Relating neuronal firing patterns to functional differentiation of cerebral cortex

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary-rather than additive-effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

Dopamine Release Dynamics in the Nucleus Accumbens Are Modulated by the Timing of Electrical Stimulation Pulses When Applied to the Medial Forebrain Bundle and Medial Prefrontal Cortex

Electrical brain stimulation has been used in vivo and in vitro to investigate neural circuitry. Historically, stimulation parameters such as amplitude, frequency, and pulse width were varied to investigate their effects on neurotransmitter release and behavior. These experiments have traditionally employed fixed-frequency stimulation patterns, but it has previously been found that neurons are more precisely tuned to variable input. Introducing variability into the interpulse interval of stimulation pulses will inform on how dopaminergic release can be modulated by variability in pulse timing. Here, dopaminergic release in rats is monitored in the nucleus accumbens (NAc), a key dopaminergic center which plays a role in learning and motivation, by fast-scan cyclic voltammetry. Dopaminergic release in the NAc could also be modulated by stimulation region due to differences in connectivity. We targeted two regions for stimulation─the medial forebrain bundle (MFB) and the medial prefrontal cortex (mPFC)─due to their involvement in reward processing and projections to the NAc. Our goal is to investigate how variable interpulse interval stimulation patterns delivered to these regions affect the time course of dopamine release in the NAc. We found that stimulating the MFB with these variable stimulation patterns saw a highly responsive, frequency-driven dopaminergic response. In contrast, variable stimulation patterns applied to the mPFC were not as sensitive to the variable frequency changes. This work will help inform on how stimulation patterns can be tuned specifically to the stimulation region to improve the efficiency of electrical stimulation and control dopamine release.

Oscillatory Waveform Shape and Temporal Spike Correlations Differ across Bat Frontal and Auditory Cortex

Neural oscillations are associated with diverse computations in the mammalian brain. The waveform shape of oscillatory activity measured in the cortex relates to local physiology and can be informative about aberrant or dynamically changing states. However, how waveform shape differs across distant yet functionally and anatomically related cortical regions is largely unknown. In this study, we capitalize on simultaneous recordings of local field potentials (LFPs) in the auditory and frontal cortices of awake, male Carollia perspicillata bats to examine, on a cycle-by-cycle basis, waveform shape differences across cortical regions. We find that waveform shape differs markedly in the fronto-auditory circuit even for temporally correlated rhythmic activity in comparable frequency ranges (i.e., in the delta and gamma bands) during spontaneous activity. In addition, we report consistent differences between areas in the variability of waveform shape across individual cycles. A conceptual model predicts higher spike-spike and spike-LFP correlations in regions with more asymmetric shapes, a phenomenon that was observed in the data: spike-spike and spike-LFP correlations were higher in the frontal cortex. The model suggests a relationship between waveform shape differences and differences in spike correlations across cortical areas. Altogether, these results indicate that oscillatory activity in the frontal and auditory cortex possesses distinct dynamics related to the anatomical and functional diversity of the fronto-auditory circuit.

The Geometry of Low- and High-Level Perceptual Spaces

Low-level features are typically continuous (e.g., the gamut between two colors), but semantic information is often categorical (there is no corresponding gradient between dog and turtle) and hierarchical (animals live in land, water, or air). To determine the impact of these differences on cognitive representations, we characterized the geometry of perceptual spaces of five domains: a domain dominated by semantic information (animal names presented as words), a domain dominated by low-level features (colored textures), and three intermediate domains (animal images, lightly texturized animal images that were easy to recognize, and heavily texturized animal images that were difficult to recognize). Each domain had 37 stimuli derived from the same animal names. From 13 participants (9F), we gathered similarity judgments in each domain via an efficient psychophysical ranking paradigm. We then built geometric models of each domain for each participant, in which distances between stimuli accounted for participants' similarity judgments and intrinsic uncertainty. Remarkably, the five domains had similar global properties: each required 5-7 dimensions, and a modest amount of spherical curvature provided the best fit. However, the arrangement of the stimuli within these embeddings depended on the level of semantic information: dendrograms derived from semantic domains (word, image, and lightly texturized images) were more "tree-like" than those from feature-dominated domains (heavily texturized images and textures). Thus, the perceptual spaces of domains along this feature-dominated to semantic-dominated gradient shift to a tree-like organization when semantic information dominates, while retaining a similar global geometry.

