Rapid temporal modulation of synchrony by competition in cortical interneuron networks

Beta-frequency sensory stimulation enhances gait rhythmicity through strengthened coupling between striatal networks and stepping movement

Stepping movement is delta (1-4 Hz) rhythmic and depends on sensory inputs. Stepping-related delta-rhythmic neural activity is coupled to beta (10-30 Hz) frequency dynamics that are also prominent in sensorimotor circuits. We explored how beta-frequency sensory stimulation influences stepping and dorsal striatal regulation of stepping. We delivered audiovisual stimulation at 10 or 145 Hz to mice voluntarily locomoting, while recording locomotion, cellular calcium dynamics and local field potentials (LFPs). We found that 10 Hz, but not 145 Hz stimulation prominently entrained striatal LFPs. Even though stimulation at both frequencies promoted locomotion and desynchronized striatal network, only 10 Hz stimulation enhanced the delta rhythmicity of stepping and strengthened the coupling between stepping and striatal LFP delta and beta oscillations. These results demonstrate that higher frequency sensory stimulation can modulate lower frequency striatal neural dynamics and improve stepping rhythmicity, highlighting the translational potential of non-invasive beta-frequency sensory stimulation for improving gait.

Beta-frequency sensory stimulation enhances gait rhythmicity through strengthened coupling between striatal networks and stepping movement

Stepping movement is delta (1-4 Hz) rhythmic and depends on sensory inputs. In addition to delta rhythms, beta (10-30 Hz) frequency dynamics are also prominent in the motor circuits and are coupled to neuronal delta rhythms both at the network and the cellular levels. Since beta rhythms are broadly supported by cortical and subcortical sensorimotor circuits, we explore how beta-frequency sensory stimulation influences delta-rhythmic stepping movement, and dorsal striatal circuit regulation of stepping. We delivered audiovisual stimulation at 10 Hz or 145 Hz to mice voluntarily locomoting, while simultaneously recording stepping movement, striatal cellular calcium dynamics and local field potentials (LFPs). We found that 10 Hz, but not 145 Hz stimulation prominently entrained striatal LFPs. Even though sensory stimulation at both frequencies promoted locomotion and desynchronized striatal network, only 10 Hz stimulation enhanced the delta rhythmicity of stepping movement and strengthened the coupling between stepping and striatal LFP delta and beta oscillations. These results demonstrate that higher frequency sensory stimulation can modulate lower frequency dorsal striatal neural dynamics and improve stepping rhythmicity, highlighting the translational potential of non-invasive beta-frequency sensory stimulation for improving gait.

Role of Interaction Delays in the Synchronization of Inhibitory Networks

Neural oscillations provide a means for efficient and flexible communication among different brain areas. Understanding the mechanisms of the generation of brain oscillations is crucial to determine principles of communication and information transfer in the brain circuits. It is well known that the inhibitory neurons play a major role in the generation of oscillations in the gamma range, in pure inhibitory networks, or in the networks composed of excitatory and inhibitory neurons. In this study, we explore the impact of different parameters and, in particular, the delay in the transmission of the signals between the neurons, on the dynamics of inhibitory networks. We show that increasing delay in a reasonable range increases the synchrony and stabilizes the oscillations. Unstable gamma oscillations characterized by a highly variable amplitude of oscillations can be observed in an intermediate range of delays. We show that in this range of delays, other experimentally observed phenomena such as sparse firing, variable amplitude and period, and the correlation between the instantaneous amplitude and period could be observed. The results broaden our understanding of the mechanism of the generation of the gamma oscillations in the inhibitory networks, known as the ING (interneuron-gamma) mechanism.

Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex

Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well-characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35–45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC). In LPFC, this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia

Treatment of schizophrenia has had limited success in treating core cognitive symptoms. The evidence of multi-gene involvement suggests that multi-target therapy may be needed. Meanwhile, the complexity of schizophrenia pathophysiology and psychopathology, coupled with the species-specificity of much of the symptomatology, places limits on analysis via animal models, in vitro assays, and patient assessment. Multiscale computer modeling complements these traditional modes of study. Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability. Our results are consistent with the idea that overly low or high gamma power is associated with pathological information flow and information processing. These data suggest the need for careful titration of schizophrenia pharmacotherapy to avoid extremes that alter information flow in different ways. These results also identify gamma power as a potential biomarker for monitoring pathology and multi-target pharmacotherapy.

Emergence of global synchronization in directed excitatory networks of type I neurons

The collective behaviour of neural networks depends on the cellular and synaptic properties of the neurons. The phase-response curve (PRC) is an experimentally obtainable measure of cellular properties that quantifies the shift in the next spike time of a neuron as a function of the phase at which stimulus is delivered to that neuron. The neuronal PRCs can be classified as having either purely positive values (type I) or distinct positive and negative regions (type II). Networks of type 1 PRCs tend not to synchronize via mutual excitatory synaptic connections. We study the synchronization properties of identical type I and type II neurons, assuming unidirectional synapses. Performing the linear stability analysis and the numerical simulation of the extended Kuramoto model, we show that feedforward loop motifs favour synchronization of type I excitatory and inhibitory neurons, while feedback loop motifs destroy their synchronization tendency. Moreover, large directed networks, either without feedback motifs or with many of them, have been constructed from the same undirected backbones, and a high synchronization level is observed for directed acyclic graphs with type I neurons. It has been shown that, the synchronizability of type I neurons depends on both the directionality of the network connectivity and the topology of its undirected backbone. The abundance of feedforward motifs enhances the synchronizability of the directed acyclic graphs.

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.

Dynamic Computation in Visual Thalamocortical Networks

Contemporary neurodynamical frameworks, such as coordination dynamics and winnerless competition, posit that the brain approximates symbolic computation by transitioning between metastable attractive states. This article integrates these accounts with electrophysiological data suggesting that coherent, nested oscillations facilitate information representation and transmission in thalamocortical networks. We review the relationship between criticality, metastability, and representational capacity, outline existing methods for detecting metastable oscillatory patterns in neural time series data, and evaluate plausible spatiotemporal coding schemes based on phase alignment. We then survey the circuitry and the mechanisms underlying the generation of coordinated alpha and gamma rhythms in the primate visual system, with particular emphasis on the pulvinar and its role in biasing visual attention and awareness. To conclude the review, we begin to integrate this perspective with longstanding theories of consciousness and cognition.

