Cortical oscillations arise from contextual interactions that regulate sparse coding

Rapid rebalancing of co-tuned ensemble activity in the auditory cortex

Sensory information is represented by small varying neuronal ensembles in sensory cortices. In the auditory cortex (AC) repeated presentations of the same sound activate differing ensembles indicating high trial-by trial variability in activity even though the sounds activate the same percept. Efficient processing of complex acoustic signals requires that these sparsely distributed neuronal ensembles actively interact in order to provide a constant percept. Thus, the differing ensembles might interact to process the incoming sound inputs. Here, we probe interactions within and across ensembles by combining in vivo 2-photon Ca2+ imaging and holographic optogenetic stimulation to study how increased activity of single cells level affects the cortical network. We stimulated a small number of neurons sharing the same frequency preference alongside the presentation of a target pure tone, further increasing their tone-evoked activity. We found that other non-stimulated co-tuned neurons decreased their tone-evoked activity when the frequency of the presented pure tone matched to their tuning property, while non co-tuned neurons were unaffected. Activity decrease was greater for non-stimulated co-tuned neurons with higher frequency selectivity. Co-tuned and non co-tuned neurons were spatially intermingled. Our results shows that co-tuned ensembles communicated and balanced their total activity across the larger network. The rebalanced network activity due to external stimulation remained constant. These effects suggest that co-tuned ensembles in AC interact and rapidly rebalance their activity to maintain encoding homeostasis, and that the rebalanced network is persistent.

Sparse representation of neurons for encoding complex sounds in the auditory cortex

Listening in complex sound environments requires rapid segregation of different sound sources, e.g., having a conversation with multiple speakers or other environmental sounds. Efficient processing requires fast encoding of inputs to adapt to target sounds and identify relevant information from past experiences. This adaptation process represents an early phase of implicit learning of the sound statistics to form auditory memory. The auditory cortex (ACtx) plays a crucial role in this implicit learning process, but the underlying circuits are unknown. In awake mice, we recorded neuronal responses in different ACtx subfields using in vivo 2-photon imaging of excitatory and inhibitory (parvalbumin; PV) neurons. We used a paradigm adapted from human studies that induced rapid implicit learning from passively presented complex sounds and imaged A1 Layer 4 (L4), A1 L2/3, and A2 L2/3. In this paradigm, a frozen spectro-temporally complex 'Target' sound randomly re-occurred within a stream of other random complex sounds. All ACtx subregions contained distinct groups of cells specifically responsive to complex acoustic sequences, indicating that even thalamocortical input layers (A1 L4) respond to complex sounds. Subgroups of excitatory and inhibitory cells in all subfields showed decreased responses for re-occurring Target sounds, indicating that ACtx is highly involved in the early implicit learning phase. At the population level, activity was more decorrelated to Target sounds independent of the duration of frozen token, subregions, and cell type. These findings suggest that ACtx and its input layers contribute to the early phase of auditory memory for complex sounds, suggesting a parallel strategy across ACtx areas and between excitatory and inhibitory neurons.

Neocortical inhibitory imbalance predicts successful sensory detection

Perceptual success depends on fast-spiking, parvalbumin-positive interneurons (FS/PVs). However, competing theories of optimal rate and correlation in pyramidal (PYR) firing make opposing predictions regarding the underlying FS/PV dynamics. We addressed this with population calcium imaging of FS/PVs and putative PYR neurons during threshold detection. In primary somatosensory and visual neocortex, a distinct PYR subset shows increased rate and spike-count correlations on detected trials ("hits"), while most show no rate change and decreased correlations. A larger fraction of FS/PVs predicts hits with either rate increases or decreases. Using computational modeling, we found that inhibitory imbalance, created by excitatory "feedback" and interactions between FS/PV pools, can account for the data. Rate-decreasing FS/PVs increase rate and correlation in a PYR subset, while rate-increasing FS/PVs reduce correlations and offset enhanced excitation in PYR neurons. These findings indicate that selection of informative PYR ensembles, through transient inhibitory imbalance, is a common motif of optimal neocortical processing.

Brain-state mediated modulation of inter-laminar dependencies in visual cortex

Spatial attention is critical for recognizing behaviorally relevant objects in a cluttered environment. How the deployment of spatial attention aids the hierarchical computations of object recognition remains unclear. We investigated this in the laminar cortical network of visual area V4, an area strongly modulated by attention. We found that deployment of attention strengthened unique dependencies in neural activity across cortical layers. On the other hand, shared dependencies were reduced within the excitatory population of a layer. Surprisingly, attention strengthened unique dependencies within a laminar population. Crucially, these modulation patterns were also observed during successful behavioral outcomes that are thought to be mediated by internal brain state fluctuations. Successful behavioral outcomes were also associated with phases of reduced neural excitability, suggesting a mechanism for enhanced information transfer during optimal states. Our results suggest common computation goals of optimal sensory states that are attained by either task demands or internal fluctuations.

Mean-field analysis of synaptic alterations underlying deficient cortical gamma oscillations in schizophrenia

Deficient gamma oscillations in the prefrontal cortex (PFC) of individuals with schizophrenia (SZ) are proposed to arise from alterations in the excitatory drive to fast-spiking interneurons ( E → I ) and in the inhibitory drive from these interneurons to excitatory neurons ( I → E ) . Consistent with this idea, prior postmortem studies showed lower levels of molecular and structural markers for the strength of E → I and I → E synapses and also greater variability in E → I synaptic strength in PFC of SZ. Moreover, simulating these alterations in a network of quadratic integrate-and-fire (QIF) neurons revealed a synergistic effect of their interactions on reducing gamma power. In this study, we aimed to investigate the dynamical nature of this synergistic interaction at macroscopic level by deriving a mean-field description of the QIF model network that consists of all-to-all connected excitatory neurons and fast-spiking interneurons. Through a series of numerical simulations and bifurcation analyses, findings from our mean-field model showed that the macroscopic dynamics of gamma oscillations are synergistically disrupted by the interactions among lower strength of E → I and I → E synapses and greater variability in E → I synaptic strength. Furthermore, the two-dimensional bifurcation analyses showed that this synergistic interaction is primarily driven by the shift in Hopf bifurcation due to lower E → I synaptic strength. Together, these simulations predict the nature of dynamical mechanisms by which multiple synaptic alterations interact to robustly reduce PFC gamma power in SZ, and highlight the utility of mean-field model to study macroscopic neural dynamics and their alterations in the illness.

