriptomics, electrophysiology, and in vivo calcium imaging, we find that the daily appearance of light induces, in excitatory neurons, a large gene program along with rapid and transient increases in the ratio of excitation and inhibition (E/I ratio) and neural activity. Furthermore, we find that the light-induced transcription factor NPAS4 drives these daily normalizations of the E/I ratio and neural activity rates and that it stabilizes the neurons' response properties. These findings indicate that daily sensory-induced transcription normalizes the E/I ratio and drives downward firing rate homeostasis to maintain proper sensory processing and perception.

High Mobility Group Box-1 (HMGB1), a Key Mediator of Cognitive Decline in Neurotrauma with a Potential for Targeted Therapy: A Comprehensive Review

Neurotrauma plays a significant role in secondary injuries by intensifying the neuroinflammatory response in the brain. High Mobility Group Box-1 (HMGB1) protein is a crucial neuroinflammatory mediator involved in this process. Numerous studies have hypothesized about the underlying pathophysiology of HMGB1 and its role in cognition, but a definitive link has yet to be established. Elevated levels of HMGB1 in the hippocampus and serum have been associated with declines in cognitive performance, particularly in spatial memory and learning. This review also found that inhibiting HMGB1 can improve cognitive deficits following neurotrauma. Interestingly, HMGB1 levels are linked to the modulation of neuroplasticity and may offer neuroprotective effects in the later stages of neurotraumatic events. Consequently, administering HMGB1 during the acute phase may help reduce neuroinflammatory effects that lead to cognitive deficits in the later stages of neurotrauma. However, further research is needed to understand the time-dependent regulation of HMGB1 and the clinical implications of treatments targeting HMGB1 after neurotrauma.

Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios.

Decreased cellular excitability of pyramidal tract neurons in primary motor cortex leads to paradoxically increased network activity in simulated parkinsonian motor cortex

Decreased excitability of pyramidal tract neurons in layer 5B (PT5B) of primary motor cortex (M1) has recently been shown in a dopamine-depleted mouse model of parkinsonism. We hypothesized that decreased PT5B neuron excitability would substantially disrupt oscillatory and non-oscillatory firing patterns of neurons in layer 5 (L5) of primary motor cortex (M1). To test this hypothesis, we performed computer simulations using a previously validated computer model of mouse M1. Inclusion of the experimentally identified parkinsonism-associated decrease of PT5B excitability into our computational model produced a paradoxical increase in rest-state PT5B firing rate, as well as an increase in beta-band oscillatory power in local field potential (LFP). In the movement-state, PT5B population firing and LFP showed reduced beta and increased high-beta, low-gamma activity of 20-35 Hz in the parkinsonian, but not in control condition. The appearance of beta-band oscillations in parkinsonism would be expected to disrupt normal M1 motor output and contribute to motor activity deficits seen in patients with Parkinson's disease (PD).

Theta Phase Entrainment of Single-Cell Spiking in Rat Somatosensory Barrel Cortex and Secondary Visual Cortex Is Enhanced during Multisensory Discrimination Behavior

Phase entrainment of cells by theta oscillations is thought to globally coordinate the activity of cell assemblies across different structures, such as the hippocampus and neocortex. This coordination is likely required for optimal processing of sensory input during recognition and decision-making processes. In quadruple-area ensemble recordings from male rats engaged in a multisensory discrimination task, we investigated phase entrainment of cells by theta oscillations in areas along the corticohippocampal hierarchy: somatosensory barrel cortex (S1BF), secondary visual cortex (V2L), perirhinal cortex (PER), and dorsal hippocampus (dHC). Rats discriminated between two 3D objects presented in tactile-only, visual-only, or both tactile and visual modalities. During task engagement, S1BF, V2L, PER, and dHC LFP signals showed coherent theta-band activity. We found phase entrainment of single-cell spiking activity to locally recorded as well as hippocampal theta activity in S1BF, V2L, PER, and dHC. While phase entrainment of hippocampal spikes to local theta oscillations occurred during sustained epochs of task trials and was nonselective for behavior and modality, somatosensory and visual cortical cells were only phase entrained during stimulus presentation, mainly in their preferred modality (S1BF, tactile; V2L, visual), with subsets of cells selectively phase-entrained during cross-modal stimulus presentation (S1BF: visual; V2L: tactile). This effect could not be explained by modulations of firing rate or theta amplitude. Thus, hippocampal cells are phase entrained during prolonged epochs, while sensory and perirhinal neurons are selectively entrained during sensory stimulus presentation, providing a brief time window for coordination of activity.

Intrinsic Plasticity Mechanisms of Repetitive Transcranial Magnetic Stimulation

Repetitive transcranial magnetic stimulation (rTMS) has become an increasingly popular tool to modulate neural excitability and induce neural plasticity in clinical and preclinical models; however, the physiological mechanisms in which it exerts these effects remain largely unknown. To date, studies have primarily focused on characterizing rTMS-induced changes occurring at the synapse, with little attention given to changes in intrinsic membrane properties. However, accumulating evidence suggests that rTMS may induce its effects, in part, via intrinsic plasticity mechanisms, suggesting a new and potentially complementary understanding of how rTMS alters neural excitability and neural plasticity. In this review, we provide an overview of several intrinsic plasticity mechanisms before reviewing the evidence for rTMS-induced intrinsic plasticity. In addition, we discuss a select number of neurological conditions where rTMS-induced intrinsic plasticity has therapeutic potential before speculating on the temporal relationship between rTMS-induced intrinsic and synaptic plasticity.

Measuring excitation-inhibition balance through spectral components of local field potentials

The balance between excitation and inhibition is critical to brain functioning, and dysregulation of this balance is a hallmark of numerous psychiatric conditions. Measuring this excitation-inhibition (E:I) balance in vivo has remained difficult, but theoretical models have proposed that characteristics of local field potentials (LFP) may provide an accurate proxy. To establish a conclusive link between LFP and E:I balance, we recorded single units and LFP from the prefrontal cortex (mPFC) of rats during decision making. Dynamic measures of synaptic coupling strength facilitated direct quantification of E:I balance and revealed a strong inverse relationship to broadband spectral power of LFP. These results provide a critical link between LFP and underlying network properties, opening the door for non-invasive recordings to measure E:I balance in clinical settings.