Muscarinic acetylcholine receptors control baseline activity and Hebbian stimulus timing-dependent plasticity in fusiform cells of the dorsal cochlear nucleus

Cholinergic modulation contributes to adaptive sensory processing by controlling spontaneous and stimulus-evoked neural activity and long-term synaptic plasticity. In the dorsal cochlear nucleus (DCN), in vitro activation of muscarinic acetylcholine receptors (mAChRs) alters the spontaneous activity of DCN neurons and interacts with N-methyl-d-aspartate (NMDA) and endocannabinoid receptors to modulate the plasticity of parallel fiber synapses onto fusiform cells by converting Hebbian long-term potentiation to anti-Hebbian long-term depression. Because noise exposure and tinnitus are known to increase spontaneous activity in fusiform cells as well as alter stimulus timing-dependent plasticity (StTDP), it is important to understand the contribution of mAChRs to in vivo spontaneous activity and plasticity in fusiform cells. In the present study, we blocked mAChRs actions by infusing atropine, a mAChR antagonist, into the DCN fusiform cell layer in normal hearing guinea pigs. Atropine delivery leads to decreased spontaneous firing rates and increased synchronization of fusiform cell spiking activity. Consistent with StTDP alterations observed in tinnitus animals, atropine infusion induced a dominant pattern of inversion of StTDP mean population learning rule from a Hebbian to an anti-Hebbian profile. Units preserving their initial Hebbian learning rules shifted toward more excitatory changes in StTDP, whereas units with initial suppressive learning rules transitioned toward a Hebbian profile. Together, these results implicate muscarinic cholinergic modulation as a factor in controlling in vivo fusiform cell baseline activity and plasticity, suggesting a central role in the maladaptive plasticity associated with tinnitus pathology.NEW & NOTEWORTHY This study is the first to use a novel method of atropine infusion directly into the fusiform cell layer of the dorsal cochlear nucleus coupled with simultaneous recordings of neural activity to clarify the contribution of muscarinic acetylcholine receptors (mAChRs) to in vivo fusiform cell baseline activity and auditory-somatosensory plasticity. We have determined that blocking the mAChRs increases the synchronization of spiking activity across the fusiform cell population and induces a dominant pattern of inversion in their stimulus timing-dependent plasticity. These modifications are consistent with similar changes established in previous tinnitus studies, suggesting that mAChRs might have a critical contribution in mediating the maladaptive alterations associated with tinnitus pathology. Blocking mAChRs also resulted in decreased fusiform cell spontaneous firing rates, which is in contrast with their tinnitus hyperactivity, suggesting that changes in the interactions between the cholinergic and GABAergic systems might also be an underlying factor in tinnitus pathology.

The Effects of Context and Attention on Spiking Activity in Human Early Visual Cortex

Here we report the first quantitative analysis of spiking activity in human early visual cortex. We recorded multi-unit activity from two electrodes in area V2/V3 of a human patient implanted with depth electrodes as part of her treatment for epilepsy. We observed well-localized multi-unit receptive fields with tunings for contrast, orientation, spatial frequency, and size, similar to those reported in the macaque. We also observed pronounced gamma oscillations in the local-field potential that could be used to estimate the underlying spiking response properties. Spiking responses were modulated by visual context and attention. We observed orientation-tuned surround suppression: responses were suppressed by image regions with a uniform orientation and enhanced by orientation contrast. Additionally, responses were enhanced on regions that perceptually segregated from the background, indicating that neurons in the human visual cortex are sensitive to figure-ground structure. Spiking responses were also modulated by object-based attention. When the patient mentally traced a curve through the neurons' receptive fields, the accompanying shift of attention enhanced neuronal activity. These results demonstrate that the tuning properties of cells in the human early visual cortex are similar to those in the macaque and that responses can be modulated by both contextual factors and behavioral relevance. Our results, therefore, imply that the macaque visual system is an excellent model for the human visual cortex.

Coherence between Rat Sensorimotor System and Hippocampus Is Enhanced during Tactile Discrimination

Rhythms with time scales of multiple cycles per second permeate the mammalian brain, yet neuroscientists are not certain of their functional roles. One leading idea is that coherent oscillation between two brain regions facilitates the exchange of information between them. In rats, the hippocampus and the vibrissal sensorimotor system both are characterized by rhythmic oscillation in the theta range, 5–12 Hz. Previous work has been divided as to whether the two rhythms are independent or coherent. To resolve this question, we acquired three measures from rats—whisker motion, hippocampal local field potential (LFP), and barrel cortex unit firing—during a whisker-mediated texture discrimination task and during control conditions (not engaged in a whisker-mediated memory task). Compared to control conditions, the theta band of hippocampal LFP showed a marked increase in power as the rats approached and then palpated the texture. Phase synchronization between whisking and hippocampal LFP increased by almost 50% during approach and texture palpation. In addition, a greater proportion of barrel cortex neurons showed firing that was phase-locked to hippocampal theta while rats were engaged in the discrimination task. Consistent with a behavioral consequence of phase synchronization, the rats identified the texture more rapidly and with lower error likelihood on trials in which there was an increase in theta-whisking coherence at the moment of texture palpation. These results suggest that coherence between the whisking rhythm, barrel cortex firing, and hippocampal LFP is augmented selectively during epochs in which the rat collects sensory information and that such coherence enhances the efficiency of integration of stimulus information into memory and decision-making centers.

In rats, the rhythms of whisking and hippocampal theta become coherent precisely when rats approach and explore a texture; higher coherence enhances the identification of texture.