Serotonergic Modulation of the Excitation/Inhibition Balance in the Visual Cortex

Serotonergic neurons constitute one of the main systems of neuromodulators, whose diffuse projections regulate the functions of the cerebral cortex. Serotonin (5-HT) is known to play a crucial role in the differential modulation of cortical activity related to behavioral contexts. Some features of the 5-HT signaling organization suggest its possible participation as a modulator of activity-dependent synaptic changes during the critical period of the primary visual cortex (V1). Cells of the serotonergic system are among the first neurons to differentiate and operate. During postnatal development, ramifications from raphe nuclei become massively distributed in the visual cortical area, remarkably increasing the availability of 5-HT for the regulation of excitatory and inhibitory synaptic activity. A substantial amount of evidence has demonstrated that synaptic plasticity at pyramidal neurons of the superficial layers of V1 critically depends on a fine regulation of the balance between excitation and inhibition (E/I). 5-HT could therefore play an important role in controlling this balance, providing the appropriate excitability conditions that favor synaptic modifications. In order to explore this possibility, the present work used in vitro intracellular electrophysiological recording techniques to study the effects of 5-HT on the E/I balance of V1 layer 2/3 neurons, during the critical period. Serotonergic action on the E/I balance has been analyzed on spontaneous activity, evoked synaptic responses, and long-term depression (LTD). Our results pointed out that the predominant action of 5-HT implies a reduction in the E/I balance. 5-HT promoted LTD at excitatory synapses while blocking it at inhibitory synaptic sites, thus shifting the Hebbian alterations of synaptic strength towards lower levels of E/I balance.

Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony

A myriad of pathological changes associated with epilepsy can be recast as decreases in cell and circuit heterogeneity. We thus propose recontextualizing epileptogenesis as a process where reduction in cellular heterogeneity, in part, renders neural circuits less resilient to seizure. By comparing patch clamp recordings from human layer 5 (L5) cortical pyramidal neurons from epileptogenic and non-epileptogenic tissue, we demonstrate significantly decreased biophysical heterogeneity in seizure-generating areas. Implemented computationally, this renders model neural circuits prone to sudden transitions into synchronous states with increased firing activity, paralleling ictogenesis. This computational work also explains the surprising finding of significantly decreased excitability in the population-activation functions of neurons from epileptogenic tissue. Finally, mathematical analyses reveal a bifurcation structure arising only with low heterogeneity and associated with seizure-like dynamics. Taken together, this work provides experimental, computational, and mathematical support for the theory that ictogenic dynamics accompany a reduction in biophysical heterogeneity.

Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks

The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. However, this learning rule needs extra time for updating operations at each step, causing extra energy consumption and reducing the computational efficiency. The event-driven or spike-based coding strategy of spiking neural networks (SNNs), i.e., neurons will only be active if driven by continuous spiking trains, employs all-or-none pulses (spikes) to transmit information, contributing to sparseness in neuron activations. In this article, we propose two event-driven IP learning rules, namely, input-driven and self-driven IP, based on basic IP learning. Input-driven means that IP updating occurs only when the neuron receives spiking inputs from its presynaptic neurons, whereas self-driven means that IP updating only occurs when the neuron generates a spike. A spiking convolutional neural network (SCNN) is developed based on the ANN2SNN conversion method, i.e., converting a well-trained rate-based artificial neural network to an SNN via directly mapping the connection weights. By comparing the computational performance of SCNNs with different IP rules on the recognition of MNIST, FashionMNIST, Cifar10, and SVHN datasets, we demonstrate that the two event-based IP rules can remarkably reduce IP updating operations, contributing to sparse computations and accelerating the recognition process. This work may give insights into the modeling of brain-inspired SNNs for low-power applications.

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons

The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

Bidirectionally Regulating Gamma Oscillations in Wilson-Cowan Model by Self-Feedback Loops: A Computational Study

The Wilson-Cowan model can emulate gamma oscillations, and thus is extensively used to research the generation of gamma oscillations closely related to cognitive functions. Previous studies have revealed that excitatory and inhibitory inputs to the model can modulate its gamma oscillations. Inhibitory and excitatory self-feedback loops are important structural features of the model, however, its functional role in the regulation of gamma oscillations in the model is still unclear. In the present study, bifurcation analysis and spectrum analysis are employed to elucidate the regulating mechanism of gamma oscillations underlined by the inhibitory and excitatory self-feedback loops, especially how the two self-feedback loops cooperate to generate the gamma oscillations and regulate the oscillation frequency. The present results reveal that, on one hand, the inhibitory self-feedback loop is not conducive to the generation of gamma oscillations, and increased inhibitory self-feedback strength facilitates the enhancement of the oscillation frequency. On the other hand, the excitatory self-feedback loop promotes the generation of gamma oscillations, and increased excitatory self-feedback strength leads to the decrease of oscillation frequency. Finally, theoretical analysis is conducted to provide explain on how the two self-feedback loops play a crucial role in the generation and regulation of neural oscillations in the model. To sum up, Inhibitory and excitatory self-feedback loops play a complementary role in generating and regulating the gamma oscillation in Wilson-Cowan model, and cooperate to bidirectionally regulate the gamma-oscillation frequency in a more flexible manner. These results might provide testable hypotheses for future experimental research.

COSMO-Onset: A Neurally-Inspired Computational Model of Spoken Word Recognition, Combining Top-Down Prediction and Bottom-Up Detection of Syllabic Onsets

Recent neurocognitive models commonly consider speech perception as a hierarchy of processes, each corresponding to specific temporal scales of collective oscillatory processes in the cortex: 30-80 Hz gamma oscillations in charge of phonetic analysis, 4-9 Hz theta oscillations in charge of syllabic segmentation, 1-2 Hz delta oscillations processing prosodic/syntactic units and the 15-20 Hz beta channel possibly involved in top-down predictions. Several recent neuro-computational models thus feature theta oscillations, driven by the speech acoustic envelope, to achieve syllabic parsing before lexical access. However, it is unlikely that such syllabic parsing, performed in a purely bottom-up manner from envelope variations, would be totally efficient in all situations, especially in adverse sensory conditions. We present a new probabilistic model of spoken word recognition, called COSMO-Onset, in which syllabic parsing relies on fusion between top-down, lexical prediction of onset events and bottom-up onset detection from the acoustic envelope. We report preliminary simulations, analyzing how the model performs syllabic parsing and phone, syllable and word recognition. We show that, while purely bottom-up onset detection is sufficient for word recognition in nominal conditions, top-down prediction of syllabic onset events allows overcoming challenging adverse conditions, such as when the acoustic envelope is degraded, leading either to spurious or missing onset events in the sensory signal. This provides a proposal for a possible computational functional role of top-down, predictive processes during speech recognition, consistent with recent models of neuronal oscillatory processes.

Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge

Current concepts of sensory processing in the cerebral cortex emphasize serial extraction and recombination of features in hierarchically structured feed-forward networks in order to capture the relations among the components of perceptual objects. These concepts are implemented in convolutional deep learning networks and have been validated by the astounding similarities between the functional properties of artificial systems and their natural counterparts. However, cortical architectures also display an abundance of recurrent coupling within and between the layers of the processing hierarchy. This massive recurrence gives rise to highly complex dynamics whose putative function is poorly understood. Here a concept is proposed that assigns specific functions to the dynamics of cortical networks and combines, in a unifying approach, the respective advantages of recurrent and feed-forward processing. It is proposed that the priors about regularities of the world are stored in the weight distributions of feed-forward and recurrent connections and that the high-dimensional, dynamic space provided by recurrent interactions is exploited for computations. These comprise the ultrafast matching of sensory evidence with the priors covertly represented in the correlation structure of spontaneous activity and the context-dependent grouping of feature constellations characterizing natural objects. The concept posits that information is encoded not only in the discharge frequency of neurons but also in the precise timing relations among the discharges. Results of experiments designed to test the predictions derived from this concept support the hypothesis that cerebral cortex exploits the high-dimensional recurrent dynamics for computations serving predictive coding.

Effect of Cross-Orientation Normalization on Different Neural Measures in Macaque Primary Visual Cortex

Divisive normalization is a canonical mechanism that can explain a variety of sensory phenomena. While normalization models have been used to explain spiking activity in response to different stimulus/behavioral conditions in multiple brain areas, it is unclear whether similar models can also explain modulation in population-level neural measures such as power at various frequencies in local field potentials (LFPs) or steady-state visually evoked potential (SSVEP) that is produced by flickering stimuli and popular in electroencephalogram studies. To address this, we manipulated normalization strength by presenting static as well as flickering orthogonal superimposed gratings (plaids) at varying contrasts to 2 female monkeys while recording multiunit activity (MUA) and LFP from the primary visual cortex and quantified the modulation in MUA, gamma (32-80 Hz), high-gamma (104-248 Hz) power, as well as SSVEP. Even under similar stimulus conditions, normalization strength was different for the 4 measures and increased as: spikes, high-gamma, SSVEP, and gamma. However, these results could be explained using a normalization model that was modified for population responses, by varying the tuned normalization parameter and semisaturation constant. Our results show that different neural measures can reflect the effect of stimulus normalization in different ways, which can be modeled by a simple normalization model.

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1

Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.

A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.

Modelling the effects of transcranial alternating current stimulation on the neural encoding of speech in noise

Transcranial alternating current stimulation (tACS) can non-invasively modulate neuronal activity in the cerebral cortex, in particular at the frequency of the applied stimulation. Such modulation can matter for speech processing, since the latter involves the tracking of slow amplitude fluctuations in speech by cortical activity. tACS with a current signal that follows the envelope of a speech stimulus has indeed been found to influence the cortical tracking and to modulate the comprehension of the speech in background noise. However, how exactly tACS influences the speech-related cortical activity, and how it causes the observed effects on speech comprehension, remains poorly understood. A computational model for cortical speech processing in a biophysically plausible spiking neural network has recently been proposed. Here we extended the model to investigate the effects of different types of stimulation waveforms, similar to those previously applied in experimental studies, on the processing of speech in noise. We assessed in particular how well speech could be decoded from the neural network activity when paired with the exogenous stimulation. We found that, in the absence of current stimulation, the speech-in-noise decoding accuracy was comparable to the comprehension of speech in background noise of human listeners. We further found that current stimulation could alter the speech decoding accuracy by a few percent, comparable to the effects of tACS on speech-in-noise comprehension. Our simulations further allowed us to identify the parameters for the stimulation waveforms that yielded the largest enhancement of speech-in-noise encoding. Our model thereby provides insight into the potential neural mechanisms by which weak alternating current stimulation may influence speech comprehension and allows to screen a large range of stimulation waveforms for their effect on speech processing.

Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions

An improved understanding of the mechanisms underlying neuromodulatory approaches to mitigate seizure onset is needed to identify clinical targets for the treatment of epilepsy. Using a Wilson–Cowan-motivated network of inhibitory and excitatory populations, we examined the role played by intrinsic and extrinsic stimuli on the network’s predisposition to sudden transitions into oscillatory dynamics, similar to the transition to the seizure state. Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such oscillations. Based on a combination of numerical simulations and mean-field analyses, our results suggest that high variance and/or high frequency stimulation waveforms can prevent multi-stability, a mathematical harbinger of sudden changes in network dynamics. By tuning the neurons’ responses to input, stimuli stabilize network dynamics away from these transitions. Furthermore, our research shows that such stabilization of neural activity occurs through a selective recruitment of inhibitory cells, providing a theoretical undergird for the known key role these cells play in both the healthy and diseased brain. Taken together, these findings provide new vistas on neuromodulatory approaches to stabilize neural microcircuit activity.

Dependency analysis of frequency and strength of gamma oscillations on input difference between excitatory and inhibitory neurons

It has been found that gamma oscillations and the oscillation frequencies are regulated by the properties of external stimuli in many biology experimental researches. To unveil the underlying mechanism, firstly, we reproduced the experimental observations in an excitatory/inhibitory (E/I) neuronal network that the oscillation became stronger and moved to a higher frequency band (gamma band) with the increasing of the input difference between E/I neurons. Secondly, we found that gamma oscillation was induced by the unbalance between positive and negative synaptic currents, which was caused by the input difference between E/I neurons. When this input difference became greater, there would be a stronger gamma oscillation (i.e., a higher peak power in the power spectrum of the population activity of neurons). Further investigation revealed that the frequency dependency of gamma oscillation on the input difference between E/I neurons could be explained by the well-known mechanisms of inter-neuron-gamma (ING) and pyramidal-interneuron-gamma (PING). Finally, we derived mathematical analysis to verify the mechanism of frequency regulations and the results were consistent with the simulation results. The results of this paper provide a possible mechanism for the external stimuli-regulated gamma oscillations.