Characterization of perception by transcutaneous electrical Stimulation in terms of tingling intensity and temporal dynamics

Electrotactile feedback is a cost-effective and versatile method to provide new information or to augment intrinsic tactile feedback. As tactile feedback provides critical information for human-environment interaction, electrotactile feedback, accordingly, has many purposes to improve the quality of human-environment interaction in both direct and remote settings. However, electrotactile feedback overlays tingling sensation on top of the natural tactile feedback. To better characterize electrotactile feedback and understand the origin of the tingling sensation, a need arises to characterize the human perception of electrotactile feedback qualitatively and quantitatively, while varying the key stimulation parameters, namely amplitude and frequency. This study consists of two experiments. In the first experiment, the voltage for each subject was characterized by setting perception and discomfort thresholds. In the second experiment, subjects received electrical stimulation in 9 different combinations of voltages and frequencies. On delivering stimulation with each parameter combination, subjects reported their perception in two comparative scales-pressure vs. tingling and constant vs. pulsing. Subjects also reported the location of perception for stimulation with every parameter combination. More tingling and less pressure was reported as frequency increased, while the tingling-pressure percept was not affected by the amplitude change. Additionally, less pulsing and more constant was reported as frequency increased, while the pulsing-constant percept was not affected by the amplitude change. Concurrently, the normalized level of voltage thresholds was decreased as frequency increased. Dependency of tingling-pressure percept on stimulation frequency suggests that incongruency between the stimulation frequency and the natural firing rate of the sensory neuron would be an important factor of the tingling sensation. This study is a steppingstone to further demystify the origin of the tingling percept caused by electrical stimulation, thus broadening the use of transcutaneous electrical stimulation as a way of providing tactile cue or augmentation.

Gamma oscillations and episodic memory

Enhanced gamma oscillatory activity (30-80 Hz) accompanies the successful formation and retrieval of episodic memories. While this co-occurrence is well documented, the mechanistic contributions of gamma oscillatory activity to episodic memory remain unclear. Here, we review how gamma oscillatory activity may facilitate spike timing-dependent plasticity, neural communication, and sequence encoding/retrieval, thereby ensuring the successful formation and/or retrieval of an episodic memory. Based on the evidence reviewed, we propose that multiple, distinct forms of gamma oscillation can be found within the canonical gamma band, each of which has a complementary role in the neural processes listed above. Further exploration of these theories using causal manipulations may be key to elucidating the relevance of gamma oscillatory activity to episodic memory.

Coding of object location by heterogeneous neural populations with spatially dependent correlations in weakly electric fish

Understanding how neural populations encode sensory stimuli remains a central problem in neuroscience. Here we performed multi-unit recordings from sensory neural populations in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus in response to stimuli located at different positions along the rostro-caudal axis. Our results reveal that the spatial dependence of correlated activity along receptive fields can help mitigate the deleterious effects that these correlations would otherwise have if they were spatially independent. Moreover, using mathematical modeling, we show that experimentally observed heterogeneities in the receptive fields of neurons help optimize information transmission as to object location. Taken together, our results have important implications for understanding how sensory neurons whose receptive fields display antagonistic center-surround organization encode location. Important similarities between the electrosensory system and other sensory systems suggest that our results will be applicable elsewhere.

A role for the claustrum in cognitive control

Early hypotheses of claustrum function were fueled by neuroanatomical data and yielded suggestions that the claustrum is involved in processes ranging from salience detection to multisensory integration for perceptual binding. While these hypotheses spurred useful investigations, incompatibilities inherent in these views must be reconciled to further conceptualize claustrum function amid a wealth of new data. Here, we review the varied models of claustrum function and synthesize them with developments in the field to produce a novel functional model: network instantiation in cognitive control (NICC). This model proposes that frontal cortices direct the claustrum to flexibly instantiate cortical networks to subserve cognitive control. We present literature support for this model and provide testable predictions arising from this conceptual framework.

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.

Excitatory-Inhibitory Homeostasis and Diaschisis: Tying the Local and Global Scales in the Post-stroke Cortex

Maintaining a balance between excitatory and inhibitory activity is an essential feature of neural networks of the neocortex. In the face of perturbations in the levels of excitation to cortical neurons, synapses adjust to maintain excitatory-inhibitory (EI) balance. In this review, we summarize research on this EI homeostasis in the neocortex, using stroke as our case study, and in particular the loss of excitation to distant cortical regions after focal lesions. Widespread changes following a localized lesion, a phenomenon known as diaschisis, are not only related to excitability, but also observed with respect to functional connectivity. Here, we highlight the main findings regarding the evolution of excitability and functional cortical networks during the process of post-stroke recovery, and how both are related to functional recovery. We show that cortical reorganization at a global scale can be explained from the perspective of EI homeostasis. Indeed, recovery of functional networks is paralleled by increases in excitability across the cortex. These adaptive changes likely result from plasticity mechanisms such as synaptic scaling and are linked to EI homeostasis, providing a possible target for future therapeutic strategies in the process of rehabilitation. In addition, we address the difficulty of simultaneously studying these multiscale processes by presenting recent advances in large-scale modeling of the human cortex in the contexts of stroke and EI homeostasis, suggesting computational modeling as a powerful tool to tie the meso- and macro-scale processes of recovery in stroke patients.