The neuronal implementation of representational geometry in primate prefrontal cortex

Modern neuroscience has seen the rise of a population-doctrine that represents cognitive variables using geometrical structures in activity space. Representational geometry does not, however, account for how individual neurons implement these representations. Leveraging the principle of sparse coding, we present a framework to dissect representational geometry into biologically interpretable components that retain links to single neurons. Applied to extracellular recordings from the primate prefrontal cortex in a working memory task with interference, the identified components revealed disentangled and sequential memory representations including the recovery of memory content after distraction, signals hidden to conventional analyses. Each component was contributed by small subpopulations of neurons with distinct spiking properties and response dynamics. Modeling showed that such sparse implementations are supported by recurrently connected circuits as in prefrontal cortex. The perspective of neuronal implementation links representational geometries to their cellular constituents, providing mechanistic insights into how neural systems encode and process information.

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

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

Hamilton energy balance and synchronization behaviors of two functional neurons

The nervous system is composed of various functional neurons, some of which perceive sound or light, and these physical signals can be converted into bioelectrical signals. From the biophysical point of view, piezoelectric ceramic embedded in neuronal circuits can detect the external auditory waves, while phototube can capture light signals, so as to obtain two functional neurons with auditory recognition and light-dependent recognition. Considering the two identical or different functional neurons are connected by an induction coil to stimulate magnetic field coupling, and there will be energy diversity when they are driven by different initial conditions or external stimulation. Thus, synaptic connections can be activated and awakened in an adaptive manner when field energy is exchanged, and the coupling channel remains open until the energy diversity between neurons is controlled at a limited threshold. For this purpose, a criterion of the coupling strength increases exponentially is proposed to discuss the enhancement of neuronal synaptic connections. It is found that two neurons can be coupled adaptively to achieve complete synchronization, quasi-synchronization or intermittent quasi-synchronization. These results could help in designing functional assistive devices for patients with hearing or vision impairment.

Identification of interacting neural populations: methods and statistical considerations

As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.

Logarithmically scaled, gamma distributed neuronal spiking

Naturally log-scaled quantities abound in the nervous system. Distributions of these quantities have non-intuitive properties, which have implications for data analysis and the understanding of neural circuits. Here, we review the log-scaled statistics of neuronal spiking and the relevant analytical probability distributions. Recent work using log-scaling revealed that interspike intervals of forebrain neurons segregate into discrete modes reflecting spiking at different timescales and are each well-approximated by a gamma distribution. Each neuron spends most of the time in an irregular spiking 'ground state' with the longest intervals, which determines the mean firing rate of the neuron. Across the entire neuronal population, firing rates are log-scaled and well approximated by the gamma distribution, with a small number of highly active neurons and an overabundance of low rate neurons (the 'dark matter'). These results are intricately linked to a heterogeneous balanced operating regime, which confers upon neuronal circuits multiple computational advantages and has evolutionarily ancient origins.

Transcriptomic cell type structures in vivo neuronal activity across multiple timescales

Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli.

Piecewise quadratic neuron model: A tool for close-to-biology spiking neuronal network simulation on dedicated hardware

Spiking neuron models simulate neuronal activities and allow us to analyze and reproduce the information processing of the nervous system. However, ionic-conductance models, which can faithfully reproduce neuronal activities, require a huge computational cost, while integral-firing models, which are computationally inexpensive, have some difficulties in reproducing neuronal activities. Here we propose a Piecewise Quadratic Neuron (PQN) model based on a qualitative modeling approach that aims to reproduce only the key dynamics behind neuronal activities. We demonstrate that PQN models can accurately reproduce the responses of ionic-conductance models of major neuronal classes to stimulus inputs of various magnitudes. In addition, the PQN model is designed to support the efficient implementation on digital arithmetic circuits for use as silicon neurons, and we confirm that the PQN model consumes much fewer circuit resources than the ionic-conductance models. This model intends to serve as a tool for building a large-scale closer-to-biology spiking neural network.