Many regions of the mammalian brain exhibit oscillations in electrical activity. In rats, the 5–12 Hz theta rhythm is present in the hippocampus and in diverse areas of the cerebral cortex. What is the function of this rhythm? One proposal is that the exchange of information between two brain regions is facilitated whenever their respective oscillations are coherent. To test this idea, we ask whether theta oscillation in the hippocampus, a crucial memory structure located deep in the brain, is coherent with the rat’s rhythm of moving its whiskers and sensing the physical environment with them. We acquired hippocampal local field potentials (LFP)—extracellular voltage fluctuations within a small volume—while rats classified textures using cyclical whisker motion (“whisking”). At the moment of texture palpation, coherence between whisking and hippocampal theta oscillations increased by nearly 50%. At the same time, neuronal firing in sensory cortex became more phase-locked to the hippocampal theta oscillations. Rats identified the texture more rapidly and with lower error likelihood on trials characterized by an increase in hippocampal theta-whisking coherence during texture palpation. These results suggest that, as rats collect touch signals, enhanced coherence between the whisking rhythm, sensory cortex, and hippocampal LFP facilitates the integration of sensory information into memory and decision-making centers in the brain.

Quantification of bursting and synchrony in cultured hippocampal neurons

It is widely appreciated that neuronal networks exhibit patterns of bursting and synchrony that are not captured by simple measures such as average spike rate. These patterns can encode information or represent pathological behavior such as seizures. However, methods for quantifying bursting and synchrony are not agreed upon and can be confounded with spike rate measures. Previous validation has largely relied on in silico networks and single experimental conditions. How published measures of bursting and synchrony perform when applied to biological networks of varied average spike rate and subjected to varied experimental challenges is unclear. In multielectrode array recordings of network activity, we found that two mechanistically distinct drugs, cyclothiazide and bicuculline, produced equivalent increases in average spike rate but differed in bursting and synchrony. We applied several measures of bursting to the recordings (2 threshold interval methods and a surprise-based method) and found that a measure based on an average critical interval, adjusted for the array-wide spike rate, performed best in quantifying differential drug effects. To quantify synchrony, we compared a coefficient of variation-based measure, the recently proposed spike time tiling coefficient, the SPIKE-distance measure, and a global synchrony index. The spike time tiling coefficient, the SPIKE-distance measure, and the global synchrony index all captured a difference between drugs with the best performance exhibited by the global synchrony index. In summary, our exploration should aid other investigators by highlighting strengths and limitations of current methods.

Ketamine Alters Outcome-Related Local Field Potentials in Monkey Prefrontal Cortex

A subanesthetic dose of the noncompetitive N-methyl-d-aspartate receptor antagonist ketamine is known to induce a schizophrenia-like phenotype in humans and nonhuman primates alike. The transient behavioral changes mimic the positive, negative, and cognitive symptoms of the disease but the neural mechanisms behind these changes are poorly understood. A growing body of evidence indicates that the cognitive control processes associated with prefrontal cortex (PFC) regions relies on groups of neurons synchronizing at narrow-band frequencies measurable in the local field potential (LFP). Here, we recorded LFPs from the caudo-lateral PFC of 2 macaque monkeys performing an antisaccade task, which requires the suppression of an automatic saccade toward a stimulus and the initiation of a goal-directed saccade in the opposite direction. Preketamine injection activity showed significant differences in a narrow 20-30 Hz beta frequency band between correct and error trials in the postsaccade response epoch. Ketamine significantly impaired the animals' performance and was associated with a loss of the differences in outcome-specific beta-band power. Instead, we observed a large increase in high-gamma-band activity. Our results suggest that the PFC employs beta-band synchronization to prepare for top-down cognitive control of saccades and the monitoring of task outcome.

The dyslexia-associated gene DCDC2 is required for spike-timing precision in mouse neocortex

BACKGROUND

Variants in dyslexia-associated genes, including DCDC2, have been linked to altered neocortical activation, suggesting that dyslexia associated genes might play as yet unspecified roles in neuronal physiology.

METHODS

Whole-cell patch clamp recordings were used to compare the electrophysiological properties of regular spiking pyramidal neurons of neocortex in Dcdc2 knockout (KO) and wild-type mice. Ribonucleic acid sequencing and reverse transcriptase polymerase chain reaction were performed to identify and characterize changes in gene expression in Dcdc2 KOs.

RESULTS

Neurons in KOs showed increased excitability and decreased temporal precision in action potential firing. The RNA sequencing screen revealed that the N-methyl-D-aspartate receptor (NMDAR) subunit Grin2B was elevated in Dcdc2 KOs, and an electrophysiological assessment confirmed a functional increase in spontaneous NMDAR-mediated activity. Remarkably, the decreased action potential temporal precision could be restored in mutants by treatment with either the NMDAR antagonist (2R)-amino-5-phosphonovaleric acid or the NMDAR 2B subunit-specific antagonist Ro 25-6981.

CONCLUSIONS

These results link the function of the dyslexia-associated gene Dcdc2 to spike timing through activity of NMDAR.

Multiple Tests Based on a Gaussian Approximation of the Unitary Events Method with Delayed Coincidence Count

The unitary events (UE) method is one of the most popular and efficient methods used over the past decade to detect patterns of coincident joint spike activity among simultaneously recorded neurons. The detection of coincidences is usually based on binned coincidence count (Grün, 1996 ), which is known to be subject to loss in synchrony detection (Grün, Diesmann, Grammont, Riehle, & Aertsen, 1999 ). This defect has been corrected by the multiple shift coincidence count (Grün et al., 1999 ). The statistical properties of this count have not been further investigated until this work, the formula being more difficult to deal with than the original binned count. First, we propose a new notion of coincidence count, the delayed coincidence count, which is equal to the multiple shift coincidence count when discretized point processes are involved as models for the spike trains. Moreover, it generalizes this notion to nondiscretized point processes, allowing us to propose a new gaussian approximation of the count. Since unknown parameters are involved in the approximation, we perform a plug-in step, where unknown parameters are replaced by estimated ones, leading to a modification of the approximating distribution. Finally the method takes the multiplicity of the tests into account via a Benjamini and Hochberg approach (Benjamini & Hochberg, 1995 ), to guarantee a prescribed control of the false discovery rate. We compare our new method, MTGAUE (multiple tests based on a gaussian approximation of the unitary events) and the UE method proposed in Grün et al. ( 1999 ) over various simulations, showing that MTGAUE extends the validity of the previous method. In particular, MTGAUE is able to detect both profusion and lack of coincidences with respect to the independence case and is robust to changes in the underlying model. Furthermore MTGAUE is applied on real data.