Discrete spectral eigenmode-resonance network of brain dynamics and connectivity

The problem of finding a compact natural representation of brain dynamics and connectivity is addressed using an expansion in terms of physical spatial eigenmodes and their frequency resonances. It is demonstrated that this discrete expansion via the system transfer function enables linear and nonlinear dynamics to be analyzed in compact form in terms of natural dynamic "atoms," each of which is a frequency resonance of an eigenmode. Because these modal resonances are determined by the system dynamics, not the investigator, they are privileged over widely used phenomenological patterns, and obviate the need for artificial discretizations and thresholding in coordinate space. It is shown that modal resonances participate as nodes of a discrete spectral network, are noninteracting in the linear regime, but are linked nonlinearly by wave-wave coalescence and decay processes. The modal resonance formulation is shown to be capable of speeding numerical calculations of strongly nonlinear interactions. Recent work in brain dynamics, especially based on neural field theory (NFT) approaches, allows eigenmodes and their resonances to be estimated from data without assuming a specific brain model. This means that dynamic equations can be inferred using system identification methods from control theory, rather than being assumed, and resonances can be interpreted as control-systems data filters. The results link brain activity and connectivity with control-systems functions such as prediction and attention via gain control and can also be linked to specific NFT predictions if desired, thereby providing a convenient bridge between physiologically based theories and experiment. Amplitudes of modes and resonances can also be tracked to provide a more direct and temporally localized representation of the dynamics than correlations and covariances, which are widely used in the field. By synthesizing many different lines of research, this work provides a way to link quantitative electrophysiological and imaging measurements, connectivity, brain dynamics, and function. This underlines the need to move between coordinate and spectral representations as required. Moreover, standard theoretical-physics approaches and mathematical methods can be used in place of ad hoc statistical measures such as those based on graph theory of artificially discretized and decimated networks, which are highly prone to selection effects and artifacts.

Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity

Under natural conditions, the visual system often sees a given input repeatedly. This provides an opportunity to optimize processing of the repeated stimuli. Stimulus repetition has been shown to strongly modulate neuronal-gamma band synchronization, yet crucial questions remained open. Here we used magnetoencephalography in 30 human subjects and find that gamma decreases across ≈10 repetitions and then increases across further repetitions, revealing plastic changes of the activated neuronal circuits. Crucially, increases induced by one stimulus did not affect responses to other stimuli, demonstrating stimulus specificity. Changes partially persisted when the inducing stimulus was repeated after 25 minutes of intervening stimuli. They were strongest in early visual cortex and increased interareal feedforward influences. Our results suggest that early visual cortex gamma synchronization enables adaptive neuronal processing of recurring stimuli. These and previously reported changes might be due to an interaction of oscillatory dynamics with established synaptic plasticity mechanisms.

Neural field theory of neural avalanche exponents

The power-law exponents of observed size and lifetime distributions of near-critical neural avalanches are calculated from neural field theory using diagrammatic methods. This brings neural avalanches within the ambit of neural field theory, which has also previously explained near-critical 1/f spectra and many other observed features of neural activity. This strengthens the case for near-criticality of the brain and opens the way for these other phenomena to be interrelated with avalanches and their dynamics.

Statistical moments of the interspike intervals for a neuron model driven by trichotomous noise

The influence of a colored three-level input noise (trichotomous noise) on the spike generation of a perfect integrate-and-fire (PIF) model of neurons is studied. Using a first-passage-time formulation, exact expressions for the Laplace transform of the output interspike interval (ISI) density and for the statistical moments of the ISIs (such as the coefficient of variation, the skewness, the serial correlation coefficient, and the Fano factor) are derived. To model the anomalous subdiffusion that can arise from, e.g., the trapping properties of dendritic spines, the model is extended by including a random operational time in the form of an inverse strictly increasing Lévy-type subordinator, and exact formulas for ISI statistics are given for this case as well. Particularly, it is shown that at some parameter regimes, the ISI density exhibits a three-modal structure. The results for the extended model show that the ISI serial correlation coefficient and the Fano factor are nonmonotonic with respect to the input current, which indicates that at an intermediate value of the input current the variability of the output spike trains is minimal. Similarities and differences between the behavior of the presented models and the previously investigated PIF models driven by dichotomous noise are also discussed.

Role of synaptic nonlinearity in persistent firing rate shifts caused by external periodic forcing

Information storage and processing in the brain largely relies on the neural population coding principle. In this framework, information is reflected in the population firing rate that reflects asynchronous irregular spiking of its constituent neurons. Periodic modulations of neural activity can lead to neural activity oscillations. Data indicate that such oscillations are ubiquitous in brain activity and are modulated, in frequency and amplitude, in a functionally meaningful manner. The relationship between oscillations and the population rate code remains an open issue. While ample works show how changes in the mean firing rate may alter neural oscillations, the reverse connection is unclear. One notable possibility is that oscillatory activity impinging on a neural population modulates its mean firing rate, thereby impacting information processing. We suggest that such modulation requires nonlinearities and propose nonlinear excitatory coupling via slow N-methyl-D-aspartate (NMDA) receptors as the prevalent mechanism. The aim of our paper is to theoretically explore to what extent the NMDA-related mechanism could account for oscillation-induced mean firing rate changes. We consider a mean-field model of a neural circuit containing an excitatory and an inhibitory population with linear transfer functions. Along with NMDA excitation, the model included fast recurrent excitatory and inhibitory connectivity. To explicitly study the effects of impinging oscillation on the rate dynamics, we subjected the circuit to a sinusoidal input signal imitating an input from distant brain regions or from a larger network into which the circuit is embedded. Using time-scale separation and time-averaging techniques, we developed a geometric method to determine the oscillation-induced mean firing rate shifts and validated it by numeric simulations of the model. Our results indicate that a large-amplitude stable firing rate shift requires nonlinear NMDA synapses on both the excitatory and the inhibitory populations. Our results delineate specific neural synaptic properties that enable neural oscillations to act as flexible modulators of the population rate code.

