Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex

Oscillations in an artificial neural network convert competing inputs into a temporal code

The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and complex cells in early visual cortex. However, the computational relevance of oscillatory dynamics experimentally observed in the visual system are typically not considered in artificial neural networks (ANNs). Computational models of neocortical dynamics, on the other hand, rarely take inspiration from computer vision. Here, we combine methods from computational neuroscience and machine learning to implement multiplexing in a simple ANN using oscillatory dynamics. We first trained the network to classify individually presented letters. Post-training, we added temporal dynamics to the hidden layer, introducing refraction in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, indicating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activate sequentially, ordered along the phase of the inhibitory oscillations. Our model implements the idea that inhibitory oscillations segregate competing inputs in time. The results of our simulations pave the way for applications in deeper network architectures and more complicated machine learning problems.

Chronic Sustained Hypoxia Leads to Brainstem Tauopathy and Declines the Power of Rhythms in the Ventrolateral Medulla: Shedding Light on a Possible Mechanism

Hypoxia, especially the chronic type, leads to disruptive results in the brain that may contribute to the pathogenesis of some neurodegenerative diseases such as Alzheimer's disease (AD). The ventrolateral medulla (VLM) contains clusters of interneurons, such as the pre-Bötzinger complex (preBötC), that generate the main respiratory rhythm drive. We hypothesized that exposing animals to chronic sustained hypoxia (CSH) might develop tauopathy in the brainstem, consequently changing the rhythmic manifestations of respiratory neurons. In this study, old (20-22 months) and young (2-3 months) male rats were subjected to CSH (10 ± 0.5% O2) for ten consecutive days. Western blotting and immunofluorescence (IF) staining were used to evaluate phosphorylated tau. Mitochondrial membrane potential (MMP or ∆ψm) and reactive oxygen species (ROS) production were measured to assess mitochondrial function. In vivo diaphragm's electromyography (dEMG) and local field potential (LFP) recordings from preBötC were employed to assess the respiratory factors and rhythmic representation of preBötC, respectively. Findings showed that ROS production increased significantly in hypoxic groups, associated with a significant decline in ∆ψm. In addition, tau phosphorylation elevated in the brainstem of hypoxic groups. On the other hand, the power of rhythms declined significantly in the preBötC of hypoxic rats, parallel with changes in the respiratory rate, total respiration time, and expiration time. Moreover, there was a positive and statistically significant correlation between LFP rhythm's power and inspiration time. Our data showed that besides CSH, aging also contributed to mitochondrial dysfunction, tau hyperphosphorylation, LFP rhythms' power decline, and changes in respiratory factors.

How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden

As COVID-19 shows a heterogeneous spreading process globally, investigating factors associated with COVID-19 spreading among different countries will provide information for containment strategy and medical service decisions. A significant challenge for analyzing how these factors impact COVID-19 transmission is assessing key epidemiological parameters and how they change under different containment strategies across different nations. This paper builds a COVID-19 spread simulation model to estimate the core COVID-19 epidemiological parameters. Then, the correlation between these core COVID-19 epidemiological parameters and the times of publicly announced interventions is analyzed, including three typical countries, China (strictly containment), the USA (moderately control), and Sweden (loose control). Results show that the recovery rate leads to a distinct COVID-19 transmission process in the three countries, as all three countries finally have similar and close to zero spreading rates in the third period of COVID-19 transmission. Then, an epidemic fundamental diagram between COVID-19 "active infections" and "current patients" is discovered, which could plan a country's COVID-19 medical capacity and containment strategies when combined with the COVID-19 spreading simulation model. Based on that, the hypothetical policies are proved effectively, which will give support for future infectious diseases.

Energy-efficiency computing of up and down transitions in a neural network

Spontaneous periodic up and down transitions of membrane potentials are considered to be a significant spontaneous activity of slow-wave sleep. Previous theoretical studies have shown that stimulation frequency and the dynamics of intrinsic currents have a major influence on synchronicity and firing rate of spontaneous fluctuation. Energy consumption is driven by internal spontaneous activity. However, its energy consumption and energy efficiency are not clear. Therefore, this article simulates the up and down transitions based on a neural network and discusses the energy consumption and energy efficiency. It is found that the dynamics of intrinsic currents have a great impact on the energy consumption and energy efficiency in the process. The energy consumption is influenced by the size of the period and the average power consumption of the state. The average power consumption by the up state is always greater than the consumption by the down state, and the energy consumption of the transition is more than firing. In addition, the lower average proportion of duration of the up state in the cycle leads to higher energy efficiency. Energy consumption is reduced and energy efficiency is enhanced by adjusting parameters of the network. The study helps us to understand and further explore the metabolic consumption of spontaneous activities.NEW & NOTEWORTHY We use a more biological neural network to explore energy consumption and energy efficiency of up and down transitions. Specifically, we find that average energy consumption is more than that caused by action potentials, which proves that metabolic consumption is acquired substantially in the resting state as well. We also find that energy efficiency is influenced by the proportion of duration of the up state in the cycle. These findings may further improve the economy of the nervous system.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons

Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e., if the mean number of synapses per neuron K is large and synaptic efficacy is of the order of 1 / K . When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of the order of 1/ log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine-tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.

Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

As coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyzed the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detected change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we could quantify the effect of interventions and incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.

Energy features in spontaneous up and down oscillations

Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities.