17β-estradiol does not have a direct effect on the function of striatal cholinergic interneurons in adult mice in vitro

The striatum is an essential component of the basal ganglia that is involved in motor control, action selection and motor learning. The pathophysiological changes of the striatum are present in several neurological and psychiatric disorder including Parkinson's and Huntington's diseases. The striatal cholinergic neurons are the main regulators of striatal microcircuitry. It has been demonstrated that estrogen exerts various effects on neuronal functions in dopaminergic and medium spiny neurons (MSN), however little is known about how the activity of cholinergic interneurons are influenced by estrogens. In this study we examined the acute effect of 17β-estradiol on the function of striatal cholinergic neurons in adult mice in vitro. We also tested the effect of estrus cycle and sex on the spontaneous activity of cholinergic interneurons in the striatum. Our RNAscope experiments showed that ERα, ERβ, and GPER1 receptor mRNAs are expressed in some striatal cholinergic neurons at a very low level. In cell-attached patch clamp experiments, we found that a high dose of 17β-estradiol (100 nM) affected the spontaneous firing rate of these neurons only in old males. Our findings did not demonstrate any acute effect of a low concentration of 17β-estradiol (100 pM) or show any association of estrus cycle or sex with the activity of striatal cholinergic neurons. Although estrogen did not induce changes in the intrinsic properties of neurons, indirect effects via modulation of the synaptic inputs of striatal cholinergic interneurons cannot be excluded.

Large coefficient of variation of inter-spike intervals induced by noise current in the resonate-and-fire model neuron

Neuronal spike variability is a statistical property associated with the noise environment. Considering a linearised Hodgkin-Huxley model, we investigate how large spike variability can be induced in a typical stellate cell when submitted to constant and noise current amplitudes. For low noise current, we observe only periodic firing (active) or silence activities. For intermediate noise values, in addition to only active or inactive periods, we also identify a single transition from an initial spike-train (active) to silence dynamics over time, where the spike variability is low. However, for high noise current, we find intermittent active and silence periods with different values. The spike intervals during active and silent states follow the exponential distribution, which is similar to the Poisson process. For non-maximal noise current, we observe the highest values of inter-spike variability. Our results suggest sub-threshold oscillations as a possible mechanism for the appearance of high spike variability in a single neuron due to noise currents.

Influence of data sampling methods on the representation of neural spiking activity in vivo

In vivo single-unit recordings distinguish the basal spiking properties of neurons in different experimental settings and disease states. Here, we examined over 300 spike trains recorded from Purkinje cells and cerebellar nuclei neurons to test whether data sampling approaches influence the extraction of rich descriptors of firing properties. Our analyses included neurons recorded in awake and anesthetized control mice, and disease models of ataxia, dystonia, and tremor. We find that recording duration circumscribes overall representations of firing rate and pattern. Notably, shorter recording durations skew estimates for global firing rate variability toward lower values. We also find that only some populations of neurons in the same mouse are more similar to each other than to neurons recorded in different mice. These data reveal that recording duration and approach are primary considerations when interpreting task-independent single neuron firing properties. If not accounted for, group differences may be concealed or exaggerated.

Detection and categorization of severe cardiac disorders based solely on heart period measurements

Cardiac disorders are common conditions associated with a high mortality rate. Due to their potential for causing serious symptoms, it is desirable to constantly monitor cardiac status using an accessible device such as a smartwatch. While electrocardiograms (ECGs) can make the detailed diagnosis of cardiac disorders, the examination is typically performed only once a year for each individual during health checkups, and it requires expert medical practitioners to make comprehensive judgments. Here we describe a newly developed automated system for alerting individuals about cardiac disorders solely by measuring a series of heart periods. For this purpose, we examined two metrics of heart rate variability (HRV) and analyzed 1-day ECG recordings of more than 1,000 subjects in total. We found that a metric of local variation was more efficient than conventional HRV metrics for alerting cardiac disorders, and furthermore, that a newly introduced metric of local-global variation resulted in superior capacity for discriminating between premature contraction and atrial fibrillation. Even with a 1-minute recording of heart periods, our new detection system had a diagnostic performance even better than that of the conventional analysis method applied to a 1-day recording.

The impact of closed-loop intracortical stimulation on neural activity in brain-injured, anesthetized animals

BACKGROUND

Acquired brain injuries, such as stroke, are a major cause of long-term disability worldwide. Intracortical microstimulation (ICMS) can be used successfully to assist in guiding appropriate connections to restore lost sensorimotor integration. Activity-Dependent Stimulation (ADS) is a specific type of closed-loop ICMS that aims at coupling the activity of two different brain regions by stimulating one in response to activity in the other. Recently, ADS was used to effectively promote behavioral recovery in rodent models following a unilateral traumatic brain injury in the primary motor cortex. While behavioral benefits have been described, the neurophysiological changes in spared areas in response to this type of stimulation have not been fully characterized. Here we explored how single-unit spiking activity is impacted by a focal ischemic lesion and, subsequently, by an ADS treatment.