Relationships between lognormal distributions of neural properties, activity, criticality, and connectivity

Relationships between convergence of inputs onto neurons, divergence of outputs from them, synaptic strengths, nonlinear firing response properties, and randomness of axonal ranges are systematically explored by interrelating means and variances of synaptic strengths, firing rates, and soma voltages. When self-consistency is imposed, it is found that broad distributions of synaptic strength are a necessary concomitant of the known massive convergence of inputs to individual neurons, and observed widths of lognormal distributions of synaptic strength and firing rate are explained provided the brain is in a near-critical state, consistent with independent observations. The strongest individual synapses are shown to have an effect on soma voltage comparable to the effect of all others combined, which supports suggestions that they may have a key role in neural communication. Remarkably, inclusion of moderate randomness in characteristic axonal ranges is shown to account for the observed [Formula: see text]-fold variability in two-point connectivity at a given separation and [Formula: see text]-fold overall when the known mean exponential fall-off is included, consistent with observed near-lognormal distributions. Inferred axonal deviations from straight-line paths are also consistent with independent estimates.

Post-error recruitment of frontal sensory cortical projections promotes attention in mice

The frontal cortex, especially the anterior cingulate cortex area (ACA), is essential for exerting cognitive control after errors, but the mechanisms that enable modulation of attention to improve performance after errors are poorly understood. Here we demonstrate that during a mouse visual attention task, ACA neurons projecting to the visual cortex (VIS; ACA_VIS neurons) are recruited selectively by recent errors. Optogenetic manipulations of this pathway collectively support the model that rhythmic modulation of ACA_VIS neurons in anticipation of visual stimuli is crucial for adjusting performance following errors. 30-Hz optogenetic stimulation of ACA_VIS neurons in anesthetized mice recapitulates the increased gamma and reduced theta VIS oscillatory changes that are associated with endogenous post-error performance during behavior and subsequently increased visually evoked spiking, a hallmark feature of visual attention. This frontal sensory neural circuit links error monitoring with implementing adjustments of attention to guide behavioral adaptation, pointing to a circuit-based mechanism for promoting cognitive control.

Estimating Fisher discriminant error in a linear integrator model of neural population activity

Decoding approaches provide a useful means of estimating the information contained in neuronal circuits. In this work, we analyze the expected classification error of a decoder based on Fisher linear discriminant analysis. We provide expressions that relate decoding error to the specific parameters of a population model that performs linear integration of sensory input. Results show conditions that lead to beneficial and detrimental effects of noise correlation on decoding. Further, the proposed framework sheds light on the contribution of neuronal noise, highlighting cases where, counter-intuitively, increased noise may lead to improved decoding performance. Finally, we examined the impact of dynamical parameters, including neuronal leak and integration time constant, on decoding. Overall, this work presents a fruitful approach to the study of decoding using a comprehensive theoretical framework that merges dynamical parameters with estimates of readout error.

Neuronal oscillations and the mouse prefrontal cortex

The mouse prefrontal cortex (PFC) encompasses a collection of agranual brain regions in the rostral neocortex and is considered to be critically involved in the neuronal computations underlying intentional behaviors. Flexible behavioral responses demand coordinated integration of sensory inputs with state, goal and memory information in brain-wide neuronal networks. Neuronal oscillations are proposed to provide a temporal scaffold for coordination of neuronal network activity and routing of information. In the present book chapter, we review findings on the role neuronal oscillations in prefrontal functioning, with a specific focus on research in mice. We discuss discoveries pertaining to local prefrontal processing, as well to interactions with other brain regions. We also discuss how the recent discovery of brain-wide respiration-entrained rhythms (RR) warrant re-evaluation of certain findings on slow oscillations (<10Hz) in prefrontal functioning.

Alterations of Neuronal Dynamics as a Mechanism for Cognitive Impairment in Epilepsy

Epilepsy is commonly associated with cognitive and behavioral deficits that dramatically affect the quality of life of patients. In order to identify novel therapeutic strategies aimed at reducing these deficits, it is critical first to understand the mechanisms leading to cognitive impairments in epilepsy. Traditionally, seizures and epileptiform activity in addition to neuronal injury have been considered to be the most significant contributors to cognitive dysfunction. In this review we however highlight the role of a new mechanism: alterations of neuronal dynamics, i.e. the timing at which neurons and networks receive and process neural information. These alterations, caused by the underlying etiologies of epilepsy syndromes, are observed in both animal models and patients in the form of abnormal oscillation patterns in unit firing, local field potentials, and electroencephalogram (EEG). Evidence suggests that such mechanisms significantly contribute to cognitive impairment in epilepsy, independently of seizures and interictal epileptiform activity. Therefore, therapeutic strategies directly targeting neuronal dynamics rather than seizure reduction may significantly benefit the quality of life of patients.

Effects of transient subordinators on the firing statistics of a neuron model driven by dichotomous noise

The behavior of a stochastic perfect integrate-and-fire (PIF) model of neurons is considered. The effect of temporally correlated random activity of synaptic inputs is modeled as a combination of an asymmetric dichotomous noise and a random operation time in the form of an inverse strictly increasing Lévy-type subordinator. Using a first-passage-time formulation, we find exact expressions for the output interspike interval (ISI) statistics. Particularly, it is shown that at some parameter regimes the ISI density exhibits a multimodal structure. Moreover, it is demonstrated that the coefficient of variation, the serial correlation coefficient, and the Fano factor display a nonmonotonic dependence on the mean input current μ, i.e., the ISI's regularity is maximized at an intermediate value of μ. The features of spike statistics, analytically revealed in our study, are compared with previously obtained results for a perfect integrate-and-fire neuron model driven by dichotomous noise (without subordination).

Reorganization of theta phase-locking in the orbitofrontal cortex drives cocaine choice under the influence

Cortical theta oscillations of neuronal activity are a fundamental mechanism driving goal-directed behavior. We previously identified in the rat orbitofrontal cortex (OFC) a neuronal correlate of individual preferences between cocaine use and an alternative nondrug reward (i.e. saccharin). Whether theta oscillations are also associated with choice behavior between a drug and a nondrug reward remains unknown. Here we investigated the temporal structure between single unit activity and theta band oscillations (4-12 Hz) in the OFC of rats choosing between cocaine and saccharin. First, we found that the relative amplitude of theta oscillations is associated with subjective value and preference between two rewards. Second, OFC phase-locked neurons fired on opposite phase of the theta oscillation during saccharin and cocaine rewards, suggesting the existence of two separable neuronal assemblies. Finally, the pharmacological influence of cocaine at the moment of choice altered both theta band power and theta phase-locking in the OFC. That is, this drug influence shifted spike-phase relative to theta cycle and decreased the synchronization of OFC neurons relative to the theta oscillation. Overall, this study indicates that the reorganization of theta phase-locking under the influence of cocaine biases OFC neuronal assemblies in favor of cocaine choice and at the expense of a normally preferred alternative, a neuronal change that may contribute to drug preference in cocaine addiction.