Delta Rhythm Orchestrates the Neural Activity Underlying the Resting State BOLD Signal via Phase-amplitude Coupling

Spontaneous ongoing neuronal activity is a prominent feature of the mammalian brain. Temporal and spatial patterns of such ongoing activity have been exploited to examine large-scale brain network organization and function. However, the neurophysiological basis of this spontaneous brain activity as detected by resting-state functional Magnetic Resonance Imaging (fMRI) remains poorly understood. To this end, multi-site local field potentials (LFP) and blood oxygenation level-dependent (BOLD) fMRI were simultaneously recorded in the rat striatum along with local pharmacological manipulation of striatal activity. Results demonstrate that delta (δ) band LFP power negatively, while beta (β) and gamma (γ) band LFPs positively correlated with BOLD fluctuation. Furthermore, there was strong cross-frequency phase-amplitude coupling (PAC), with the phase of δ LFPs significantly modulating the amplitude of the high frequency signal. Enhancing dopaminergic neuronal activity significantly reduced ventral striatal functional connectivity, δ LFP-BOLD correlation, and the PAC effect. These data suggest that different frequency bands of the LFP contribute distinctively to BOLD spontaneous fluctuation and that PAC is the organizing mechanism through which low frequency LFPs orchestrate neural activity that underlies resting state functional connectivity.

Predicting neuronal dynamics with a delayed gain control model

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements-intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.

Single and population coding of taste in the gustatory cortex of awake mice

Electrophysiological analysis has revealed much about the broad coding and neural ensemble dynamics that characterize gustatory cortical (GC) taste processing in awake rats and about how these dynamics relate to behavior. With regard to mice, however, data concerning cortical taste coding have largely been restricted to imaging, a technique that reveals average levels of neural responsiveness but that (currently) lacks the temporal sensitivity necessary for evaluation of fast response dynamics; furthermore, the few extant studies have thus far failed to provide consensus on basic features of coding. We have recorded the spiking activity of ensembles of GC neurons while presenting representatives of the basic taste modalities (sweet, salty, sour, and bitter) to awake mice. Our first central result is the identification of similarities between rat and mouse taste processing: most mouse GC neurons (~66%) responded distinctly to multiple (3-4) tastes; temporal coding analyses further reveal, for the first time, that single mouse GC neurons sequentially code taste identity and palatability, the latter responses emerging ~0.5 s after the former, with whole GC ensembles transitioning suddenly and coherently from coding taste identity to coding taste palatability. The second finding is that spatial location plays very little role in any aspect of taste responses: neither between- (anterior-posterior) nor within-mouse (dorsal-ventral) mapping revealed anatomic regions with narrow or temporally simple taste responses. These data confirm recent results showing that mouse cortical taste responses are not "gustotopic" but also go beyond these imaging results to show that mice process tastes through time.NEW & NOTEWORTHY Here, we analyzed taste-related spiking activity in awake mouse gustatory cortical (GC) neural ensembles, revealing deep similarities between mouse cortical taste processing and that repeatedly demonstrated in rat: mouse GC ensembles code multiple aspects of taste in a coarse-coded, time-varying manner that is essentially invariant across the spatial extent of GC. These data demonstrate that, contrary to some reports, cortical network processing is distributed, rather than being separated out into spatial subregion.

Tone frequency representation beyond the tonotopic map: Cross-correlation between ongoing activity in the rat auditory cortex

Functional maps play crucial roles in the neural representations of the sensory cortices, although such representations occasionally extend beyond these maps. For example, the auditory cortex exhibits distinct tonotopic activation at the onset of tone, which is followed by rapid decays in the majority of neuronal signals and ongoing activities in only a small number of neurons. Such ongoing activity should be maintained by the cortical states. To better understand maintenance of ongoing activity beyond that triggered directly by stimuli, we used a rat model. Here, we hypothesized that neural correlations between local field potentials (LFPs) within a local area of the auditory cortex may serve as a measure of the cortical state underlying ongoing activity. We densely mapped the auditory cortex of rats and demonstrated that cross-correlation patterns of ongoing activity were highly decodable. Informative features were widely distributed over the auditory cortex and across multiple frequency bands. Furthermore, acoustic trauma disrupted tonotopic representation at the onset but did not affect neural representations by the correlation of ongoing activities. These results suggest that cross-correlations of LFP within the auditory cortex represent frequencies of sustained auditory stimuli, and that these representations are made beyond direct tonotopic activation at stimulus onset.

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential (V _m) fluctuations are driven by the convergence of synaptic inputs from a diverse cross-section of upstream neurons. Furthermore, neural activity is often scale-free, implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scale-freeness support the hypothesis that single V _m recordings carry useful information about high-dimensional cortical activity. Conveniently, the theory of "critical branching networks" (one purported explanation for scale-freeness) provides testable predictions about scale-free measurements that are readily applied to V _m fluctuations. To investigate, we obtained whole-cell current-clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in V _m below the firing threshold and analyzed them by adapting the definition of "neuronal avalanches" (i.e., spurts of population spiking). The V _m fluctuations which we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays described previously (Shew et al., 2015). Simultaneously recorded single-unit local field potential did not provide a good match, demonstrating the specific utility of V _m Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence of critical phenomena while also establishing subthreshold pyramidal neuron V _m fluctuations as an informative gauge of high-dimensional cortical population activity.SIGNIFICANCE STATEMENT The relationship between membrane potential (V _m) dynamics of single neurons and population dynamics is indispensable to understanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from single-neuron whole-cell recordings with population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic V _m scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain's own subsampling method (convergence of synaptic inputs) while extending the range of fundamental evidence for critical phenomena in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, V _m fluctuations.