METHODS

Intracortical microelectrode arrays were implanted in the ipsilesional rostral forelimb area (RFA) to record spike activity and to trigger intracortical microstimulation in the primary somatosensory area (S1) of anaesthetized Long Evans rats. An ischemic injury was induced in the caudal forelimb area through microinjections of Endothelin-1. Activity from both RFA and S1 was recorded and analyzed off-line by evaluating possible changes, either induced by the lesion in the Control group or by stimulation in the ADS group.

RESULTS

We found that the ischemic lesion in the motor area led to an overall increase in spike activity within RFA and a decrease in S1 with respect to the baseline condition. Subsequent treatment with ADS increased the firing rate in both RFA and S1. Post-stimulation spiking activity was significantly higher compared to pre-stimulation activity in the ADS animals versus non-stimulated controls. Moreover, stimulation promoted the generation of highly synchronized bursting patterns in both RFA and S1 only in the ADS group.

CONCLUSIONS

This study describes the impact on single-unit activity in ipsilesional areas immediately following a cortical infarct and demonstrates that application of ADS is effective in altering this activity.

Cell type-specific excitability probed by optogenetic stimulation depends on the phase of the alpha oscillation

BACKGROUND

Alpha oscillations have been proposed to provide phasic inhibition in the brain. Yet, pinging alpha oscillations with transcranial magnetic stimulation (TMS) to examine phase-dependent network excitability has resulted in conflicting findings. At the cellular level, such gating by the alpha oscillation remains poorly understood.

OBJECTIVE

We examine how the excitability of pyramidal cells and presumed fast-spiking inhibitory interneurons depends on the phase of the alpha oscillation.

METHODS

Optogenetic stimulation pulses were administered at random phases of the alpha oscillation in the posterior parietal cortex (PPC) of two adult ferrets that expressed channelrhodopsin in pyramidal cells. Post-stimulation firing probability was calculated as a function of the stimulation phase of the alpha oscillation for both verum and sham stimulation.

RESULTS

The excitability of pyramidal cells depended on the alpha phase, in anticorrelation with their intrinsic phase preference; pyramidal cells were more responsive to optogenetic stimulation at the alpha phase with intrinsically low firing rates. In contrast, presumed fast-spiking inhibitory interneurons did not show such a phase dependency despite their stronger intrinsic phase preference.

CONCLUSIONS

Alpha oscillations gate input to PPC in a phase-dependent manner such that low intrinsic activity was associated with higher responsiveness to input. This finding supports a model of cortical oscillation, in which internal processing and communication are limited to the depolarized half-cycle, whereas the other half-cycle serves as a signal detector for unexpected input. The functional role of different parts of the alpha cycle may vary across the cortex depending on local neuronal firing properties.

Susceptibility to express amphetamine locomotor sensitization correlates with dorsolateral striatum bursting activity and GABAergic synapses in the globus pallidus

Repeated psychostimulant administration results in behavioral sensitization, a process that is relevant in the early phases of drug addiction. Critically, behavioral sensitization is not observed in all subjects. Evidence shows that differential neuronal activity in the dorsolateral striatum (DLS) accompanies the expression of amphetamine (AMPH) locomotor sensitization. However, whether individual differences in DLS activity previous to AMPH administration can predict the expression of locomotor sensitization has not been assessed. Here, we examined DLS neuronal activity before and after repeated AMPH administration and related it to the susceptibility of rats to sensitize. For that, single-unit recordings on DLS medium spiny neurons (MSNs) were carried out in freely moving male Sprague Dawley rats during repeated AMPH administration. We also examined differences in neurostructure that could accompany sensitization. We quantified the density of the inhibitory postsynaptic marker gephyrin (Geph) in the entopeduncular nucleus (EP) and globus pallidus (GP). A higher burst firing and a lower percentage of correlation between MSNs post-Saline firing rate vs. locomotion predicted the expression of locomotor sensitization. Moreover, during the AMPH challenge, we observed that burst firing decreased in sensitized rats, in contrast to non-sensitized rats in which burst firing was maintained. Finally, a higher Geph density on GP but not EP was observed in non-sensitized rats after AMPH challenge. These results indicate that initial differences in DLS burst firing might underlie the susceptibility to express locomotor sensitization and suggest that the potentiation of dorsal striatum indirect pathway could be considered a protective mechanism to locomotor sensitization.

Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions

The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because interareal communication has been reported both during specific sleep stages and in awake, behaving animals, these brain regions can serve as an archetype to establish that measuring functional interactions is important for comprehending neural systems. To this end, we analyze here a public dataset of local field potentials (LFPs) recorded in rats simultaneously from the hippocampus and amygdala during different behaviors. Employing a specific, time-lagged embedding technique, named topological causality (TC), we infer directed interactions between the LFP band powers of the two regions across six frequency bands in a time-resolved manner. The combined power and interaction signals are processed with our own unsupervised tools developed originally for the analysis of molecular dynamics simulations to effectively visualize and identify putative, neural states that are visited by the animals repeatedly. Our proposed methodology minimizes impositions onto the data, such as isolating specific epochs, or averaging across externally annotated behavioral stages, and succeeds in separating internal states by external labels such as sleep or stimulus events. We show that this works better for two of the three rats we analyzed, and highlight the need to acknowledge individuality in analyses of this type. Importantly, we demonstrate that the quantification of functional interactions is a significant factor in discriminating these external labels, and we suggest our methodology as a general tool for large, multisite recordings.

Trial-to-Trial Variability of Spiking Delay Activity in Prefrontal Cortex Constrains Burst-Coding Models of Working Memory

A hallmark neuronal correlate of working memory (WM) is stimulus-selective spiking activity of neurons in PFC during mnemonic delays. These observations have motivated an influential computational modeling framework in which WM is supported by persistent activity. Recently, this framework has been challenged by arguments that observed persistent activity may be an artifact of trial-averaging, which potentially masks high variability of delay activity at the single-trial level. In an alternative scenario, WM delay activity could be encoded in bursts of selective neuronal firing which occur intermittently across trials. However, this alternative proposal has not been tested on single-neuron spike-train data. Here, we developed a framework for addressing this issue by characterizing the trial-to-trial variability of neuronal spiking quantified by Fano factor (FF). By building a doubly stochastic Poisson spiking model, we first demonstrated that the burst-coding proposal implies a significant increase in FF positively correlated with firing rate, and thus loss of stability across trials during the delay. Simulation of spiking cortical circuit WM models further confirmed that FF is a sensitive measure that can well dissociate distinct WM mechanisms. We then tested these predictions on datasets of single-neuron recordings from macaque PFC during three WM tasks. In sharp contrast to the burst-coding model predictions, we only found a small fraction of neurons showing increased WM-dependent burstiness, and stability across trials during delay was strengthened in empirical data. Therefore, reduced trial-to-trial variability during delay provides strong constraints on the contribution of single-neuron intermittent bursting to WM maintenance.SIGNIFICANCE STATEMENT There are diverging classes of theoretical models explaining how information is maintained in working memory by cortical circuits. In an influential model class, neurons exhibit persistent elevated memorandum-selective firing, whereas a recently developed class of burst-coding models suggests that persistent activity is an artifact of trial-averaging, and spiking is sparse in each single trial, subserved by brief intermittent bursts. However, this alternative picture has not been characterized or tested on empirical spike-train data. Here we combine mathematical analysis, computational model simulation, and experimental data analysis to test empirically these two classes of models and show that the trial-to-trial variability of empirical spike trains is not consistent with burst coding. These findings provide constraints for theoretical models of working memory.

Detection of Mutual Exciting Structure in Stock Price Trend Dynamics

We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. We first performed statistical measurements for the crossover event sequence, introducing the distribution of the inter-event times of dual moving average crossovers and the correlations of local variation (LV), which is often used in spike train analysis. It was demonstrated that the crossover dynamics in most stock sectors are generally more regular than a standard Poisson process, and the correlation between variations is ubiquitous. In this sense, the proposed model allowed us to identify some asymmetric cross-excitations, and a mutually exciting structure of stock sectors could be characterized by mutual excitation correlations obtained from the kernel matrix of our model. Using simulations, we were able to substantiate that a burst of the dual moving average crossovers in one sector increases the intensity of burst both in the same sector (self-excitation) as well as in other sectors (cross-excitation), generating episodes of highly clustered burst across the market. Furthermore, based on our finding, an algorithmic pair trading strategy was developed and backtesting results on real market data showed that the mutual excitation mechanism might be profitable for stock trading.

Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states

Gamma oscillations are widely seen in the awake and sleeping cerebral cortex, but the exact role of these oscillations is still debated. Here, we used biophysical models to examine how Gamma oscillations may participate to the processing of afferent stimuli. We constructed conductance-based network models of Gamma oscillations, based on different cell types found in cerebral cortex. The models were adjusted to extracellular unit recordings in humans, where Gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which Gamma is generated by interneuron networks (ING) and third, a mechanism which relies on Gamma oscillations generated by pacemaker chattering neurons (CHING). We find that all three mechanisms generate features consistent with human recordings, but that the ING mechanism is most consistent with the firing rate change inside Gamma bursts seen in the human data. We next evaluated the responsiveness and resonant properties of these networks, contrasting Gamma oscillations with the asynchronous mode. We find that for both slowly-varying stimuli and precisely-timed stimuli, the responsiveness is generally lower during Gamma compared to asynchronous states, while resonant properties are similar around the Gamma band. We could not find conditions where Gamma oscillations were more responsive. We therefore predict that asynchronous states provide the highest responsiveness to external stimuli, while Gamma oscillations tend to overall diminish responsiveness.

In the awake and attentive brain, the activity of neurons is typically asynchronous and irregular. It also occasionally displays oscillations in the Gamma frequency range (30–90 Hz), which are believed to be involved in information processing. Here, we use computational models to investigate how brain circuits generate oscillations in a manner consistent with microelectrode recordings in humans. We then study how these networks respond to external input, comparing asynchronous and oscillatory states. This is tested according to several paradigms, an integrative mode, where slowly varying inputs are progressively integrated, a coincidence detection mode, where brief inputs are processed according to the phase of the oscillations, and a resonance mode where the network is probed with oscillatory inputs. Surprisingly, we find that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness to external inputs. We discuss possible implications of this responsiveness decrease on information processing and propose new directions for further exploration.

The brain and its time: intrinsic neural timescales are key for input processing

We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

Membrane Resonance in Pyramidal and GABAergic Neurons of the Mouse Perirhinal Cortex

The perirhinal cortex (PRC) is a polymodal associative region of the temporal lobe that works as a gateway between cortical areas and hippocampus. In recent years, an increasing interest arose in the role played by the PRC in learning and memory processes, such as object recognition memory, in contrast with certain forms of hippocampus-dependent spatial and episodic memory. The integrative properties of the PRC should provide all necessary resources to select and enhance the information to be propagated to and from the hippocampus. Among these properties, we explore in this paper the ability of the PRC neurons to amplify the output voltage to current input at selected frequencies, known as membrane resonance. Within cerebral circuits the resonance of a neuron operates as a filter toward inputs signals at certain frequencies to coordinate network activity in the brain by affecting the rate of neuronal firing and the precision of spike timing. Furthermore, the ability of the PRC neurons to resonate could have a fundamental role in generating subthreshold oscillations and in the selection of cortical inputs directed to the hippocampus. Here, performing whole-cell patch-clamp recordings from perirhinal pyramidal neurons and GABAergic interneurons of GAD67-GFP⁺ mice, we found, for the first time, that the majority of PRC neurons are resonant at their resting potential, with a resonance frequency of 0.5–1.5 Hz at 23°C and of 1.5–2.8 Hz at 36°C. In the presence of ZD7288 (blocker of HCN channels) resonance was abolished in both pyramidal neurons and interneurons, suggesting that I_h current is critically involved in resonance generation. Otherwise, application of TTx (voltage-dependent Na⁺ channel blocker) attenuates the resonance in pyramidal neurons but not in interneurons, suggesting that only in pyramidal neurons the persistent sodium current has an amplifying effect. These experimental results have also been confirmed by a computational model. From a functional point of view, the resonance in the PRC would affect the reverberating activity between neocortex and hippocampus, especially during slow wave sleep, and could be involved in the redistribution and strengthening of memory representation in cortical regions.

Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques

Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.

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.

From Univariate to Multivariate Coupling Between Continuous Signals and Point Processes: A Mathematical Framework

Time series data sets often contain heterogeneous signals, composed of both continuously changing quantities and discretely occurring events. The coupling between these measurements may provide insights into key underlying mechanisms of the systems under study. To better extract this information, we investigate the asymptotic statistical properties of coupling measures between continuous signals and point processes. We first introduce martingale stochastic integration theory as a mathematical model for a family of statistical quantities that include the phase locking value, a classical coupling measure to characterize complex dynamics. Based on the martingale central limit theorem, we can then derive the asymptotic gaussian distribution of estimates of such coupling measure that can be exploited for statistical testing. Second, based on multivariate extensions of this result and random matrix theory, we establish a principled way to analyze the low-rank coupling between a large number of point processes and continuous signals. For a null hypothesis of no coupling, we establish sufficient conditions for the empirical distribution of squared singular values of the matrix to converge, as the number of measured signals increases, to the well-known Marchenko-Pastur (MP) law, and the largest squared singular value converges to the upper end of the MP support. This justifies a simple thresholding approach to assess the significance of multivariate coupling. Finally, we illustrate with simulations the relevance of our univariate and multivariate results in the context of neural time series, addressing how to reliably quantify the interplay between multichannel local field potential signals and the spiking activity of a large population of neurons.