Interneuron cell types are fit to function

Understanding brain circuits begins with an appreciation of their component parts - the cells. Although GABAergic interneurons are a minority population within the brain, they are crucial for the control of inhibition. Determining the diversity of these interneurons has been a central goal of neurobiologists, but this amazing cell type has so far defied a generalized classification system. Interneuron complexity within the telencephalon could be simplified by viewing them as elaborations of a much more finite group of developmentally specified cardinal classes that become further specialized as they mature. Our perspective emphasizes that the ultimate goal is to dispense with classification criteria and directly define interneuron types by function.

Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex

Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to cellular dynamics to network connectomics. We developed a model of sensory and motor neocortex consisting of 704 spiking model neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a two-joint virtual arm to reach to a fixed target. For each of 125 trained networks, we used 200 training sessions, each involving 15 s reaches to the target from 16 starting positions. Learning altered network dynamics, with enhancements to neuronal synchrony and behaviorally relevant information flow between neurons. After learning, networks demonstrated retention of behaviorally relevant memories by using proprioceptive information to perform reach-to-target from multiple starting positions. Networks dynamically controlled which joint rotations to use to reach a target, depending on current arm position. Learning-dependent network reorganization was evident in both sensory and motor populations: learned synaptic weights showed target-specific patterning optimized for particular reach movements. Our model embodies an integrative hypothesis of sensorimotor cortical learning that could be used to interpret future electrophysiological data recorded in vivo from sensorimotor learning experiments. We used our model to make the following predictions: learning enhances synchrony in neuronal populations and behaviorally relevant information flow across neuronal populations, enhanced sensory processing aids task-relevant motor performance and the relative ease of a particular movement in vivo depends on the amount of sensory information required to complete the movement.

Top-down beta rhythms support selective attention via interlaminar interaction: a model

Cortical rhythms have been thought to play crucial roles in our cognitive abilities. Rhythmic activity in the beta frequency band, around 20 Hz, has been reported in recent studies that focused on neural correlates of attention, indicating that top-down beta rhythms, generated in higher cognitive areas and delivered to earlier sensory areas, can support attentional gain modulation. To elucidate functional roles of beta rhythms and underlying mechanisms, we built a computational model of sensory cortical areas. Our simulation results show that top-down beta rhythms can activate ascending synaptic projections from L5 to L4 and L2/3, responsible for biased competition in superficial layers. In the simulation, slow-inhibitory interneurons are shown to resonate to the 20 Hz input and modulate the activity in superficial layers in an attention-related manner. The predicted critical roles of these cells in attentional gain provide a potential mechanism by which cholinergic drive can support selective attention.

Top-down signals originate from higher cognitive areas such as parietal and prefrontal cortex and propagate to earlier stages of the brain. They have been thought to be associated with selective attention, and recent physiological studies suggest that top-down signals in the beta frequency band can support selective attention. In this study, we employ a computational model to investigate potential mechanisms by which top-down beta rhythms can influence neural responses induced by presentation of stimuli. The model includes several cell types, reportedly crucial for generating cortical rhythmic activity in the gamma and beta frequency bands, and the simulation results show that top-down beta rhythms are capable of reproducing experimentally observed attentional effects on neural responses to visual stimuli. These modulatory effects of top-down beta rhythms are mainly induced via activation of ascending inhibition originating from deep layer slow inhibitory interneurons. Since the excitability of slow interneurons can be increased by cholinergic neuromodulators, these interneurons may mediate the effects of cholinergic tone on attention.

Cortical gamma oscillations: the functional key is activation, not cognition

Cortical oscillatory synchrony in the gamma range has been attracting increasing attention in cognitive neuroscience ever since being proposed as a solution to the so-called binding problem. This growing literature is critically reviewed in both its basic neuroscience and cognitive aspects. A physiological "default assumption" regarding these oscillations is introduced, according to which they signal a state of physiological activation of cortical tissue, and the associated need to balance excitation with inhibition in particular. As such these oscillations would belong among a variety of generic neural control operations that enable neural tissue to perform its systems level functions, without implementing those functions themselves. Regional control of cerebral blood flow provides an analogy in this regard, and gamma oscillations are tightly correlated with this even more elementary control operation. As correlates of neural activation they will also covary with cognitive activity, and this typically suffices to account for the covariation between gamma activity and cognitive task variables. A number of specific cases of gamma synchrony are examined in this light, including the original impetus for attributing cognitive significance to gamma activity, namely the experiments interpreted as evidence for "binding by synchrony". This examination finds no compelling reasons to assign functional roles to oscillatory synchrony in the gamma range beyond its generic functions at the level of infrastructural neural control.

Effects of spatiotemporal stimulus properties on spike timing correlations in owl monkey primary somatosensory cortex

The correlated discharges of cortical neurons in primary somatosensory cortex are a potential source of information about somatosensory stimuli. One aspect of neuronal correlations that has not been well studied is how the spatiotemporal properties of tactile stimuli affect the presence and magnitude of correlations. We presented single- and dual-point stimuli with varying spatiotemporal relationships to the hands of three anesthetized owl monkeys and recorded neuronal activity from 100-electrode arrays implanted in primary somatosensory cortex. Correlation magnitudes derived from joint peristimulus time histogram (JPSTH) analysis of single neuron pairs were used to determine the level of spike timing correlations under selected spatiotemporal stimulus conditions. Correlated activities between neuron pairs were commonly observed, and the proportions of correlated pairs tended to decrease with distance between the recorded neurons. Distance between stimulus sites also affected correlations. When stimuli were presented simultaneously at two sites, ∼37% of the recorded neuron pairs showed significant correlations when adjacent phalanges were stimulated, and ∼21% of the pairs were significantly correlated when nonadjacent digits were stimulated. Spatial proximity of paired stimuli also increased the average correlation magnitude. Stimulus onset asynchronies in the paired stimuli had small effects on the correlation magnitude. These results show that correlated discharges between neurons at the first level of cortical processing provide information about the relative locations of two stimuli on the hand.