Distinct roles of parvalbumin and somatostatin interneurons in gating the synchronization of spike times in the neocortex

Synchronization of precise spike times across multiple neurons carries information about sensory stimuli. Inhibitory interneurons are suggested to promote this synchronization, but it is unclear whether distinct interneuron subtypes provide different contributions. To test this, we examined single-unit recordings from barrel cortex in vivo and used optogenetics to determine the contribution of parvalbumin (PV)- and somatostatin (SST)-positive interneurons to the synchronization of spike times across cortical layers. We found that PV interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are low (<12 Hz), whereas SST interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contributions to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in the spatiotemporal synchronization of precise spike times.

Alarmin HMGB1 Plays a Detrimental Role in Hippocampal Dysfunction Caused by Hypoxia-Ischemia Insult in Neonatal Mice: Evidence from the Application of the HMGB1 Inhibitor Glycyrrhizin

Hippocampal dysfunction related to cognitive impairment and emotional disorders in young children and adolescents caused by neonatal hypoxic-ischemic brain injury (HIBI) has attracted increasing attention in recent years. Crosstalk between the nervous and immune systems organized by hypoxia-ischemia (HI) insult may contribute to hippocampal dysfunction after HIBI. Extracellular HMGB1 functions as a damage-associated molecular pattern to instigate and amplify inflammatory responses, but whether this molecule is correlated with hippocampal dysfunction after HIBI is largely unknown. Therefore, this study examined hippocampal function after HMGB1 inhibition in an experimental HIBI model to verify the hypothesis that HMGB1 is a key mediator of hippocampal neuropathology in neonatal HIBI. By administering different doses of the HMGB1-specific inhibitor glycyrrhizin (GLY), we first found that GLY reversed the HI insult-induced loss of neurons and myelin in the hippocampal region and neurobehavioral impairments, partially in a dose-dependent manner, and based on this, we determined the optimal drug concentration to be 50 mg/kg. Subsequent analysis found that this neuroprotective effect was achieved through the inhibition of HMGB1 expression and nucleocytoplasmic translocation, a reduction in the abnormal expression of proteins associated with the downstream signaling pathway of HMGB1, a decrease in the inflammatory response, the suppression of increases in microglia/astrocytes, and the inhibition of hippocampal cell apoptosis. Collectively, our discoveries contribute to the rising appreciation of the role of HMGB1 in the neuropathology of hippocampal dysfunction and related behavioral outcomes following HIBI.

Mechanisms underlying gain modulation in the cortex

Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.

Dynamics and sources of response variability and its coordination in visual cortex

The trial-to-trial response variability in sensory cortices and the extent to which this variability can be coordinated among cortical units have strong implications for cortical signal processing. Yet, little is known about the relative contributions and dynamics of defined sources to the cortical response variability and their correlations across cortical units. To fill this knowledge gap, here we obtained and analyzed multisite local field potential (LFP) recordings from visual cortex of turtles in response to repeated naturalistic movie clips and decomposed cortical across-trial LFP response variability into three defined sources, namely, input, network, and local fluctuations. We found that input fluctuations dominate cortical response variability immediately following stimulus onset, whereas network fluctuations dominate the response variability in the steady state during continued visual stimulation. Concurrently, we found that the network fluctuations dominate the correlations of the variability during the ongoing and steady-state epochs, but not immediately following stimulus onset. Furthermore, simulations of various model networks indicated that (i) synaptic time constants, leading to oscillatory activity, and (ii) synaptic clustering and synaptic depression, leading to spatially constrained pockets of coherent activity, are both essential features of cortical circuits to mediate the observed relative contributions and dynamics of input, network, and local fluctuations to the cortical LFP response variability and their correlations across recording sites. In conclusion, these results show how a mélange of multiscale thalamocortical circuit features mediate a complex stimulus-modulated cortical activity that, when naively related to the visual stimulus alone, appears disguised as high and coordinated across-trial response variability.

A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1

Cortical computation depends on interactions between excitatory and inhibitory neurons. The contributions of distinct neuron types to sensory processing and network synchronization in primate visual cortex remain largely undetermined. We show that in awake monkey V1, there exists a distinct cell type (››30% of neurons) that has narrow-waveform (NW) action potentials and high spontaneous discharge rates and fires in high-frequency bursts. These neurons are more stimulus selective and phase locked to 30- to 80-Hz gamma oscillations than other neuron types. Unlike other neuron types, their gamma-phase locking is highly predictive of orientation tuning. We find evidence for strong rhythmic inhibition in these neurons, suggesting that they interact with interneurons to act as excitatory pacemakers for the V1 gamma rhythm. We did not find a similar class of NW bursting neurons in L2-L4 of mouse V1. Given its properties, this class of NW bursting neurons should be pivotal for the encoding and transmission of stimulus information.