A coarse-graining framework for spiking neuronal networks: from strongly-coupled conductance-based integrate-and-fire neurons to augmented systems of ODEs

Homogeneously structured, fluctuation-driven networks of spiking neurons can exhibit a wide variety of dynamical behaviors, ranging from homogeneity to synchrony. We extend our partitioned-ensemble average (PEA) formalism proposed in Zhang et al. (Journal of Computational Neuroscience, 37(1), 81-104, 2014a) to systematically coarse grain the heterogeneous dynamics of strongly coupled, conductance-based integrate-and-fire neuronal networks. The population dynamics models derived here successfully capture the so-called multiple-firing events (MFEs), which emerge naturally in fluctuation-driven networks of strongly coupled neurons. Although these MFEs likely play a crucial role in the generation of the neuronal avalanches observed in vitro and in vivo, the mechanisms underlying these MFEs cannot easily be understood using standard population dynamic models. Using our PEA formalism, we systematically generate a sequence of model reductions, going from Master equations, to Fokker-Planck equations, and finally, to an augmented system of ordinary differential equations. Furthermore, we show that these reductions can faithfully describe the heterogeneous dynamic regimes underlying the generation of MFEs in strongly coupled conductance-based integrate-and-fire neuronal networks.

Highly Heterogeneous Excitatory Connections Require Less Amount of Noise to Sustain Firing Activities in Cortical Networks

Cortical networks both in vivo and in vitro sustain asynchronous irregular firings with extremely low frequency. To realize such self-sustained activity in neural network models, balance between excitatory and inhibitory activities is known to be one of the keys. In addition, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., sparse but strong connections and dense weak connections, plays an essential role. The previous studies, however, have not thoroughly considered the cooperative dynamics between a network of such heterogeneous synaptic connections and intrinsic noise. The noise stimuli, representing inherent nature of the neuronal activities, e.g., variability of presynaptic discharges, should be also of significant importance for sustaining the irregular firings in cortical networks. Here, we numerically demonstrate that highly heterogeneous distribution, typically a lognormal type, of excitatory-to-excitatory connections, reduces the amount of noise required to sustain the network firing activities. In the sense that noise consumes an energy resource, the heterogeneous network receiving less amount of noise stimuli is considered to realize an efficient dynamics in cortex. A noise-driven network of bi-modally distributed synapses further shows that many weak and a few very strong synapses are the key feature of the synaptic heterogeneity, supporting the network firing activity.

Activation of axon initial segmental GABAA receptors inhibits action potential generation in neocortical GABAergic interneurons

Ionotropic GABA_A receptors expressing at the axon initial segment (AIS) of glutamatergic pyramidal cell (PC) in the cortex plays critical roles in regulating action potential generation. However, it remains unclear whether these receptors also express at the AIS of cortical GABAergic interneurons. In mouse prefrontal cortical slices, we performed experiments at the soma and AIS of the two most abundant GABAergic interneurons: parvalbumin (PV) and somatostatin (SST) positive neurons. Local application of GABA at the perisomatic axonal regions could evoke picrotoxin-sensitive currents with a reversal potential near the Cl^- equilibrium potential. Puffing agonists to outside-out patches excised from AIS confirmed the expression of GABA_A receptors. Further pharmacological experiments revealed that GABA_A receptors in AIS of PV neurons contain α1 subunits, different from those containing α2/3 in AIS and α4 in axon trunk of layer-5 PCs. Cell-attached recording at the soma of PV and SST neurons revealed that the activation of AIS GABA_A receptors inhibits the action potential generation induced by synaptic stimulation. Together, our results demonstrate that the AIS of PV and SST neurons express GABA_A receptors with distinct subunit composition, which exert an inhibitory effect on neuronal excitability in these inhibitory interneurons.

Clustering promotes switching dynamics in networks of noisy neurons

Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

Intrinsic sodium currents and excitatory synaptic transmission influence spontaneous firing in up and down activities

Periodic up and down transitions of membrane potentials are considered to be a significant spontaneous activity. These kinds of oscillations always accompany with some spontaneous firing in up state. Our previous theoretical studies mainly looked at the subthreshold up and down transitions and characteristics of up and down dynamics. In this paper, we focus on suprathreshold spontaneous firing of up and down transitions based on improved network model and its stimulations. The simulated results indicate that fast sodium current is critical to the generation of spontaneous neural firing. While persistent sodium current plays a part in spontaneous fluctuation. Both intrinsic fast and persistent sodium dynamics influence spontaneous firing rate and synchronous activity in up and down behavior. Meanwhile, blocking excitatory synaptic transmission decreases neural firing and reveals spontaneous firing. These simulated results are basically in accordance with experimental results. Through the observation and analysis of the findings, we prove the validity of the model so we can further adopt this model to study other properties and characteristics of the network, laying the foundation for further work on cortex activity.