Noise-induced synchronization in small world networks of phase oscillators

A small-world (SW) network of similar phase oscillators, interacting according to the Kuramoto model, is studied numerically. It is shown that deterministic Kuramoto dynamics on SW networks has various stable stationary states. This can be attributed to the so-called defect patterns in an SW network, which it inherits from deformation of helical patterns in its regular parent. Turning on an uncorrelated random force causes vanishing of the defect patterns, hence increasing the synchronization among oscillators for moderate noise intensities. This phenomenon, called stochastic synchronization, is generally observed in some natural networks such as the brain neural network.

Oscillatory synchronization model of attention to moving objects

The world is a dynamic environment hence it is important for the visual system to be able to deploy attention on moving objects and attentively track them. Psychophysical experiments indicate that processes of both attentional enhancement and inhibition are spatially focused on the moving objects; however the mechanisms of these processes are unknown. The studies indicate that the attentional selection of target objects is sustained via a feedforward-feedback loop in the visual cortical hierarchy and only the target objects are represented in attention-related areas. We suggest that feedback from the attention-related areas to early visual areas modulates the activity of neurons; establishes synchronization with respect to a common oscillatory signal for target items via excitatory feedback, and also establishes de-synchronization for distractor items via inhibitory feedback. A two layer computational neural network model with integrate-and-fire neurons is proposed and simulated for simple attentive tracking tasks. Consistent with previous modeling studies, we show that via temporal tagging of neural activity, distractors can be attentively suppressed from propagating to higher levels. However, simulations also suggest attentional enhancement of activity for distractors in the first layer which represents neural substrate dedicated for low level feature processing. Inspired by this enhancement mechanism, we developed a feature based object tracking algorithm with surround processing. Surround processing improved tracking performance by 57% in PETS 2001 dataset, via eliminating target features that are likely to suffer from faulty correspondence assignments.

Dynamical changes in neurons during seizures determine tonic to clonic shift

A tonic-clonic seizure transitions from high frequency asynchronous activity to low frequency coherent oscillations, yet the mechanism of transition remains unknown. We propose a shift in network synchrony due to changes in cellular response. Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network. The cellular response shift mechanism for the tonic-clonic seizure transition reproduces the population behavior closely when compared to EEG data.

Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the resonance blood oxygen level-dependent signal in macaque primary visual cortex

The functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signal is regularly used to assign neuronal activity to cognitive function. Recent analyses have shown that the local field potential (LFP) gamma power is a better predictor of the fMRI BOLD signal than spiking activity. However, LFP gamma power and spiking activity are usually correlated, clouding the analysis of the neural basis of the BOLD signal. We show that changes in LFP gamma power and spiking activity in the primary visual cortex (V1) of the awake primate can be dissociated by using grating and plaid pattern stimuli, which differentially engage surround suppression and cross-orientation inhibition/facilitation within and between cortical columns. Grating presentation yielded substantial V1 LFP gamma frequency oscillations and significant multi-unit activity. Plaid pattern presentation significantly reduced the LFP gamma power while increasing population multi-unit activity. The fMRI BOLD activity followed the LFP gamma power changes, not the multi-unit activity. Inference of neuronal activity from the fMRI BOLD signal thus requires detailed a priori knowledge of how different stimuli or tasks activate the cortical network.

Cellularly-driven differences in network synchronization propensity are differentially modulated by firing frequency

Spatiotemporal pattern formation in neuronal networks depends on the interplay between cellular and network synchronization properties. The neuronal phase response curve (PRC) is an experimentally obtainable measure that characterizes the cellular response to small perturbations, and can serve as an indicator of cellular propensity for synchronization. Two broad classes of PRCs have been identified for neurons: Type I, in which small excitatory perturbations induce only advances in firing, and Type II, in which small excitatory perturbations can induce both advances and delays in firing. Interestingly, neuronal PRCs are usually attenuated with increased spiking frequency, and Type II PRCs typically exhibit a greater attenuation of the phase delay region than of the phase advance region. We found that this phenomenon arises from an interplay between the time constants of active ionic currents and the interspike interval. As a result, excitatory networks consisting of neurons with Type I PRCs responded very differently to frequency modulation compared to excitatory networks composed of neurons with Type II PRCs. Specifically, increased frequency induced a sharp decrease in synchrony of networks of Type II neurons, while frequency increases only minimally affected synchrony in networks of Type I neurons. These results are demonstrated in networks in which both types of neurons were modeled generically with the Morris-Lecar model, as well as in networks consisting of Hodgkin-Huxley-based model cortical pyramidal cells in which simulated effects of acetylcholine changed PRC type. These results are robust to different network structures, synaptic strengths and modes of driving neuronal activity, and they indicate that Type I and Type II excitatory networks may display two distinct modes of processing information.

Synchronization of the firing of neurons in the brain is related to many cognitive functions, such as recognizing faces, discriminating odors, and coordinating movement. It is therefore important to understand what properties of neuronal networks promote synchrony of neural firing. One measure that is often used to determine the contribution of individual neurons to network synchrony is called the phase response curve (PRC). PRCs describe how the timing of neuronal firing changes depending on when input, such as a synaptic signal, is received by the neuron. A characteristic of PRCs that has previously not been well understood is that they change dramatically as the neuron's firing frequency is modulated. This effect carries potential significance, since cognitive functions are often associated with specific frequencies of network activity in the brain. We showed computationally that the frequency dependence of PRCs can be explained by the relative timing of ionic membrane currents with respect to the time between spike firings. Our simulations also showed that the frequency dependence of neuronal PRCs leads to frequency-dependent changes in network synchronization that can be different for different neuron types. These results further our understanding of how synchronization is generated in the brain to support various cognitive functions.

Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition

Neurons respond to sensory stimuli by altering the rate and temporal pattern of action potentials. These spike trains both encode and propagate information that guides behavior. Local inhibitory networks can affect the information encoded and propagated by neurons by altering correlations between different spike trains. Correlations introduce redundancy that can reduce encoding but also facilitate propagation of activity to downstream targets. Given this trade-off, how can networks maximize both encoding and propagation efficacy? Here, we examine this problem by measuring the effects of olfactory bulb inhibition on the pairwise statistics of mitral cell spiking. We evoked spiking activity in the olfactory bulb in vitro and measured how lateral inhibition shapes correlations across timescales. We show that inhibitory circuits simultaneously increase fast correlation (i.e., synchrony increases) and decrease slow correlation (i.e., firing rates become less similar). Further, we use computational models to show the benefits of fast correlation/slow decorrelation in the context of odor coding. Olfactory bulb inhibition enhances population-level discrimination of similar inputs, while improving propagation of mitral cell activity to cortex. Our findings represent a targeted strategy by which a network can optimize the correlation structure of its output in a dynamic, activity-dependent manner. This trade-off is not specific to the olfactory system, but rather our work highlights mechanisms by which neurons can simultaneously accomplish multiple, and sometimes competing, aspects of sensory processing.

From network structure to network reorganization: implications for adult neurogenesis

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.

A new look at gamma? High- (>60 Hz) γ-band activity in cortical networks: function, mechanisms and impairment

γ-band oscillations are thought to play a crucial role in information processing in cortical networks. In addition to oscillatory activity between 30 and 60 Hz, current evidence from electro- and magnetoencephalography (EEG/MEG) and local-field potentials (LFPs) has consistently shown oscillations >60 Hz (high γ-band) whose function and generating mechanisms are unclear. In the present paper, we summarize data that highlights the importance of high γ-band activity for cortical computations through establishing correlations between the modulation of oscillations in the 60-200 Hz frequency and specific cognitive functions. Moreover, we will suggest that high γ-band activity is impaired in neuropsychiatric disorders, such as schizophrenia and epilepsy. In the final part of the paper, we will review physiological mechanisms underlying the generation of high γ-band oscillations and discuss the functional implications of low vs. high γ-band activity patterns in cortical networks.

Neurophysiological and computational principles of cortical rhythms in cognition

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.

Attention reduces stimulus-driven gamma frequency oscillations and spike field coherence in V1

Rhythmic activity of neuronal ensembles has been proposed to play an important role in cognitive functions such as attention, perception, and memory. Here we investigate whether rhythmic activity in V1 of the macaque monkey (macaca mulatta) is affected by top-down visual attention. We measured the local field potential (LFP) and V1 spiking activity while monkeys performed an attention-demanding detection task. We show that gamma oscillations were strongly modulated by the stimulus and by attention. Stimuli that engaged inhibitory mechanisms induced the largest gamma LFP oscillations and the largest spike field coherence. Directing attention toward a visual stimulus at the receptive field of the recorded neurons decreased LFP gamma power and gamma spike field coherence. This decrease could reflect an attention-mediated reduction of surround inhibition. Changes in synchrony in V1 would thus be a byproduct of reduced inhibitory drive, rather than a mechanism that directly aids perceptual processing.

Amplification of asynchronous inhibition-mediated synchronization by feedback in recurrent networks

Synchronization of 30–80 Hz oscillatory activity of the principle neurons in the olfactory bulb (mitral cells) is believed to be important for odor discrimination. Previous theoretical studies of these fast rhythms in other brain areas have proposed that principle neuron synchrony can be mediated by short-latency, rapidly decaying inhibition. This phasic inhibition provides a narrow time window for the principle neurons to fire, thus promoting synchrony. However, in the olfactory bulb, the inhibitory granule cells produce long lasting, small amplitude, asynchronous and aperiodic inhibitory input and thus the narrow time window that is required to synchronize spiking does not exist. Instead, it has been suggested that correlated output of the granule cells could serve to synchronize uncoupled mitral cells through a mechanism called “stochastic synchronization”, wherein the synchronization arises through correlation of inputs to two neural oscillators. Almost all work on synchrony due to correlations presumes that the correlation is imposed and fixed. Building on theory and experiments that we and others have developed, we show that increased synchrony in the mitral cells could produce an increase in granule cell activity for those granule cells that share a synchronous group of mitral cells. Common granule cell input increases the input correlation to the mitral cells and hence their synchrony by providing a positive feedback loop in correlation. Thus we demonstrate the emergence and temporal evolution of input correlation in recurrent networks with feedback. We explore several theoretical models of this idea, ranging from spiking models to an analytically tractable model.

Neurons in many parts of the brain fire spikes rhythmically and synchronously in many behaviorally and functionally relevant contexts. There are many mechanisms for producing oscillatory synchronization between populations of biological oscillators. One way to produce synchrony is that the population of oscillators receives common correlated input. In this paper, we study a population of oscillating neurons (mitral cells) that are not directly coupled to each other but receive broadband correlated input from a second population of neurons (granule cells). The granule cell population, in turn, receives inputs from the mitral cells; hence, the mitral and granule cells are reciprocally connected. Correlated input to the oscillating mitral cells produces tighter synchrony in the activity of the mitral cell population. We hypothesize that this increased mitral cell synchrony will evoke greater activity in specific groups of granule cells and that these specific granule cells, in turn, become the source of the correlated input to the mitral cells. That is, the synchronous input from the mitral cells increases the fraction of correlated feedback. Thus, we close the correlation loop. We show through analysis and simulations that this feedback mechanism can lead to the spontaneous appearance of highly synchronous activity within the mitral cells. We show that there is good experimental support for this mechanism in the circuitry of the olfactory bulb. We speculate that such mechanisms could also arise in other parts of the brain.