Homeostatic mechanisms may shape the type and duration of oscillatory modulation

Neural oscillations are observed ubiquitously in the mammalian brain, but their stability is known to be rather variable. Some oscillations are tonic and last for seconds or even minutes. Other oscillations appear as unstable bursts. Likewise, some oscillations rely on excitatory AMPAergic synapses, but others are GABAergic and inhibitory. Why this diversity exists is not clear. We hypothesized Ca²⁺-dependent homeostasis could be important in finding an explanation. We tested this hypothesis in a highly simplified model of hippocampal neurons. In this model homeostasis profoundly alters the modulatory effect of neural oscillations. Under homeostasis, tonic AMPAergic oscillations actually decrease excitability and desynchronize firing. Tonic oscillations that are synaptically GABAergic-like those in real hippocampus-don't provoke a homeostatic response, however. If our simple model is correct, homeostasis can explain why the theta rhythm in the hippocampus is synaptically inhibitory: GABA has little to no intrinsic homeostatic response and so can preserve the pyramidal cell's natural dynamic range. Based on these results we speculate that homeostasis may explain why AMPAergic oscillations in cortex, and in hippocampus, often appear as bursts. Bursts do not interact with the slow homeostatic time constant and so retain their normal excitatory effect.NEW & NOTEWORTHY The intricate interplay of neuromodulators, like acetylcholine, with homeostasis is well known. The interplay between oscillatory modulation and homeostasis is not. We studied oscillatory modulation and homeostasis for the first time using a simplified model of hippocampus. We report a paradoxical result: Ca-mediated homeostasis causes AMPAergic oscillations to become effectively inhibitory. This result, along with other new observations, means homeostasis might be just as complex and important for oscillations as it is for other neuromodulators.

Cholinergic Modulation Promotes Attentional Modulation in Primary Visual Cortex- A Modeling Study

Attention greatly influences sensory neural processing by enhancing firing rates of neurons that represent the attended stimuli and by modulating their tuning properties. The cholinergic system is believed to partly mediate the attention contingent improvement of cortical processing by influencing neuronal excitability, synaptic transmission and neural network characteristics. Here, we used a biophysically based model to investigate the mechanisms by which cholinergic system influences sensory information processing in the primary visual cortex (V1) layer 4C. The physiological properties and architectures of our model were inspired by experimental data and include feed-forward input from dorsal lateral geniculate nucleus that sets up orientation preference in V1 neural responses. When including a cholinergic drive, we found significant sharpening in orientation selectivity, desynchronization of LFP gamma power and spike-field coherence, decreased response variability and correlation reduction mostly by influencing intracortical interactions and by increasing inhibitory drive. Our results indicated that these effects emerged due to changes specific to the behavior of the inhibitory neurons. The behavior of our model closely resembles the effects of attention on neural activities in monkey V1. Our model suggests precise mechanisms through which cholinergic modulation may mediate the effects of attention in the visual cortex.

Combined Positive and Negative Feedback Allows Modulation of Neuronal Oscillation Frequency during Sensory Processing

A key step in sensory information processing involves modulation and integration of neuronal oscillations in disparate frequency bands, a poorly understood process. Here, we investigate how top-down input causes frequency changes in slow oscillations during sensory processing and, in turn, how the slow oscillations are combined with fast oscillations (which encode sensory input). Using experimental connectivity patterns and strengths of interneurons, we develop a system-level model of a neuronal circuit controlling these oscillatory behaviors, allowing us to understand the mechanisms responsible for the observed oscillatory behaviors. Our analysis discovers a circuit capable of producing the observed oscillatory behaviors and finds that a detailed balance in the strength of synaptic connections is the critical determinant to produce such oscillatory behaviors. We not only uncover how disparate frequency bands are modulated and combined but also give insights into the causes of abnormal neuronal activities present in brain disorders.

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.

Cell-Type Specific Burst Firing Interacts with Theta and Beta Activity in Prefrontal Cortex During Attention States

Population-level theta and beta band activity in anterior cingulate and prefrontal cortices (ACC/PFC) are prominent signatures of self-controlled, adaptive behaviors. But how these rhythmic activities are linked to cell-type specific activity has remained unclear. Here, we suggest such a cell-to-systems level linkage. We found that the rate of burst spiking events is enhanced particularly during attention states and that attention-specific burst spikes have a unique temporal relationship to local theta and beta band population-level activities. For the 5-10 Hz theta frequency range, bursts coincided with transient increases of local theta power relative to nonbursts, particularly for bursts of putative interneurons. For the 16-30 Hz beta frequency, bursts of putative interneurons phase synchronized stronger than nonbursts, and were associated with larger beta power modulation. In contrast, burst of putative pyramidal cells showed similar beta power modulation as nonbursts, but were accompanied by stronger beta power only when they occurred early in the beta cycle. These findings suggest that in the ACC/PFC during attention states, mechanisms underlying burst firing are intimately linked to narrow band population-level activities, providing a cell-type specific window into rhythmic inhibitory gating and the emergence of rhythmically coherent network states during goal directed behavior.

Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression-An EEG Study on Rest-Stimulus Interaction

Major depressive disorder (MDD) is a complex psychiatric disorder characterized by changes in both resting state and stimulus-evoked activity. Whether resting state changes are carried over to stimulus-evoked activity, however, is unclear. We conducted a combined rest (3 min) and task (three-stimulus auditory oddball paradigm) EEG study in n=28 acute depressed MDD patients, comparing them with n=25 healthy participants. Our focus was on the temporal dynamics of both resting state and stimulus-evoked activity for which reason we measured peak frequency (PF), coefficient of variation (CV), Lempel-Ziv complexity (LZC), and trial-to-trial variability (TTV). Our main findings are: i) atypical temporal dynamics in resting state, specifically in the alpha and theta bands as measured by peak frequency (PF), coefficient of variation (CV) and power; ii) decreased reactivity to external deviant stimuli as measured by decreased changes in stimulus-evoked variance and complexity-TTV, LZC, and power and frequency sliding (FS and PS); iii) correlation of stimulus related measures (TTV, LZC, PS, and FS) with resting state measures. Together, our findings show that resting state dynamics alone are atypical in MDD and, even more important, strongly shapes the dynamics of subsequent stimulus-evoked activity. We thus conclude that MDD can be characterized by an atypical temporal dynamic of its rest-stimulus interaction; that, in turn, makes it difficult for depressed patients to react to relevant stimuli such as the deviant tone in our paradigm.