Genetically expressed voltage sensor ArcLight for imaging large scale cortical activity in the anesthetized and awake mouse

With the recent breakthrough in genetically expressed voltage indicators (GEVIs), there has been a tremendous demand to determine the capabilities of these sensors in vivo. Novel voltage sensitive fluorescent proteins allow for direct measurement of neuron membrane potential changes through changes in fluorescence. Here, we utilized ArcLight, a recently developed GEVI, and examined the functional characteristics in the widely used mouse somatosensory whisker pathway. We measured the resulting evoked fluorescence using a wide-field microscope and a CCD camera at 200 Hz, which enabled voltage recordings over the entire cortical region with high temporal resolution. We found that ArcLight produced a fluorescent response in the S1 barrel cortex during sensory stimulation at single whisker resolution. During wide-field cortical imaging, we encountered substantial hemodynamic noise that required additional post hoc processing through noise subtraction techniques. Over a period of 28 days, we found clear and consistent ArcLight fluorescence responses to a simple sensory input. Finally, we demonstrated the use of ArcLight to resolve cortical S1 sensory responses in the awake mouse. Taken together, our results demonstrate the feasibility of ArcLight as a measurement tool for mesoscopic, chronic imaging.

Visual stimulation quenches global alpha range activity in awake primate V4: a case study

Increasing evidence suggests that sensory stimulation not only changes the level of cortical activity with respect to baseline but also its structure. Despite having been reported in a multitude of conditions and preparations (for instance, as a quenching of intertrial variability, Churchland et al., 2010), such changes remain relatively poorly characterized. Here, we used optical imaging of voltage-sensitive dyes to explore, in V4 of an awake macaque, the spatiotemporal characteristics of both visually evoked and spontaneously ongoing neuronal activity and their difference. With respect to the spontaneous case, we detected a reduction in large-scale activity ([Formula: see text]) in the alpha range (5 to 12.5 Hz) during sensory inflow accompanied by a decrease in pairwise correlations. Moreover, the spatial patterns of correlation obtained during the different visual stimuli were on the average more similar one to another than they were to that obtained in the absence of stimulation. Finally, these observed changes in activity dynamics approached saturation already at very low stimulus contrasts, unlike the progressive, near-linear increase of the mean raw evoked responses over a wide range of contrast values, which could indicate a specific switching in the presence of a sensory inflow.

Modulation of Synaptic Plasticity in the Cortex Needs to Understand All the Players

The prefrontal cortex (PFC) is involved in cognitive tasks such as working memory, decision making, risk assessment and regulation of attention. These functions performed by the PFC are supposed to rely on rhythmic electrical activity generated by neuronal network oscillations determined by a precise balance between excitation and inhibition balance (E/I balance) resulting from the coordinated activities of recurrent excitation and feedback and feedforward inhibition. Functional alterations in PFC functions have been associated with cognitive deficits in several pathologies such as major depression, anxiety and schizophrenia. These pathological situations are correlated with alterations of different neurotransmitter systems (i.e., serotonin (5-HT), dopamine (DA), acetylcholine…) that result in alterations of the E/I balance. The aim of this review article is to cover the basic aspects of the regulation of the E/I balance as well as to highlight the importance of the complementarity role of several neurotransmitters in the modulation of the plasticity of excitatory and inhibitory synapses. We illustrate our purpose by recent findings that demonstrate that 5-HT and DA cooperate to regulate the plasticity of excitatory and inhibitory synapses targeting layer 5 pyramidal neurons (L5PyNs) of the PFC and to fine tune the E/I balance. Using a method based on the decomposition of the synaptic conductance into its excitatory and inhibitory components, we show that concomitant activation of D1-like receptors (D1Rs) and 5-HT_1ARs, through a modulation of NMDA receptors, favors long term potentiation (LTP) of both excitation and inhibition and consequently does not modify the E/I balance. We also demonstrate that activation of D2-receptors requires functional 5-HT_1ARs to shift the E-I balance towards more inhibition and to favor long term depression (LTD) of excitatory synapses through the activation of glycogen synthase kinase 3β (GSK3β). This cooperation between different neurotransmitters is particularly relevant in view of pathological situations in which alterations of one neurotransmitter system will also have consequences on the regulation of synaptic efficacy by other neurotransmitters. This opens up new perspectives in the development of therapeutic strategies for the pharmacological treatment of neuronal disorders.

Synchronous transitions of up and down states in a network model based on stimulations

The phenomenon of spontaneous periodic up and down transitions is considered to be a significant characteristic of slow oscillations. Our previous theoretical studies have shown that the single neuron and network model can both exhibit spontaneous up and down transitions. Another characteristic of up and down dynamics is the synchronicity. So in this paper, we focused on the synchronized characteristic of up and down transitions in the network based on stimulations. Spontaneous activities showed no synchronous transitions between neurons. However, the external stimulation, mainly the stimulation frequency and the number of neurons stimulated on were related to the synchronous transitions of up and down states. The simulation results suggested that simultaneous high frequency excitation or firing of neurons in the network was responsible for the generation of synchronous transitions of up and down states. Through the observation and analysis of the findings, we have tried to explain the reason for synchronous up and down transitions and to lay the foundation for further work on the role of these synchronized transitions in cortex activity.