Coherence potentials: loss-less, all-or-none network events in the cortex

Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.

Pattern motion selectivity of spiking outputs and local field potentials in macaque visual cortex

The dorsal pathway of the primate visual cortex is involved in the processing of motion signals that are useful for perception and behavior. Along this pathway, motion information is first measured by the primary visual cortex (V1), which sends specialized projections to extrastriate regions such as the middle temporal area (MT). Previous work with plaid stimuli has shown that most V1 neurons respond to the individual components of moving stimuli, whereas some MT neurons are capable of estimating the global motion of the pattern. In this work, we show that the majority of neurons in the medial superior temporal area (MST), which receives input from MT, have this pattern-selective property. Interestingly, the local field potentials (LFPs) measured simultaneously with the spikes often exhibit properties similar to that of the presumptive feedforward input to each area: in the high-gamma frequency band, the LFPs in MST are as component selective as the spiking outputs of MT, and MT LFPs have plaid responses that are similar to the spiking outputs of V1. In the lower LFP frequency bands (beta and low gamma), component selectivity is very common, and pattern selectivity is almost entirely absent in both MT and MST. Together, these results suggest a surprisingly strong link between the sensory tuning of cortical LFPs and afferent inputs, with important implications for the interpretation of imaging studies and for models of cortical function.

Quantifying statistical interdependence by message passing on graphs-part I: one-dimensional point processes

We present a novel approach to quantify the statistical interdependence of two time series, referred to as stochastic event synchrony (SES). The first step is to extract the two given time series. The next step is to try to align events from one time series with events from the other. The better the alignment the more similar the two series are considered to be. More precisely, the similarity is quantified by the following parameters: time delay, variance of the time jitter, fraction of noncoincident events, and average similarity of the aligned events. The pairwise alignment and SES parameters are determined by statistical inference. In particular, the SES parameters are computed by maximum a posteriori (MAP) estimation, and the pairwise alignment is obtained by applying the max product algorithm. This letter deals with one-dimensional point processes; the extension to multidimensional point processes is considered in a companion letter in this issue. By analyzing surrogate data, we demonstrate that SES is able to quantify both timing precision and event reliability more robustly than classical measures can. As an illustration, neuronal spike data generated by Morris-Lecar neuron model are considered.

Informational lesions: optical perturbation of spike timing and neural synchrony via microbial opsin gene fusions

Synchronous neural activity occurs throughout the brain in association with normal and pathological brain functions. Despite theoretical work exploring how such neural coordination might facilitate neural computation and be corrupted in disease states, it has proven difficult to test experimentally the causal role of synchrony in such phenomena. Attempts to manipulate neural synchrony often alter other features of neural activity such as firing rate. Here we evaluate a single gene which encodes for the blue-light gated cation channel channelrhodopsin-2 and the yellow-light driven chloride pump halorhodopsin from Natronobacterium pharaonis, linked by a ‘self-cleaving’ 2A peptide. This fusion enables proportional expression of both opsins, sensitizing neurons to being bi-directionally controlled with blue and yellow light, facilitating proportional optical spike insertion and deletion upon delivery of trains of precisely-timed blue and yellow light pulses. Such approaches may enable more detailed explorations of the causal role of specific features of the neural code.

Neural synchrony in cortical networks: history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies.

Attention: oscillations and neuropharmacology

Attention is a rich psychological and neurobiological construct that influences almost all aspects of cognitive behaviour. It enables enhanced processing of behaviourally relevant stimuli at the expense of irrelevant stimuli. At the cellular level, rhythmic synchronization at local and long-range spatial scales complements the attention-induced firing rate changes of neurons. The former is hypothesized to enable efficient communication between neuronal ensembles tuned to spatial and featural aspects of the attended stimulus. Recent modelling studies suggest that the rhythmic synchronization in the gamma range may be mediated by a fine balance between N-methyl-d-aspartate and α-amino-3-hydroxy-5-methylisoxazole-4-propionate postsynaptic currents, whereas other studies have highlighted the possible contribution of the neuromodulator acetylcholine. This review summarizes some recent modelling and experimental studies investigating mechanisms of attention in sensory areas and discusses possibilities of how glutamatergic and cholinergic systems could contribute to increased processing abilities at the cellular and network level during states of top-down attention.

Linked activity of neurons in the sensorimotor cortex of the rabbit in the state of a defensive dominant and "animal hypnosis"

A cryptic focus of excitation (a dominant focus) was created in the brains of rabbits by threshold stimulation of the left limb with a current at a frequency of 0.5 Hz. After creation of a focus, there were equal probabilities of detecting pairs of neurons whose linked activity was dominated by a 2-sec rhythm in the sensorimotor cortex of both the right and left hemispheres (29.3% and 32.4%, respectively). When animals were placed in "animal hypnosis," the total proportion of neuron pairs whose activity was dominated by the rhythm created by establishment of the dominant decreased significantly only in the right hemisphere (21%). After exiting the state of animal hypnosis, the proportion of neurons in the cortex of the right hemisphere whose activity was dominated by the 2-sec rhythm increased significantly if the neurons in the pair were close-lying but decreased significantly if the neurons in the pair were mutually distant. No such changes after hypnotization were seen in the cortex of the left hemisphere. In both the right and left hemispheres, dominance of the 2-sec rhythm in the activity of pairs of neurons was seen significantly more frequently when cross-correlation histograms were constructed by analyzing cells in relation to the spike activity of neurons generating spikes of the lowest (right hemisphere) or lowest and intermediate (left hemisphere) amplitude on neurograms of multineuron activity.