Exogenous neuromodulation of spinal neurons induces beta-band coherence during self-sustained discharge of hind limb motor unit populations

The spontaneous or self-sustained discharge of spinal motoneurons can be observed in both animals and humans. Although the origins of this self-sustained discharge are not fully known, it can be generated by activation of persistent inward currents intrinsic to the motoneuron. If self-sustained discharge is generated exclusively through this intrinsic mechanism, the discharge of individual motor units will be relatively independent of one another. Alternatively, if increased activation of premotor circuits underlies this prolonged discharge of spinal motoneurons, we would expect correlated activity among motoneurons. Our aim is to assess potential synaptic drive by quantifying coherence during self-sustained discharge of spinal motoneurons. Electromyographic activity was collected from 20 decerebrate animals using a 64-channel electrode grid placed on the isolated soleus muscle before and following intrathecal administration of methoxamine, a selective α₁-noradrenergic agonist. Sustained muscle activity was recorded and decomposed into the discharge times of ~10-30 concurrently active individual motor units. Consistent with previous reports, the self-sustained discharge of motor units occurred at low mean discharge rates with low-interspike variability. Before methoxamine administration, significant low-frequency coherence (<2 Hz) was observed, while minimal coherence was observed within higher frequency bands. Following intrathecal administration of methoxamine, increases in motor unit discharge rates and strong coherence in both the low-frequency and 15- to 30-Hz beta bands were observed. These data demonstrate beta-band coherence among motor units can be observed through noncortical mechanisms and that neuromodulation of spinal/brainstem neurons greatly influences coherent discharge within spinal motor pools.NEW & NOTEWORTHY The correlated discharge of spinal motoneurons is often used to describe the input to the motor pool. We demonstrate spinal/brainstem neurons devoid of cortical input can generate correlated motor unit discharge in the 15- to 30-Hz beta band, which is amplified through neuromodulation. Activity in the beta band is often ascribed to cortical drive in humans; however, these data demonstrate the capability of the mammalian segmental motor system to generate and modulate this coherent state of motor unit discharge.

Mouse corticospinal system comprises different functional neuronal ensembles depending on their hodology

BACKGROUND

Movement performance depends on the synaptic interactions generated by coherent parallel sensorimotor cortical outputs to different downstream targets. The major outputs of the neocortex to subcortical structures are driven by pyramidal tract neurons (PTNs) located in layer 5B. One of the main targets of PTNs is the spinal cord through the corticospinal (CS) system, which is formed by a complex collection of distinct CS circuits. However, little is known about intracortical synaptic interactions that originate CS commands and how different populations of CS neurons are functionally organized. To further understand the functional organization of the CS system, we analyzed the activity of unambiguously identified CS neurons projecting to different zones of the same spinal cord segment using two-photon calcium imaging and retrograde neuronal tracers.

RESULTS

Sensorimotor cortex slices obtained from transgenic mice expressing GCaMP6 funder the Thy1 promoter were used to analyze the spontaneous calcium transients in layer 5 pyramidal neurons. Distinct subgroups of CS neurons projecting to dorsal horn and ventral areas of the same segment show more synchronous activity between them than with other subgroups.

CONCLUSIONS

The results indicate that CS neurons projecting to different spinal cord zones segregated into functional ensembles depending on their hodology, suggesting that a modular organization of CS outputs controls sensorimotor behaviors in a coordinated manner.

Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases

The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.

Short-term dynamics of input and output of CA1 network greatly differ between the dorsal and ventral rat hippocampus

Background

The functional heterogeneity of the hippocampus along its longitudinal axis at the level of behavior is an established concept; however, the neurobiological mechanisms are still unknown. Diversifications in the functioning of intrinsic hippocampal circuitry including short-term dynamics of synaptic inputs and neuronal output, that are important determinants of information processing in the brain, may profoundly contribute to functional specializations along the hippocampus. The objectives of the present study were the examination of the role of the GABA_A receptor-mediated inhibition, the μ-opioid receptors and the effect of stimulation intensity on the dynamics of both synaptic input and neuronal output of CA1 region in the dorsal and ventral hippocampus. We used recordings of field potentials from adult rat hippocampal slices evoked by brief repetitive activation of Schaffer collaterals.

Results

We find that the local CA1 circuit of the dorsal hippocampus presents a remarkably increased dynamic range of frequency-dependent short-term changes in both input and output, ranging from strong facilitation to intense depression at low and high stimulation frequencies respectively. Furthermore, the input–output relationship in the dorsal CA1 circuit is profoundly influenced by frequency and time of presynaptic activation. Strikingly, the ventral hippocampus responds mostly with depression, displaying a rather monotonous input–output relationship over frequency and time. Partial blockade of GABA_A receptor-mediated transmission (by 5 μM picrotoxin) profoundly influences input and output dynamics in the dorsal hippocampus but affected only the neuronal output in the ventral hippocampus. M-opioid receptors control short-term dynamics of input and output in the dorsal hippocampus but they play no role in the ventral hippocampus.

Conclusion

The results demonstrate that information processing by CA1 local network is highly diversified between the dorsal and ventral hippocampus. Transient detection of incoming patterns of activity and frequency-dependent sustained signaling of amplified neuronal information may be assigned to the ventral and dorsal hippocampal circuitry respectively. This disparity should have profound implications for the functional roles ascribed to distinct segments along the long axis of the hippocampus.

Electronic supplementary material

The online version of this article (10.1186/s12868-019-0517-5) contains supplementary material, which is available to authorized users.

High-order coordination of cortical spiking activity modulates perceptual accuracy

The accurate relay of electrical signals within cortical networks is key to perception and cognitive function. Theoretically, it has long been proposed that temporal coordination of neuronal spiking activity controls signal transmission and behavior. However, whether and how temporally precise neuronal coordination in population activity influences perception are unknown. Here, we recorded populations of neurons in early and mid-level visual cortex (areas V1 and V4) simultaneously to discover that the precise temporal coordination between the spiking activity of three or more cells carries information about visual perception in the absence of firing rate modulation. The accuracy of perceptual responses correlated with high-order spiking coordination within V4, but not V1, and with feedforward coordination between V1 and V4. These results indicate that while visual stimuli are encoded in the discharge rates of neurons, perceptual accuracy is related to temporally precise spiking coordination within and between cortical networks.