Rapid Sensory Adaptation Redux: A Circuit Perspective

Adaptation is fundamental to life. All organisms adapt over timescales that span from evolution to generations and lifetimes to moment-by-moment interactions. The nervous system is particularly adept at rapidly adapting to change, and this in fact may be one of its fundamental principles of organization and function. Rapid forms of sensory adaptation have been well documented across all sensory modalities in a wide range of organisms, yet we do not have a comprehensive understanding of the adaptive cellular mechanisms that ultimately give rise to the corresponding percepts, due in part to the complexity of the circuitry. In this Perspective, we aim to build links between adaptation at multiple scales of neural circuitry by investigating the differential adaptation across brain regions and sub-regions and across specific cell types, for which the explosion of modern tools has just begun to enable. This investigation points to a set of challenges for the field to link functional observations to adaptive properties of the neural circuit that ultimately underlie percepts.

Distinct Corticostriatal and Intracortical Pathways Mediate Bilateral Sensory Responses in the Striatum

Individual striatal neurons integrate somatosensory information from both sides of the body, however, the afferent pathways mediating these bilateral responses are unclear. Whereas ipsilateral corticostriatal projections are prevalent throughout the neocortex, contralateral projections provide sparse input from primary sensory cortices, in contrast to the dense innervation from motor and frontal regions. There is, therefore, an apparent discrepancy between the observed anatomical pathways and the recorded striatal responses. We used simultaneous in vivo whole-cell and extracellular recordings combined with focal cortical silencing, to dissect the afferent pathways underlying bilateral sensory integration in the mouse striatum. We show that unlike direct corticostriatal projections mediating responses to contralateral whisker deflection, responses to ipsilateral stimuli are mediated mainly by intracortical projections from the contralateral somatosensory cortex (S1). The dominant pathway is the callosal projection from contralateral to ipsilateral S1. Our results suggest a functional difference between the cortico-basal ganglia pathways underlying bilateral sensory and motor processes.

A Spiking Neural Network System for Robust Sequence Recognition

This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software.

The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions

During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz), synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states) and almost complete silence (Down states). The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.

Impact of membrane bistability on dynamical response of neuronal populations

Neurons in many brain areas can develop a pronounced depolarized state of membrane potential (up state) in addition to the normal hyperpolarized state near the resting potential. The influence of the up state on signal encoding, however, is not well investigated. Here we construct a one-dimensional bistable neuron model and calculate the linear dynamical response to noisy oscillatory inputs analytically. We find that with the appearance of an up state, the transmission function is enhanced by the emergence of a local maximum at some optimal frequency and the phase lag relative to the input signal is reduced. We characterize the dependence of the enhancement of frequency response on intrinsic dynamics and on the occupancy of the up state.

Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states

The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.

Contribution of parvalbumin and somatostatin-expressing GABAergic neurons to slow oscillations and the balance in beta-gamma oscillations across cortical layers

Cortical interneurons are classified into several subtypes that contribute to cortical oscillatory activity. Parvalbumin (PV)-expressing cells, a type of inhibitory interneuron, are involved in the gamma oscillations of local field potentials (LFPs). Under ketamine-xylazine anesthesia or sleep, mammalian cortical circuits exhibit slow oscillations in which the active-up state and silent-down state alternate at ~1 Hz. The up state is composed of various high-frequency oscillations, including gamma oscillations. However, it is unclear how PV cells and somatostatin (SOM) cells contribute to the slow oscillations and the high-frequency oscillations nested in the up state. To address these questions, we used mice lacking glutamate decarboxylase 67, primarily in PV cells (PV-GAD67 mice) or in SOM cells (SOM-GAD67 mice). We then compared LFPs between PV-GAD67 mice and SOM-GAD67 mice. PV cells target the proximal regions of pyramidal cells, whereas SOM cells are dendrite-preferring interneurons. We found that the up state was shortened in duration in the PV-GAD67 mice, but tended to be longer in SOM-GAD67 mice. Firing rate tended to increase in PV-GAD67 mice, but tended to decrease in SOM-GAD67 mice. We also found that delta oscillations tended to increase in SOM-GAD67 mice, but tended to decrease in PV-GAD67 mice. Current source density and wavelet analyses were performed to determine the depth profiles of various high-frequency oscillations. High gamma and ripple (60–200 Hz) power decreased in the neocortical upper layers specifically in PV-GAD67 mice, but not in SOM-GAD67. In addition, beta power (15–30 Hz) increased in the deep layers, specifically in PV-GAD67 mice. These results suggest that PV cells play important roles in persistence of the up state and in the balance between gamma and beta bands across cortical layers, whereas SOM and PV cells may make an asymmetric contribution to regulate up-state and delta oscillations.

A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony

In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.

Frequency-dependent signal processing in apical dendrites of hippocampal CA1 pyramidal cells

Depending on an animal's behavioral state, hippocampal CA1 pyramidal cells receive distinct patterns of excitatory and inhibitory synaptic inputs. The time-dependent changes in the frequencies of these inputs and the nonuniform distribution of voltage-gated channels lead to dynamic fluctuations in membrane conductance. In this study, using a whole-cell patch-clamp method, we attempted to record and analyze the frequency dependencies of membrane responsiveness in Wistar rat hippocampal CA1 pyramidal cells following noise current injection directly into dendrites and somata under pharmacological blockade of all synaptic inputs. To estimate the frequency-dependent properties of membrane potential, membrane impedance was determined from the voltage response divided by the input current in the frequency domain. The cell membrane of most neurons showed low-pass filtering properties in all regions. In particular, the properties were strongly expressed in the somata or proximal dendrites. Moreover, the data revealed nonuniform distribution of dendritic impedance, which was high in the intermediate segment of the apical dendritic shaft (∼220-260μm from the soma). The low-pass filtering properties in the apical dendrites were more enhanced by membrane depolarization than those in the somata. Coherence spectral analysis revealed high coherence between the input signal and the output voltage response in the theta-gamma frequency range, and large lags emerged in the distal dendrites in the gamma frequency range. Our results suggest that apical dendrites of hippocampal CA1 pyramidal cells integrate synaptic inputs according to the frequency components of the input signal along the dendritic segments receiving the inputs.