White matter plasticity and maturation in human cognition

White matter plasticity likely plays a critical role in supporting cognitive development. However, few studies have used the imaging methods specific to white matter tissue structure or experimental designs sensitive to change in white matter necessary to elucidate these relations. Here we briefly review novel imaging approaches that provide more specific information regarding white matter microstructure. Furthermore, we highlight recent studies that provide greater clarity regarding the relations between changes in white matter and cognition maturation in both healthy children and adolescents and those with white matter insult. Finally, we examine the hypothesis that white matter is linked to cognitive function via its impact on neural synchronization. We test this hypothesis in a population of children and adolescents with recurrent demyelinating syndromes. Specifically, we evaluate group differences in white matter microstructure within the optic radiation; and neural phase synchrony in visual cortex during a visual task between 25 patients and 28 typically developing age-matched controls. Children and adolescents with demyelinating syndromes show evidence of myelin and axonal compromise and this compromise predicts reduced phase synchrony during a visual task compared to typically developing controls. We investigate one plausible mechanism at play in this relationship using a computational model of gamma generation in early visual cortical areas. Overall, our findings show a fundamental connection between white matter microstructure and neural synchronization that may be critical for cognitive processing. In the future, longitudinal or interventional studies can build upon our knowledge of these exciting relations between white matter, neural communication, and cognition.

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.

Do Brain Oscillations Orchestrate Memory?

Rhythmicity and oscillations are common features in nature, and can be seen in phenomena such as seasons, breathing, and brain activity. Despite the fact that a single neuron transmits its activity to its neighbor through a transient pulse, rhythmic activity emerges from large population-wide activity in the brain, and such rhythms are strongly coupled with the state and cognitive functions of the brain. However, it is still debated whether the oscillations of brain activity actually carry information. Here, we briefly introduce the biological findings of brain oscillations, and summarize the recent progress in understanding how oscillations mediate brain function. Finally, we examine the possible relationship between brain cognitive function and oscillation, focusing on how oscillation is related to memory, particularly with respect to state-dependent memory formation and memory retrieval under specific brain waves. We propose that oscillatory waves in the neocortex contribute to the synchronization and activation of specific memory trace ensembles in the neocortex by promoting long-range neural communication.

Thalamic Reticular Dysfunction as a Circuit Endophenotype in Neurodevelopmental Disorders

Diagnoses of behavioral disorders such as autism spectrum disorder and schizophrenia are based on symptomatic descriptions that have been difficult to connect to mechanism. Although psychiatric genetics provide insight into the genetic underpinning of such disorders, with a majority of cases explained by polygenic factors, it remains difficult to design rational treatments. In this review, we highlight the value of understanding neural circuit function both as an intermediate level of explanatory description that links gene to behavior and as a pathway for developing rational diagnostics and therapeutics for behavioral disorders. As neural circuits perform hierarchically organized computational functions and give rise to network-level processes (e.g., macroscopic rhythms and goal-directed or homeostatic behaviors), correlated network-level deficits may indicate perturbation of a specific circuit. Therefore, identifying such correlated deficits or a circuit endophenotype would provide a mechanistic point of entry, enhancing both diagnosis and treatment of a given behavioral disorder. We focus on a circuit endophenotype of the thalamic reticular nucleus (TRN) and how its impairment in neurodevelopmental disorders gives rise to a correlated set of readouts across sleep and attention. Because TRN neurons express several disorder-relevant genes identified through genome-wide association studies, exploring the consequences of different TRN disruptions may be of broad translational significance.

Layer-specific excitation/inhibition balances during neuronal synchronization in the visual cortex

KEY POINTS

Understanding the balance between synaptic excitation and inhibition in cortical circuits in the brain, and how this contributes to cortical rhythms, is fundamental to explaining information processing in the cortex. This study used cortical layer-specific optogenetic activation in mouse cortex to show that excitatory neurons in any cortical layer can drive powerful gamma rhythms, while inhibition balances excitation. The net impact of this is to keep activity within each layer in check, but simultaneously to promote the propagation of activity to downstream layers. The data show that rhythm-generating circuits exist in all principle layers of the cortex, and provide layer-specific balances of excitation and inhibition that affect the flow of information across the layers.

ABSTRACT

Rhythmic activity can synchronize neural ensembles within and across cortical layers. While gamma band rhythmicity has been observed in all layers, the laminar sources and functional impacts of neuronal synchronization in the cortex remain incompletely understood. Here, layer-specific optogenetic stimulation demonstrates that populations of excitatory neurons in any cortical layer of the mouse's primary visual cortex are sufficient to powerfully entrain neuronal oscillations in the gamma band. Within each layer, inhibition balances excitation and keeps activity in check. Across layers, translaminar output overcomes inhibition and drives downstream firing. These data establish that rhythm-generating circuits exist in all principle layers of the cortex, but provide layer-specific balances of excitation and inhibition that may dynamically shape the flow of information through cortical circuits. These data might help explain how excitation/inhibition (E/I) balances across cortical layers shape information processing, and shed light on the diverse nature and functional impacts of cortical gamma rhythms.

A quantitative theory of gamma synchronization in macaque V1

Gamma-band synchronization coordinates brief periods of excitability in oscillating neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared frequency over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous frequency modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma frequencies. When similar enough, these frequencies continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized frequency difference. Crucially, the precise dynamics of frequencies and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled oscillators influence each other's phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous frequency modulations applies to gamma in V1 and is likely generalizable to other brain regions and rhythms.