Myelin Plasticity and Nervous System Function

Structural plasticity in the myelinated infrastructure of the nervous system has come to light. Although an innate program of myelin development proceeds independent of nervous system activity, a second mode of myelination exists in which activity-dependent, plastic changes in myelin-forming cells influence myelin structure and neurological function. These complementary and possibly temporally overlapping activity-independent and activity-dependent modes of myelination crystallize in a model of experience-modulated myelin development and plasticity with broad implications for neurological function. In this article, I consider the contributions of myelin to neural circuit function, the dynamic influences of experience on myelin microstructure, and the role that plasticity of myelin may play in cognition.

Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

The rate and temporal pattern of neural spiking each have the potential to influence computation. In the cerebellum, it has been hypothesized that the irregularity of interspike intervals in Purkinje cells affects their ability to transmit information to downstream neurons. Accordingly, during oculomotor behavior in mice and rhesus monkeys, mean irregularity of Purkinje cell spiking varied with mean eye velocity. However, moment-to-moment variations revealed a tight correlation between eye velocity and spike rate, with no additional information conveyed by spike irregularity. Moreover, when spike rate and irregularity were independently controlled using optogenetic stimulation, the eye movements elicited were well-described by a linear population rate code with 3-5 ms temporal precision. Biophysical and random-walk models identified biologically realistic parameter ranges that determine whether spike irregularity influences responses downstream. The results demonstrate cerebellar control of movements through a remarkably rapid rate code, with no evidence for an additional contribution of spike irregularity.

Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations

The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce their firing rates when their receptive field input can be predicted by spatial context. Gamma-synchronized (30–80 Hz) firing may provide a complementary signal to rates, reflecting stronger synchronization between neuronal populations receiving mutually predictable inputs. We show that large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma synchronization in macaque V1, particularly when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma synchronization while increasing firing rates. Differences between responses to different colors, including strong gamma-responses to red, arose from stimulus adaptation to a full-screen background, suggesting prominent differences in adaptation between M- and L-cone signaling pathways. Thus, synchrony signaled whether RF inputs were predicted from spatial context, while firing rates increased when stimuli were unpredicted from context.

Gamma Synchronization between V1 and V4 Improves Behavioral Performance

Behavior is often driven by visual stimuli, relying on feedforward communication from lower to higher visual areas. Effective communication depends on enhanced interareal coherence, but it remains unclear whether this coherence occurs at an optimal phase relation that actually improves stimulus transmission to behavioral report. We recorded local field potentials from V1 and V4 of macaques performing an attention task during which they reported changes in the attended stimulus. V1-V4 gamma synchronization immediately preceding the stimulus change partly predicted subsequent reaction times (RTs). RTs slowed systematically as trial-by-trial interareal gamma phase relations deviated from the phase relation at which V1 and V4 synchronized on average. V1-V4 gamma phase relations accounted for RT differences of 13-31 ms. Effects were specific to the attended stimulus and not explained by local power or phase. Thus, interareal gamma synchronization occurs at the optimal phase relation for transmission of sensory inputs to motor responses.

Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings

Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies. Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems. In this work, we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity from large-scale neuronal networks. The method is validated by means of realistic simulations of large-scale neuronal populations. New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale (i.e., 4096 electrodes) microtransducer arrays coupled to in vitro neural populations are presented. Specifically, we show that: (i) functional inhibitory connections are accurately identified in in vitro cortical networks, providing that a reasonable firing rate and recording length are achieved; (ii) small-world topology, with scale-free and rich-club features are reliably obtained, on condition that a minimum number of active recording sites are available. The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings.

The balance between excitation and inhibition is fundamental for proper brain functions and for this reason is precisely regulated in adult cortices. Impaired excitation/inhibition balance is often associated with several neurological disorders, such as epilepsy, autism and schizophrenia. However, estimating functional inhibitory connections is not an easy task and few methods are available to identify such connections from electrophysiological data. Here we present a cross-correlation based method to identify both excitatory and inhibitory functional connections in large-scale neuronal networks. The method is applicable to both in vitro and in vivo spike data recordings. Once a connectivity map (i.e. a graph) is obtained, we characterized the associated topology by means of classical graph theory metrics to unveil functional architecture. In this work, we analyze in vitro cortical networks probed by means of large-scale microelectrode arrays (i.e., 4096 sensors) and we derive network topologies from spike data. The functional organization found is called “small-world and scale-free” and is the same organization found in cortical in vivo brain regions by means of different experimental methods. We also show that to obtain reliable information about network architecture at least a network with a hundred of nodes-neurons is needed.

Optimal Readout of Correlated Neural Activity in a Decision-Making Circuit

The neural correlates of decision making have been extensively studied with tasks involving a choice between two alternatives that is guided by visual cues. While a large body of work argues for a role of the lateral intraparietal (LIP) region of cortex in these tasks, this role may be confounded by the interaction between LIP and other regions, including medial temporal (MT) cortex. Here, we describe a simplified linear model of decision making that is adapted to two tasks: a motion discrimination and a categorization task. We show that the distinct contribution of MT and LIP may indeed be confounded in these tasks. In particular, we argue that the motion discrimination task relies on a straightforward visuomotor mapping, which leads to redundant information between MT and LIP. The categorization task requires a more complex mapping between visual information and decision behavior, and therefore does not lead to redundancy between MT and LIP. Going further, the model predicts that noise correlations within LIP should be greater in the categorization compared to the motion discrimination task due to the presence of shared inputs from MT. The impact of these correlations on task performance is examined by analytically deriving error estimates of an optimal linear readout for shared and unique inputs. Taken together, results clarify the contribution of MT and LIP to decision making and help characterize the role of noise correlations in these regions.