Interaction of cellular and network mechanisms in spatiotemporal pattern formation in neuronal networks

Spatiotemporal patterning of neuronal activity is considered to be an important feature of cognitive processing in the brain as well as pathological brain states, such as seizures. Here, we investigate complex interactions between intrinsic properties of neurons and network structure in the generation of network spatiotemporal patterning in the context of seizure-like synchrony. We show that membrane excitability properties have differential effects on network activity patterning for different network topologies. We consider excitatory networks consisting of neurons with excitability properties varying between type I and type II that exhibit significantly different spike frequency responses to external current stimulation, especially at firing threshold. We find that networks with type II-like neurons show higher synchronization and bursting capacity across a range of network topologies than corresponding networks with type I-like neurons. These differences in activity patterning are persistent across different network sizes, connectivity strengths, magnitudes of random external input, and the addition of inhibitory interneurons to the network, making them highly likely to be relevant to brain function. Furthermore, we show that heterogeneous networks of mixed cell types show emergent dynamical patterns even for very low mixing ratios. Specifically, the addition of a small percentage of type II-like cells into a network of type I-like cells can markedly change the patterning of network activity. These findings suggest that cellular as well as network mechanisms can go hand in hand, leading to the generation of seizure-like discharges, suggesting that a single ictogenic mechanism alone may not be responsible for seizure generation.

Gamma oscillations mediate stimulus competition and attentional selection in a cortical network model

Simultaneous presentation of multiple stimuli can reduce the firing rates of neurons in extrastriate visual cortex below the rate elicited by a single preferred stimulus. We describe computational results suggesting how this remarkable effect may arise from strong excitatory drive to a substantial local population of fast-spiking inhibitory interneurons, which can lead to a loss of coherence in that population and thereby raise the effectiveness of inhibition. We propose that in attentional states fast-spiking interneurons may be subject to a bath of inhibition resulting from cholinergic activation of a second class of inhibitory interneurons, restoring conditions needed for gamma rhythmicity. Oscillations and coherence are emergent features, not assumptions, in our model. The gamma oscillations in turn support stimulus competition. The mechanism is a form of "oscillatory selection," in which neural interactions change phase relationships that regulate firing rates, and attention shapes those neural interactions.

Synchrony and variability induced by spatially correlated additive and multiplicative noise in the coupled Langevin model

The synchrony and variability of the coupled Langevin model subjected to spatially correlated additive and multiplicative noise are discussed. We have employed numerical simulations and the analytical augmented-moment method, which is the second-order moment method for local and global variables [H. Hasegawa, Phys. Rev. E 67, 041903 (2003)]. It has been shown that the synchrony of an ensemble is increased (decreased) by a positive (negative) spatial correlation in both additive and multiplicative noise. Although the variability for local fluctuations is almost insensitive to spatial correlations, that for global fluctuations is increased (decreased) by positive (negative) correlations. When a pulse input is applied, the synchrony is increased for the correlated multiplicative noise, whereas it may be decreased for correlated additive noise coexisting with uncorrelated multiplicative noise. An application of our study to neuron ensembles has demonstrated the possibility that information is conveyed by the variance and synchrony in input signals, which accounts for some neuronal experiments.

Reliability, synchrony and noise

The brain is noisy. Neurons receive tens of thousands of highly fluctuating inputs and generate spike trains that appear highly irregular. Much of this activity is spontaneous - uncoupled to overt stimuli or motor outputs - leading to questions about the functional impact of this noise. Although noise is most often thought of as disrupting patterned activity and interfering with the encoding of stimuli, recent theoretical and experimental work has shown that noise can play a constructive role - leading to increased reliability or regularity of neuronal firing in single neurons and across populations. These results raise fundamental questions about how noise can influence neural function and computation.

What happens to the brain in weightlessness? A first approach by EEG tomography

Basic changes in environmental conditions are fundamental to understanding brain cortical mechanisms. Several studies have reported impairment of central nervous processes during weightlessness. There is ongoing debate as to whether these impairments are attributable to primary physiological effects or secondary psychological effects of the weightlessness environment. This study evaluates the physiological effects of changed gravity conditions on brain cortical activity. In a first experiment, EEG activity of seven participants was recorded at normal, increased and zero gravity during a parabolic flight. Additionally an EEG under normal gravity conditions preflight was recorded. In a second experiment, 24 participants were exposed to a supine, seated and 9 degree head-down tilt position while EEG was recorded. Data were analysed using low resolution brain electromagnetic tomography (LORETA). Beta-2 EEG activity (18-35 Hz) was found to be increased in the right superior frontal gyrus under normal gravity conditions inflight. By exposure to weightlessness a distinct inhibition of this activity within the same areas could be noticed. As the tilt experiment showed changes in the left inferior temporal gyrus in supine and tilted positions we conclude that the observed changes under weightlessness are not explainable by hemodynamic changes but rather reflect emotional processes related to the experience of weightlessness. These findings suggest that weightlessness has a major impact on electro cortical activity and may affect central nervous and adaptation processes.

Role of interneuron diversity in the cortical microcircuit for attention

Receptive fields of neurons in cortical area V4 are large enough to fit multiple stimuli, making V4 the ideal place to study the effects of selective attention at the single-neuron level. Experiments have revealed evidence for stimulus competition and have characterized the effect thereon of spatial and feature-based attention. We developed a biophysical model with spiking neurons and conductance-based synapses. To account for the comprehensive set of experimental results, it was necessary to include in the model, in addition to regular spiking excitatory (E) cells, two types of interneurons: feedforward interneurons (FFI) and top-down interneurons (TDI). Feature-based attention was mediated by a projection of the TDI to the FFI, stimulus competition was mediated by a cross-columnar excitatory connection to the FFI, whereas spatial attention was mediated by an increase in activity of the feedforward inputs from cortical area V2. The model predicts that spatial attention increases the FFI firing rate, whereas feature-based attention decreases the FFI firing rate and increases the TDI firing rate. During strong stimulus competition, the E cells were synchronous in the beta frequency range (15-35 Hz), but with feature-based attention, they became synchronous in the gamma frequency range (35-50 Hz). We propose that the FFI correspond to fast-spiking, parvalbumin-positive basket cells and that the TDI correspond to cells with a double-bouquet morphology that are immunoreactive to calbindin or calretinin. Taken together, the model results provide an experimentally testable hypothesis for the behavior of two interneuron types under attentional modulation.