Changes in White Matter Microstructure Impact Cognition by Disrupting the Ability of Neural Assemblies to Synchronize

Cognition is compromised by white matter (WM) injury but the neurophysiological alterations linking them remain unclear. We hypothesized that reduced neural synchronization caused by disruption of neural signal propagation is involved. To test this, we evaluated group differences in: diffusion tensor WM microstructure measures within the optic radiations, primary visual area (V1), and cuneus; neural phase synchrony to a visual attention cue during visual-motor task; and reaction time to a response cue during the same task between 26 pediatric patients (17/9: male/female) treated with cranial radiation treatment for a brain tumor (12.67 ± 2.76 years), and 26 healthy children (16/10: male/female; 12.01 ± 3.9 years). We corroborated our findings using a corticocortical computational model representing perturbed signal conduction from myelin. Patients show delayed reaction time, WM compromise, and reduced phase synchrony during visual attention compared with healthy children. Notably, using partial least-squares-path modeling we found that WM insult within the optic radiations, V1, and cuneus is a strong predictor of the slower reaction times via disruption of neural synchrony in visual cortex. Observed changes in synchronization were reproduced in a computational model of WM injury. These findings provide new evidence linking cognition with WM via the reliance of neural synchronization on propagation of neural signals.SIGNIFICANCE STATEMENT By comparing brain tumor patients to healthy children, we establish that changes in the microstructure of the optic radiations and neural synchrony during visual attention predict reaction time. Furthermore, by testing the directionality of these links through statistical modeling and verifying our findings with computational modeling, we infer a causal relationship, namely that changes in white matter microstructure impact cognition in part by disturbing the ability of neural assemblies to synchronize. Together, our human imaging data and computer simulations show a fundamental connection between WM microstructure and neural synchronization that is critical for cognitive processing.

State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications

Neural populations produce complex oscillatory patterns thought to implement brain function. The dominant rhythm in the healthy adult human brain is formed by alpha oscillations with a typical power peak most commonly found between 8 and 12Hz. This alpha peak frequency has been repeatedly discussed as a highly heritable and stable neurophysiological "trait" marker reflecting anatomical properties of the brain, and individuals' general cognitive capacity. However, growing evidence suggests that the alpha peak frequency is highly volatile at shorter time scales, dependent on the individuals' "state". Based on the converging experimental and theoretical results from numerous recent studies, here we propose that alpha frequency variability forms the basis of an adaptive mechanism mirroring the activation level of neural populations which has important functional implications. We here integrate experimental and computational perspectives to shed new light on the potential role played by shifts in alpha peak frequency and discuss resulting implications. We further propose a potential mechanism by which alpha oscillations are regulated in a noisy network of spiking neurons in presence of delayed feedback.

Shaping Intrinsic Neural Oscillations with Periodic Stimulation

Rhythmic brain activity plays an important role in neural processing and behavior. Features of these oscillations, including amplitude, phase, and spectrum, can be influenced by internal states (e.g., shifts in arousal, attention or cognitive ability) or external stimulation. Electromagnetic stimulation techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, and transcranial alternating current stimulation are used increasingly in both research and clinical settings. Currently, the mechanisms whereby time-dependent external stimuli influence population-scale oscillations remain poorly understood. Here, we provide computational insights regarding the mapping between periodic pulsatile stimulation parameters such as amplitude and frequency and the response dynamics of recurrent, nonlinear spiking neural networks. Using a cortical model built of excitatory and inhibitory neurons, we explored a wide range of stimulation intensities and frequencies systematically. Our results suggest that rhythmic stimulation can form the basis of a control paradigm in which one can manipulate the intrinsic oscillatory properties of driven networks via a plurality of input-driven mechanisms. Our results show that, in addition to resonance and entrainment, nonlinear acceleration is involved in shaping the rhythmic response of our modeled network. Such nonlinear acceleration of spontaneous and synchronous oscillatory activity in a neural network occurs in regimes of intense, high-frequency rhythmic stimulation. These results open new perspectives on the manipulation of synchronous neural activity for basic and clinical research.

SIGNIFICANCE STATEMENT

Oscillatory activity is widely recognized as a core mechanism for information transmission within and between brain circuits. Noninvasive stimulation methods can shape this activity, something that is increasingly capitalized upon in basic research and clinical practice. Here, we provide computational insights on the mechanistic bases for such effects. Our results show that rhythmic stimulation forms the basis of a control paradigm in which one can manipulate the intrinsic oscillatory properties of driven networks via a plurality of input-driven mechanisms. In addition to resonance and entrainment, nonlinear acceleration is involved in shaping the rhythmic response of our modeled network, particularly in regimes of high-frequency rhythmic stimulation. These results open new perspectives on the manipulation of synchronous neural activity for basic and clinical research.

Acetylcholine modulates gamma frequency oscillations in the hippocampus by activation of muscarinic M1 receptors

Modulation of gamma oscillations is important for the processing of information and the disruption of gamma oscillations is a prominent feature of schizophrenia and Alzheimer's disease. Gamma oscillations are generated by the interaction of excitatory and inhibitory neurons where their precise frequency and amplitude are controlled by the balance of excitation and inhibition. Acetylcholine enhances the intrinsic excitability of pyramidal neurons and suppresses both excitatory and inhibitory synaptic transmission, but the net modulatory effect on gamma oscillations is not known. Here, we find that the power, but not frequency, of optogenetically induced gamma oscillations in the CA3 region of mouse hippocampal slices is enhanced by low concentrations of the broad‐spectrum cholinergic agonist carbachol but reduced at higher concentrations. This bidirectional modulation of gamma oscillations is replicated within a mathematical model by neuronal depolarisation, but not by reducing synaptic conductances, mimicking the effects of muscarinic M1 receptor activation. The predicted role for M1 receptors was supported experimentally; bidirectional modulation of gamma oscillations by acetylcholine was replicated by a selective M1 receptor agonist and prevented by genetic deletion of M1 receptors. These results reveal that acetylcholine release in CA3 of the hippocampus modulates gamma oscillation power but not frequency in a bidirectional and dose‐dependent manner by acting primarily through muscarinic M1 receptors.

Cortical gamma band synchronization through somatostatin interneurons

Gamma band rhythms may synchronize distributed cell assemblies to facilitate information transfer within and across brain areas, yet their underlying mechanisms remain hotly debated. Most circuit models postulate that soma-targeting parvalbumin-positive GABAergic neurons are the essential inhibitory neuron subtype necessary for gamma rhythms. Using cell-type-specific optogenetic manipulations in behaving animals, we show that dendrite-targeting somatostatin (SOM) interneurons are critical for a visually induced, context-dependent gamma rhythm in visual cortex. A computational model independently predicts that context-dependent gamma rhythms depend critically on SOM interneurons. Further in vivo experiments show that SOM neurons are required for long-distance coherence across the visual cortex. Taken together, these data establish an alternative mechanism for synchronizing distributed networks in visual cortex. By operating through dendritic and not just somatic inhibition, SOM-mediated oscillations may expand the computational power of gamma rhythms for optimizing the synthesis and storage of visual perceptions.