Neurodynamics of up and down transitions in a single neuron

Recent experimental studies have revealed that up and down transitions exist in membrane potential of neurons. This paper focuses on the neurodynamical research of these transitions in a single neuron since it is the basic to study the transitions in the neural network for further work. The results show there exists two stable levels in the neuron called up and down states. And transitions between these two states are bidirectional or unidirectional with the values of parameters changing. We also study the periodic spontaneous activity of the transitions between up and down states without any inputting stimulus which coheres with the experimental results.

Binocular input coincidence mediates critical period plasticity in the mouse primary visual cortex

Classical studies on the development of ocular dominance (OD) organization in primary visual cortex (V1) have revealed a postnatal critical period (CP), during which visual inputs between the two eyes are most effective in shaping cortical circuits through synaptic competition. A brief closure of one eye during CP caused a pronounced shift of response preference of V1 neurons toward the open eye, a form of CP plasticity in the developing V1. However, it remains unclear what particular property of binocular inputs during CP is responsible for mediating this experience-dependent OD plasticity. Using whole-cell recording in mouse V1, we found that visually driven synaptic inputs from the two eyes to binocular cells in layers 2/3 and 4 became highly coincident during CP. Enhancing cortical GABAergic transmission activity by brain infusion with diazepam not only caused a precocious onset of the high coincidence of binocular inputs and OD plasticity in pre-CP mice, but rescued both of them in dark-reared mice, suggesting a tight link between coincident binocular inputs and CP plasticity. In Thy1-ChR2 mice, chronic disruption of this binocular input coincidence during CP by asynchronous optogenetic activation of retinal ganglion cells abolished the OD plasticity. Computational simulation using a feed-forward network model further suggests that the coincident inputs could mediate this CP plasticity through a homeostatic synaptic learning mechanism with synaptic competition. These results suggest that the high-level correlation of binocular inputs is a hallmark of the CP of developing V1 and serves as neural substrate for the induction of OD plasticity.

Functional coupling from simple to complex cells in the visually driven cortical circuit

In the classic model of the primary visual cortex, upper-layer complex cells are driven by feedforward inputs from layer 4 simple cells. Based on spike cross-correlation, previous in vivo work has suggested that this connection is strong and dense, with a high probability of connection (50%) and significant strength in connected pairs. A much sparser projection has been found in brain slices, however, with the probability of layer 4 cells connecting to layer 2/3 cells being relatively low (10%). Here, we explore this connection in vivo in the cat primary visual cortex by recording simultaneously spikes of layer 4 simple cells and the membrane potential (V(m)) of layer 2/3 complex cells. By triggering the average of the complex cell's V(m) on the spikes of the simple cell (V(m)-STA), we found functional coupling to be very common during visual stimulation: the simple cell's spikes tended to occur near the troughs of the complex cell's V(m) fluctuations and were, on average, followed by a significant (~1 mV) fast-rising (10 ms) depolarization in the complex cell. In the absence of visual stimulation, however, when single simple cells were activated electrically through the recording electrode, no significant depolarization, or at most a very weak input (0.1-0.2 mV), was detected in the complex cell. We suggest that the functional coupling observed during visual stimulation arises from coordinated or nearly synchronous activity among a large population of simple cells, only a small fraction of which are presynaptic to the recorded complex cell.

Emergence of orientation selectivity in the Mammalian visual pathway

Orientation selectivity is a property of mammalian primary visual cortex (V1) neurons, yet its emergence along the visual pathway varies across species. In carnivores and primates, elongated receptive fields first appear in V1, whereas in lagomorphs such receptive fields emerge earlier, in the retina. Here we examine the mouse visual pathway and reveal the existence of orientation selectivity in lateral geniculate nucleus (LGN) relay cells. Cortical inactivation does not reduce this orientation selectivity, indicating that cortical feedback is not its source. Orientation selectivity is similar for LGN relay cells spiking and subthreshold input to V1 neurons, suggesting that cortical orientation selectivity is inherited from the LGN in mouse. In contrast, orientation selectivity of cat LGN relay cells is small relative to subthreshold inputs onto V1 simple cells. Together, these differences show that although orientation selectivity exists in visual neurons of both rodents and carnivores, its emergence along the visual pathway, and thus its underlying neuronal circuitry, is fundamentally different.