Circuit-specific and neuronal subcellular-wide E-I balance in cortical pyramidal cells

We used ChR2-assisted circuit mapping (CRACM) to examine neuronal/compartmental excitatory and inhibitory synaptic balance (E-I balance) in pyramidal cells (PCs) located in several brain regions (including both neocortices and paleocortices). Within the vS1, different inputs on the same neurons, or the same inputs formed on different targets, induced different E/I ratios. E/I ratios in PCs from different regions were largely different. Chemogenetic silencing of somatostatin (SOM)- or parvalbumin (PV)-containing interneurons (INs) while optogenetically activating long-range M1 inputs demonstrated differential contribution of PV and SOM INs to the E/I ratios in a layer-specific manner in S1. Our results thus demonstrate that there are both universal subcellular-wide E-I balance within single PC and high specificity in the value of E/I ratios across different circuits (i.e. visual, somatosensory, piriform and hippocampal). Specificity of E/I balance are likely caused by unique glutamatergic innervation of interneurons. The dichotomy of high specificity and generalization of subcellular E-I balance in different circuits forms the basis for further understanding of neuronal computation under physiological conditions and various neuro-psychiatric disease-states.

The balanced and introspective brain

Transfers of large-scale neural activity into, within and between corticothalamic neural populations and brain hemispheres are analysed using time-integrated transfer functions and state parameters obtained from neural field theory for a variety of arousal states. It is shown that the great majority of activity results from feedbacks within the corticothalamic system, including significant transfer between hemispheres, but only a small minority arises via net input from the external world, with the brain thus in a near-critical, highly introspective state. Notably, the total excitatory and inhibitory influences on cortical neurons are balanced to within a few per cent across arousal states. Strong negative intrahemispheric feedforward exists to the cortex, and even larger interhemispheric positive feedforward, but these are modified by feedback loops to yield near-critical positive overall gain. The results underline the utility of transfer functions for the analysis of brain activity.

Abnormal hippocampal theta and gamma hypersynchrony produces network and spike timing disturbances in the Fmr1-KO mouse model of Fragile X syndrome

Neuronal networks can synchronize their activity through excitatory and inhibitory connections, which is conducive to synaptic plasticity. This synchronization is reflected in rhythmic fluctuations of the extracellular field. In the hippocampus, theta and gamma band LFP oscillations are a hallmark of the processing of spatial information and memory. Fragile X syndrome (FXS) is an intellectual disability and the most common genetic cause of autism spectrum disorder (Belmonte and Bourgeron, 2006). Here, we investigated how neuronal network synchronization in the mouse hippocampus is compromised by the Fmr1 mutation that causes FXS (Santos et al., 2014), relating recently observed single-cell level impairments (Arbab et al., 2017) to neuronal network aberrations. We implanted tetrodes in hippocampus of freely moving Fmr1-KO and littermate wildtype (WT) mice (Mientjes et al., 2006), to record spike trains from multiple, isolated neurons as well as LFPs in a spatial exploration paradigm. Compared to wild type mice, Fmr1-KO mice displayed greater power of hippocampal theta oscillations, and higher coherence in the slow gamma band. Additionally, spike trains of Fmr1-KO interneurons show decreased spike-count correlations and they are hypersynchronized with theta and slow gamma oscillations. The hypersynchronization of Fmr1-KO oscillations and spike timing reflects functional deficits in local networks. This network hypersynchronization pathologically decreases the heterogeneity of spike-LFP phase coupling, compromising information processing within the hippocampal circuit. These findings may reflect a pathophysiological mechanism explaining cognitive impairments in FXS and autism, in which there is anomalous processing of social and environmental cues and associated deficits in memory and cognition.

Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model

The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent α_{c}≈0.402, which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

Verbal working memory and functional large-scale networks in schizophrenia

The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia.

Cortical high gamma network oscillations and connectivity: a translational index for antipsychotics to normalize aberrant neurophysiological activity

Oscillatory activity in the gamma frequency range is a critical mechanism, which integrates neural networks within and across brain structures during cognitive processes. In schizophrenia, abnormalities in high gamma oscillations are ubiquitous and most likely reflect dysfunction in neuronal networks. In conscious rats, disturbed network oscillations associated with positive symptoms and cognitive deficits were modeled in different cortical areas by the dopaminergic agonist (amphetamine) and the N-methyl-D-aspartate (NMDA) receptor antagonists (PCP and MK801). Subsequently, the efficacies of marketed atypical antipsychotics (olanzapine, risperidone, and clozapine) to normalize dysfunctional oscillations and network connectivity were examined. Acute NMDA antagonists elicited aberrant synchrony in the gamma frequency oscillations. In addition, coherent slow alpha network activity was observed with MK801 and amphetamine, both of whose oscillatory rhythms were correlated with pronounced locomotor activity. All antipsychotics commonly decreased slow alpha and high gamma network oscillations in different cortical regions as well as motion behavior. In the combined treatments, antipsychotics attenuated NMDA antagonist-induced abnormalities in functional network oscillations and connectivity, whose effects on motor behavior is mechanistically related. These results suggest that pharmacologically induced disruption of cortical gamma oscillations and network connectivity in rats is a candidate model to study dysfunctional oscillatory patterns described in positive and negative symptoms of schizophrenia. The efficacy of antipsychotics to rescue cortical network oscillatory patterns is in line with the idea that glutamatergic and dopaminergic systems play a role in maintaining the integrity of cortical circuits. Thus, gamma oscillations could provide a powerful translational index to assess the integrity of neural networks and to evaluate the efficacy of drugs with potential antipsychotic properties.

Note on the coefficient of variations of neuronal spike trains

It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.

Linking structure and activity in nonlinear spiking networks

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks’ spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities—including those of different cell types—combine with connectivity to shape population activity and function.

Neuronal networks, like many biological systems, exhibit variable activity. This activity is shaped by both the underlying biology of the component neurons and the structure of their interactions. How can we combine knowledge of these two things—that is, models of individual neurons and of their interactions—to predict the statistics of single- and multi-neuron activity? Current approaches rely on linearizing neural activity around a stationary state. In the face of neural nonlinearities, however, these linear methods can fail to predict spiking statistics and even fail to correctly predict whether activity is stable or pathological. Here, we show how to calculate any spike train cumulant in a broad class of models, while systematically accounting for nonlinear effects. We then study a fundamental effect of nonlinear input-rate transfer–coupling between different orders of spiking statistic–and how this depends on single-neuron and network properties.