Decorrelated Input Dissociates Narrow Band γ Power and BOLD in Human Visual Cortex

Although fMRI using the BOLD contrast is widely used for noninvasively mapping hemodynamic brain activity in humans, its exact link to underlying neural processing is poorly understood. Whereas some studies have reported that BOLD signals measured in visual cortex are tightly linked to neural activity in the narrow band γ (NBG) range, others have found a weak correlation between the two. To elucidate the mechanisms behind these conflicting findings, we hypothesized that BOLD reflects the strength of synaptic inputs to cortex, whereas NBG is more dependent on how well these inputs are correlated. To test this, we measured NBG, BOLD, and cerebral blood flow responses to stimuli that either correlate or decorrelate neural activity in human visual cortex. Next, we simulated a recurrent network model of excitatory and inhibitory neurons that reproduced in detail the experimental NBG and BOLD data. Results show that the visually evoked BOLD response was solely predicted by the sum of local inputs, whereas NBG was critically dependent on how well these inputs were correlated. In summary, the NBG-BOLD relationship strongly depends on the nature of sensory input to cortex: stimuli that increase the number of correlated inputs to visual cortex will increase NBG and BOLD in a similar manner, whereas stimuli that increase the number of decorrelated inputs will dissociate the two. The NBG-BOLD relationship is therefore not fixed but is rather highly dependent on input correlations that are both stimulus- and state-dependent.SIGNIFICANCE STATEMENT It is widely believed that γ oscillations in cortex are tightly linked to local hemodynamic activity. Here, we present experimental evidence showing how a stimulus can increase local blood flow to the brain despite suppressing γ power. Moreover, using a sophisticated model of cortical neurons, it is proposed that this occurs when synaptic input to cortex is strong yet decorrelated. Because input correlations are largely determined by the state of the brain, our results demonstrate that the relationship between γ and local hemodynamics is not fixed, but rather context dependent. This likely explains why certain neurodevelopmental disorders are characterized by weak γ activity despite showing normal blood flow.

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

Dynamic Control of Synchronous Activity in Networks of Spiking Neurons

Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system’s response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation‐inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject‐specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal‐interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models—and optimize the corresponding connectivity estimates at the within and between‐subject level. We also consider Bayesian cross‐validation to obtain predictive estimates for gamma‐response phenotypes, using a leave‐one‐out procedure. Hum Brain Mapp 37:4597–4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

How Does the Sparse Memory "Engram" Neurons Encode the Memory of a Spatial-Temporal Event?

Episodic memory in human brain is not a fixed 2-D picture but a highly dynamic movie serial, integrating information at both the temporal and the spatial domains. Recent studies in neuroscience reveal that memory storage and recall are closely related to the activities in discrete memory engram (trace) neurons within the dentate gyrus region of hippocampus and the layer 2/3 of neocortex. More strikingly, optogenetic reactivation of those memory trace neurons is able to trigger the recall of naturally encoded memory. It is still unknown how the discrete memory traces encode and reactivate the memory. Considering a particular memory normally represents a natural event, which consists of information at both the temporal and spatial domains, it is unknown how the discrete trace neurons could reconstitute such enriched information in the brain. Furthermore, as the optogenetic-stimuli induced recall of memory did not depend on firing pattern of the memory traces, it is most likely that the spatial activation pattern, but not the temporal activation pattern of the discrete memory trace neurons encodes the memory in the brain. How does the neural circuit convert the activities in the spatial domain into the temporal domain to reconstitute memory of a natural event? By reviewing the literature, here we present how the memory engram (trace) neurons are selected and consolidated in the brain. Then, we will discuss the main challenges in the memory trace theory. In the end, we will provide a plausible model of memory trace cell network, underlying the conversion of neural activities between the spatial domain and the temporal domain. We will also discuss on how the activation of sparse memory trace neurons might trigger the replay of neural activities in specific temporal patterns.

Abnormal Gamma Oscillations in N-Methyl-D-Aspartate Receptor Hypofunction Models of Schizophrenia

N-methyl-D-aspartate receptor (NMDAR) hypofunction in parvalbumin-expressing (PV+) inhibitory neurons (INs) may contribute to symptoms in patients with schizophrenia (SZ). This hypothesis was inspired by studies in humans involving NMDAR antagonists that trigger SZ symptoms. Animal models of SZ using neuropharmacology and genetic knockouts have successfully replicated some of the key observations in human subjects involving alteration of gamma band oscillations (GBO) observed in electroencephalography and magnetoencephalography signals. However, it remains to be seen if NMDAR hypofunction in PV+ neurons is fundamental to the phenotype observed in these models. In this review, we discuss some of the key computational models of GBO and their predictions in the context of NMDAR hypofunction in INs. While PV+ INs have been the main focus of SZ studies in animal models, we also discuss the implications of NMDAR hypofunction in other types of INs using computational models for GBO modulation in the visual cortex.

More Gamma More Predictions: Gamma-Synchronization as a Key Mechanism for Efficient Integration of Classical Receptive Field Inputs with Surround Predictions

During visual stimulation, neurons in visual cortex often exhibit rhythmic and synchronous firing in the gamma-frequency (30–90 Hz) band. Whether this phenomenon plays a functional role during visual processing is not fully clear and remains heavily debated. In this article, we explore the function of gamma-synchronization in the context of predictive and efficient coding theories. These theories hold that sensory neurons utilize the statistical regularities in the natural world in order to improve the efficiency of the neural code, and to optimize the inference of the stimulus causes of the sensory data. In visual cortex, this relies on the integration of classical receptive field (CRF) data with predictions from the surround. Here we outline two main hypotheses about gamma-synchronization in visual cortex. First, we hypothesize that the precision of gamma-synchronization reflects the extent to which CRF data can be accurately predicted by the surround. Second, we hypothesize that different cortical columns synchronize to the extent that they accurately predict each other’s CRF visual input. We argue that these two hypotheses can account for a large number of empirical observations made on the stimulus dependencies of gamma-synchronization. Furthermore, we show that they are consistent with the known laminar dependencies of gamma-synchronization and the spatial profile of intercolumnar gamma-synchronization, as well as the dependence of gamma-synchronization on experience and development. Based on our two main hypotheses, we outline two additional hypotheses. First, we hypothesize that the precision of gamma-synchronization shows, in general, a negative dependence on RF size. In support, we review evidence showing that gamma-synchronization decreases in strength along the visual hierarchy, and tends to be more prominent in species with small V1 RFs. Second, we hypothesize that gamma-synchronized network dynamics facilitate the emergence of spiking output that is particularly information-rich and sparse.