The unimodal distribution of sub-threshold, ongoing activity in cortical networks

The characterization of the subthreshold, ongoing activity in cortical neurons has been the focus of numerous studies. This activity, described as spontaneous slow waves in membrane potential, has been observed in a span of species in diverse cortical and subcortical areas. We here characterized membrane potential fluctuations in motor and the frontal association cortices cortical neurons of ketamine–xylazine anesthetized rats. We recorded from 95 neurons from a range of cortical depths to unravel the network and cellular mechanisms that shape the subthreshold ongoing spontaneous activity of these neurons. We define a unitary event that generates the subthreshold ongoing activity: giant synaptic potentials (GSPs). These events have a duration of 87 ± 50 ms and an amplitude of 19 ± 6.4 mV. They occur at a frequency of 3.7 ± 0.8 Hz and involve an increase in conductance change of 22 ± 21%. GSPs are mainly due to excitatory activity that occurs throughout all cortical layers, unaffected by the intrinsic properties of the cells. Indeed, blocking the GABA_A receptors, a procedure that had a profound effect on cortical activity, did not alter these unitary events. We propose that this unitary event is composed of individual, excitatory synaptic potentials that appear at different levels of synchrony and that the level of synchrony determines the shape of the subthreshold activity.

A spontaneous state of weakly correlated synaptic excitation and inhibition in visual cortex

Cortical spontaneous activity reflects an animal's behavioral state and affects neural responses to sensory stimuli. The correlation between excitatory and inhibitory synaptic input to single neurons is a key parameter in models of cortical circuitry. Recent measurements demonstrated highly correlated synaptic excitation and inhibition during spontaneous "up-and-down" states, during which excitation accounted for approximately 80% of inhibitory variance (Shu et al., 2003; Haider et al., 2006). Here we report in vivo whole-cell estimates of the correlation between excitation and inhibition in the rat visual cortex under pentobarbital anesthesia, during which up-and-down states are absent. Excitation and inhibition are weakly correlated, relative to the up-and-down state: excitation accounts for less than 40% of inhibitory variance. Although these correlations are lower than when the circuit cycles between up-and-down states, both behaviors may arise from the same circuitry. Our observations provide evidence that different correlational patterns of excitation and inhibition underlie different cortical states.

Up states are rare in awake auditory cortex

The dynamics of subthreshold membrane potential provide insight into the organization of activity in neural circuits. In many brain areas, membrane potential is bistable, transiting between a relatively hyperpolarized down state and a depolarized up state. These up and down states, which have been proposed to play a number of computational roles, have mainly been studied in anesthetized and in vitro preparations. Here, we have used intracellular recordings to characterize the dynamics of membrane potential in the auditory cortex of awake rats. We find that long up states are rare in the awake auditory cortex, with only 0.4% of up states >500 ms. Most neurons displayed only brief up states (bumps) and spent on average ∼1% of recording time in up states >500 ms. We suggest that the near absence of long up states in awake auditory cortex may reflect an adaptation to the rapid processing of auditory stimuli.

Inhibition shapes response selectivity in the inferior colliculus by gain modulation

Pharmacological block of inhibition is often used to determine if inhibition contributes to spike selectivity, in which a preferred stimulus evokes more spikes than a null stimulus. When inhibitory block reduces spike selectivity, a common interpretation is that differences between the preferred- and null-evoked inhibitions created the selectivity from less-selective excitatory inputs. In models based on empirical properties of cells from the inferior colliculus (IC) of awake bats, we show that inhibitory differences are not required. Instead, inhibition can enhance spike selectivity by changing the gain, the ratio of output spikes to input current. Within the model, we made preferred stimuli that evoked more spikes than null stimuli using five distinct synaptic mechanisms. In two cases, synaptic selectivity (the differences between the preferred and null inputs) was entirely excitatory, and in two it was entirely inhibitory. In each case, blocking inhibition eliminated spike selectivity. Thus, observing spike rates following inhibitory block did not distinguish among the cases where synaptic selectivity was entirely excitatory or inhibitory. We then did the same modeling experiment using empirical synaptic conductances derived from responses to preferred and null sounds. In most cases, inhibition in the model enhanced spike selectivity mainly by gain modulation and firing rate reduction. Sometimes, inhibition reduced the null gain to zero, eliminating null-evoked spikes. In some cases, inhibition increased the preferred gain more than the null gain, enhancing the difference between the preferred- and null-evoked spikes. Finally, inhibition kept firing rates low. When selectivity is quantified by the selectivity index (SI, the ratio of the difference to the sum of the spikes evoked by the preferred and null stimuli), inhibitory block reduced the SI by increasing overall firing rates. These results are consistent with inhibition shaping spike selectivity by gain control.

A canonical model of multistability and scale-invariance in biological systems

Multistability and scale-invariant fluctuations occur in a wide variety of biological organisms from bacteria to humans as well as financial, chemical and complex physical systems. Multistability refers to noise driven switches between multiple weakly stable states. Scale-invariant fluctuations arise when there is an approximately constant ratio between the mean and standard deviation of a system's fluctuations. Both are an important property of human perception, movement, decision making and computation and they occur together in the human alpha rhythm, imparting it with complex dynamical behavior. Here, we elucidate their fundamental dynamical mechanisms in a canonical model of nonlinear bifurcations under stochastic fluctuations. We find that the co-occurrence of multistability and scale-invariant fluctuations mandates two important dynamical properties: Multistability arises in the presence of a subcritical Hopf bifurcation, which generates co-existing attractors, whilst the introduction of multiplicative (state-dependent) noise ensures that as the system jumps between these attractors, fluctuations remain in constant proportion to their mean and their temporal statistics become long-tailed. The simple algebraic construction of this model affords a systematic analysis of the contribution of stochastic and nonlinear processes to cortical rhythms, complementing a recently proposed biophysical model. Similar dynamics also occur in a kinetic model of gene regulation, suggesting universality across a broad class of biological phenomena.

Biological systems are able to adapt to rapidly and widely changing environments. Many biological organisms employ two distinct mechanisms that improve their survival in these circumstances: Firstly they exhibit rapid, qualitative changes in their internal dynamics; secondly they possess the ability to respond to change that is not absolute, but scales in proportion to the underlying intensity of the environment. In this paper, we study a simple class of noisy, dynamical systems that mathematically represent a very broad range of more complex models. We hence show how a combination of nonlinear instabilities and state-dependent noise in this model is able to unify these two apparently distinct biological phenomena. To illustrate its unifying potential, this simple model is applied to two very distinct biological processes – the spontaneous activity of the human cortex (i.e. when subjects are at rest), and genetic regulation in a bacteriophage. We also provide proof of principle that our model can be inverted from empirical data, allowing estimation of the parameters that express the nonlinear and stochastic influences at play in the underlying system.

Enhancement of asynchronous release from fast-spiking interneuron in human and rat epileptic neocortex

Down-regulation of GABAergic inhibition may result in the generation of epileptiform activities. Besides spike-triggered synchronous GABA release, changes in asynchronous release (AR) following high-frequency discharges may further regulate epileptiform activities. In brain slices obtained from surgically removed human neocortical tissues of patients with intractable epilepsy and brain tumor, we found that AR occurred at GABAergic output synapses of fast-spiking (FS) neurons and its strength depended on the type of connections, with FS autapses showing the strongest AR. In addition, we found that AR depended on residual Ca²⁺ at presynaptic terminals but was independent of postsynaptic firing. Furthermore, AR at FS autapses was markedly elevated in human epileptic tissue as compared to non-epileptic tissue. In a rat model of epilepsy, we found similar elevation of AR at both FS autapses and synapses onto excitatory neurons. Further experiments and analysis showed that AR elevation in epileptic tissue may result from an increase in action potential amplitude in the FS neurons and elevation of residual Ca²⁺ concentration. Together, these results revealed that GABAergic AR occurred at both human and rat neocortex, and its elevation in epileptic tissue may contribute to the regulation of epileptiform activities.

The mechanism of orientation selectivity in primary visual cortex without a functional map

Neurons in primary visual cortex (V1) display substantial orientation selectivity even in species where V1 lacks an orientation map, such as in mice and rats. The mechanism underlying orientation selectivity in V1 with such a salt-and-pepper organization is unknown; it is unclear whether a connectivity that depends on feature similarity is required, or a random connectivity suffices. Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations. We show that even though the total feedforward and total recurrent excitatory and inhibitory inputs all have a very weak orientation selectivity, strong selectivity emerges in the neuronal spike responses if the network operates in the balanced excitation/inhibition regime. This is because in this regime the (large) untuned components in the excitatory and inhibitory contributions approximately cancel. As a result the untuned part of the input into a neuron as well as its modulation with orientation and time all have a size comparable to the neuronal threshold. However, the tuning of the F0 and F1 components of the input are uncorrelated and the high-frequency fluctuations are not tuned. This is reflected in the subthreshold voltage response. Remarkably, due to the nonlinear voltage-firing rate transfer function, the preferred orientation of the F0 and F1 components of the spike response are highly correlated.

Nonlinear-dynamics theory of up-down transitions in neocortical neural networks

The neurons of the neocortex show ~1-Hz synchronized transitions between an active up state and a quiescent down state. The up-down state transitions are highly coherent over large sections of the cortex, yet they are accompanied by pronounced, incoherent noise. We propose a simple model for the up-down state oscillations that allows analysis by straightforward dynamical systems theory. An essential feature is a nonuniform network geometry composed of groups of excitatory and inhibitory neurons with strong coupling inside a group and weak coupling between groups. The enhanced deterministic noise of the up state appears as the natural result of the proximity of a partial synchronization transition. The synchronization transition takes place as a function of the long-range synaptic strength linking different groups of neurons.

Modeling fast stimulus-response association learning along the occipito-parieto-frontal pathway following rule instructions

On the basis of instructions, humans are able to set up associations between sensory and motor areas of the brain separated by several neuronal relays, within a few seconds. This paper proposes a model of fast learning along the dorsal pathway, from primary visual areas to pre-motor cortex. A new synaptic learning rule is proposed where synaptic efficacies converge rapidly toward a specific value determined by the number of active inputs of a neuron, respecting a principle of resource limitation in terms of total synaptic input efficacy available to a neuron. The efficacies are stable with regards to repeated arrival of spikes in a spike train. This rule reproduces the inverse relationship between initial and final synaptic efficacy observed in long-term potentiation (LTP) experiments. Simulations of learning experiments are conducted in a multilayer network of leaky integrate-and-fire (LIF) spiking neuron models. It is proposed that cortical feedback connections convey a top-down learning-enabling signal that guides bottom-up learning in "hidden" neurons that are not directly exposed to input or output activity. Simulations of repeated presentation of the same stimulus-response pair, show that, under conditions of fast learning with probabilistic synaptic transmission, the networks tend to recruit a new sub-network at each presentation to represent the association, rather than re-using a previously trained one. This increasing allocation of neural resources results in progressively shorter execution times, in line with experimentally observed reduction in response time with practice. This article is part of a Special Issue entitled: Neural Coding.

Explicit-duration hidden Markov model inference of UP-DOWN states from continuous signals

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.