Potential Mechanisms Underlying Intercortical Signal Regulation via Cholinergic Neuromodulators

The dynamical behavior of the cortex is extremely complex, with different areas and even different layers of a cortical column displaying different temporal patterns. A major open question is how the signals from different layers and different brain regions are coordinated in a flexible manner to support function. Here, we considered interactions between primary auditory cortex and adjacent association cortex. Using a biophysically based model, we show how top-down signals in the beta and gamma regimes can interact with a bottom-up gamma rhythm to provide regulation of signals between the cortical areas and among layers. The flow of signals depends on cholinergic modulation: with only glutamatergic drive, we show that top-down gamma rhythms may block sensory signals. In the presence of cholinergic drive, top-down beta rhythms can lift this blockade and allow signals to flow reciprocally between primary sensory and parietal cortex.

SIGNIFICANCE STATEMENT

Flexible coordination of multiple cortical areas is critical for complex cognitive functions, but how this is accomplished is not understood. Using computational models, we studied the interactions between primary auditory cortex (A1) and association cortex (Par2). Our model is capable of replicating interaction patterns observed in vitro and the simulations predict that the coordination between top-down gamma and beta rhythms is central to the gating process regulating bottom-up sensory signaling projected from A1 to Par2 and that cholinergic modulation allows this coordination to occur.

Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling

Inhibition-stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from inhibitory interneurons, a circuit element found in the hippocampus and the primary visual cortex. In their working regime, ISNs produce damped oscillations in the [Formula: see text]-range in response to inputs to the inhibitory population. In order to understand the properties of interconnected ISNs, we investigated periodic forcing of ISNs. We show that ISNs can be excited over a range of frequencies and derive properties of the resonance peaks. In particular, we studied the phase-locked solutions, the torus solutions, and the resonance peaks. Periodically forced ISNs respond with (possibly multistable) phase-locked activity, whereas networks with sustained intrinsic oscillations respond more dynamically to periodic inputs with tori. Hence, the dynamics are surprisingly rich, and phase effects alone do not adequately describe the network response. This strengthens the importance of phase-amplitude coupling as opposed to phase-phase coupling in providing multiple frequencies for multiplexing and routing information.

Glial Regulation of the Neuronal Connectome through Local and Long-Distant Communication

If "the connectome" represents a complete map of anatomical and functional connectivity in the brain, it should also include glia. Glia define and regulate both the brain's anatomical and functional connectivity over a broad range of length scales, spanning the whole brain to subcellular domains of synaptic interactions. This Perspective article examines glial interactions with the neuronal connectome (including long-range networks, local circuits, and individual synaptic connections) and highlights opportunities for future research. Our understanding of the structure and function of the neuronal connectome would be incomplete without an understanding of how all types of glia contribute to neuronal connectivity and function, from single synapses to circuits.

Genetic control of morphogenesis in Dictyostelium

Cells grow, move, expand, shrink and die in the process of generating the characteristic shapes of organisms. Although the structures generated during development of the social amoeba Dictyostelium discoideum look nothing like the structures seen in metazoan embryogenesis, some of the morphogenetic processes used in their making are surprisingly similar. Recent advances in understanding the molecular basis for directed cell migration, cell type specific sorting, differential adhesion, secretion of matrix components, pattern formation, regulation and terminal differentiation are reviewed. Genes involved in Dictyostelium aggregation, slug formation, and culmination of fruiting bodies are discussed.

Speech encoding by coupled cortical theta and gamma oscillations

Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta–gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding.

DOI:http://dx.doi.org/10.7554/eLife.06213.001

Some people speak twice as fast as others, while people with different accents pronounce the same words in different ways. However, despite these differences between speakers, humans can usually follow spoken language with remarkable ease.

The different elements of speech have different frequencies: the typical frequency for syllables, for example, is about four syllables per second in speech. Phonemes, which are the smallest elements of speech, appear at a higher frequency. However, these elements are all transmitted at the same time, so the brain needs to be able to process them simultaneously.

The auditory cortex, the part of the brain that processes sound, produces various ‘waves’ of electrical activity, and these waves also have a characteristic frequency (which is the number of bursts of neural activity per second). One type of brain wave, called the theta rhythm, has a frequency of three to eight bursts per second, which is similar to the typical frequency of syllables in speech, and the frequency of another brain wave, the gamma rhythm, is similar to the frequency of phonemes. It has been suggested that these two brain waves may have a central role in our ability to follow speech, but to date there has been no direct evidence to support this theory.

Hyafil et al. have now used computer models of neural oscillations to explore this theory. Their simulations show that, as predicted, the theta rhythm tracks the syllables in spoken language, while the gamma rhythm encodes the specific features of each phoneme. Moreover, the two rhythms work together to establish the sequence of phonemes that makes up each syllable. These findings will support the development of improved speech recognition technologies.

DOI:http://dx.doi.org/10.7554/eLife.06213.002

Linear tuning of gamma amplitude and frequency to luminance contrast: evidence from a continuous mapping paradigm

Individual differences in the visual gamma (30–100Hz) response and their potential as trait markers of underlying physiology (particularly related to GABAergic inhibition) have become a matter of increasing interest in recent years. There is growing evidence, however, that properties of the gamma response (e.g., its amplitude and frequency) are highly stimulus dependent, and that individual differences in the gamma response may reflect individual differences in the stimulus tuning functions of gamma oscillations. Here, we measured the tuning functions of gamma amplitude and frequency to luminance contrast in eighteen participants using MEG. We used a grating stimulus in which stimulus contrast was modulated continuously over time. We found that both gamma amplitude and frequency were linearly modulated by stimulus contrast, but that the gain of this modulation (as reflected in the linear gradient) varied across individuals. We additionally observed a stimulus-induced response in the beta frequency range (10–25Hz), but neither the amplitude nor the frequency of this response was consistently modulated by the stimulus over time. Importantly, we did not find a correlation between the gain of the gamma-band amplitude and frequency tuning functions across individuals, suggesting that these may be independent traits driven by distinct neurophysiological processes.