Engineering in vitro human neural tissue analogs by 3D bioprinting and electrostimulation

There is a fundamental need for clinically relevant, reproducible, and standardized in vitro human neural tissue models, not least of all to study heterogenic and complex human-specific neurological (such as neuropsychiatric) disorders. Construction of three-dimensional (3D) bioprinted neural tissues from native human-derived stem cells (e.g., neural stem cells) and human pluripotent stem cells (e.g., induced pluripotent) in particular is appreciably impacting research and conceivably clinical translation. Given the ability to artificially and favorably regulate a cell's survival and behavior by manipulating its biophysical environment, careful consideration of the printing technique, supporting biomaterial and specific exogenously delivered stimuli, is both required and advantageous. By doing so, there exists an opportunity, more than ever before, to engineer advanced and precise tissue analogs that closely recapitulate the morphological and functional elements of natural tissues (healthy or diseased). Importantly, the application of electrical stimulation as a method of enhancing printed tissue development in vitro, including neuritogenesis, synaptogenesis, and cellular maturation, has the added advantage of modeling both traditional and new stimulation platforms, toward improved understanding of efficacy and innovative electroceutical development and application.

Input of sensory, limbic, basal ganglia and pallial/cortical information into the ventral/lateral habenula: Functional principles in anuran amphibians

The habenula - a phylogenetically old brain structure present in all vertebrates - is involved in pain processing, reproductive behaviors, sleep-wake cycles, stress responses, reward, and learning. We performed intra- and extracellular recordings of ventral habenula (VHb) neurons in the isolated brain of anurans and revealed similar cell and response properties to those reported for the lateral habenula of mammals. We identified tonic regular, tonic irregular, rhythmic firing, and silent VHb neurons. Transitions between these firing patterns were observed during spontaneous activity. Electrical stimulation of various brain areas demonstrated VHb input of auditory, optic, limbic, basal ganglia, and pallial information. This resulted in three different response behaviors in VHb neurons: excitation, inhibition, or alternating facilitation and suppression of neuronal activity. Spontaneously changing activity patterns were observed to modulate, reset, or suppress the response behavior of VHb neurons, indicating a gating mechanism. This could be a network status or context dependent selection mechanism for which information are transmitted to task relevant brain areas (i.e., sensory system, limbic system, basal ganglia). Furthermore, alternating facilitation and suppression sequences upon auditory nerve stimulation correlated positively fictive motor activities recorded via the compound potential of the vagal nerve. Stimulation of the auditory nerve or the habenula led to facilitation, suppression, or alternating facilitation and suppression of neuronal activity in putative dopaminergic neurons. Due to complex habenula feedback loops with basal ganglia, limbic, and sensory systems, the habenula involvement in a variety of functions might therefore be explained by a modulatory effect on a task-relevant input stream.

Collective dynamics in the presence of finite-width pulses

The idealization of neuronal pulses as δ-spikes is a convenient approach in neuroscience but can sometimes lead to erroneous conclusions. We investigate the effect of a finite pulse width on the dynamics of balanced neuronal networks. In particular, we study two populations of identical excitatory and inhibitory neurons in a random network of phase oscillators coupled through exponential pulses with different widths. We consider three coupling functions inspired by leaky integrate-and-fire neurons with delay and type I phase-response curves. By exploring the role of the pulse widths for different coupling strengths, we find a robust collective irregular dynamics, which collapses onto a fully synchronous regime if the inhibitory pulses are sufficiently wider than the excitatory ones. The transition to synchrony is accompanied by hysteretic phenomena (i.e., the co-existence of collective irregular and synchronous dynamics). Our numerical results are supported by a detailed scaling and stability analysis of the fully synchronous solution. A conjectured first-order phase transition emerging for δ-spikes is smoothed out for finite-width pulses.

Larger GPU-accelerated brain simulations with procedural connectivity

Simulations are an important tool for investigating brain function but large models are needed to faithfully reproduce the statistics and dynamics of brain activity. Simulating large spiking neural network models has, until now, needed so much memory for storing synaptic connections that it required high performance computer systems. Here, we present an alternative simulation method we call 'procedural connectivity' where connectivity and synaptic weights are generated 'on the fly' instead of stored and retrieved from memory. This method is particularly well suited for use on graphical processing units (GPUs)-which are a common fixture in many workstations. Using procedural connectivity and an additional GPU code generation optimization, we can simulate a recent model of the macaque visual cortex with 4.13 × 106 neurons and 24.2 × 109 synapses on a single GPU-a significant step forward in making large-scale brain modeling accessible to more researchers.

Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation

Alongside stereotactic magnetic resonance imaging, microelectrode recording (MER) is frequently used during the deep brain stimulation (DBS) surgery for optimal target localization. The aim of this study is to optimize subthalamic nucleus (STN) mapping using MER analytical patterns. 16 patients underwent bilateral STN-DBS. MER was performed simultaneously for 5 microelectrodes in a setting of Ben’s-gun pattern in awake patients. Using spikes and background activity several different parameters and their spectral estimates in various frequency bands including low frequency (2–7 Hz), Alpha (8–12 Hz), Beta (sub-divided as Low_Beta (13–20 Hz) and High_Beta (21–30 Hz)) and Gamma (31 to 49 Hz) were computed. The optimal STN lead placement with the most optimal clinical effect/side-effect ratio accorded to the maximum spike rate in 85% of the implantation. Mean amplitude of background activity in the low beta frequency range was corresponding to right depth in 85% and right location in 94% of the implantation respectively. MER can be used for STN mapping and intraoperative decisions for the implantation of DBS electrode leads with a high accuracy. Spiking and background activity in the beta range are the most promising independent parameters for the delimitation of the proper anatomical site.

Neural Oscillations: Understanding a Neural Code of Pain

Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.

Effects of tDCS on spontaneous spike activity in a healthy ambulatory rat model

BACKGROUND

The neurophysiological effects of transcranial direct current stimulation (tDCS) are typically described with respect to changes in cortical excitability, defined by using transcranial magnetic stimulation pulses to determine changes in motor evoked potentials. However, how individual cortical neurons change firing patterns under the influence of tDCS is largely unknown. While the relatively weak currents produced in the brain by tDCS may not be adequate to directly depolarize neuronal membranes, ongoing neuronal activity, combined with subthreshold changes in membrane polarization might be sufficient to alter the threshold for neural firing.

OBJECTIVES

The purpose of this study was to determine the effects of tDCS on neurophysiological activity in motor cortex of freely moving, healthy rats.

METHODS

In nine healthy, ambulatory rats, each studied under six different stimulation conditions varying in current intensity (maximum current density = 39.8 A/m² at 0.4 mA) and polarity (anodal or cathodal), neural activity was analyzed in response to 20 min of tDCS applied through bone screws insulated from the overlying scalp.

RESULTS

After analysis of 480 multi-unit channels that satisfied a rigid set of neurophysiological criteria, we found no systematic effect of tDCS stimulation condition on firing rate or firing pattern. Restricting the analysis to the most responsive units, subtle, but statistically significant changes occurred only in the highest intensity anodal condition.

CONCLUSIONS

These results confirm that at current densities typically used in human or animal tDCS studies, observed effects of tDCS are likely to occur via mechanisms other than direct neuronal depolarization.

Fast spiking interneuron activity in primate striatum tracks learning of attention cues

Cognitive flexibility depends on a fast neural learning mechanism for enhancing momentary relevant over irrelevant information. A possible neural mechanism realizing this enhancement uses fast spiking interneurons (FSIs) in the striatum to train striatal projection neurons to gate relevant and suppress distracting cortical inputs. We found support for such a mechanism in nonhuman primates during the flexible adjustment of visual attention in a reversal learning task. FSI activity was modulated by visual attention cues during feature-based learning. One FSI subpopulation showed stronger activation during learning, while another FSI subpopulation showed response suppression after learning, which could indicate a disinhibitory effect on the local circuit. Additionally, FSIs that showed response suppression to learned attention cues were activated by salient distractor events, suggesting they contribute to suppressing bottom-up distraction. These findings suggest that striatal fast spiking interneurons play an important role when cues are learned that redirect attention away from previously relevant to newly relevant visual information. This cue-specific activity was independent of motor-related activity and thus tracked specifically the learning of reward predictive visual features.

The VIP-VPAC2 neuropeptidergic axis is a cellular pacemaking hub of the suprachiasmatic nucleus circadian circuit

The hypothalamic suprachiasmatic nuclei (SCN) are the principal mammalian circadian timekeeper, co-ordinating organism-wide daily and seasonal rhythms. To achieve this, cell-autonomous circadian timing by the ~20,000 SCN cells is welded into a tight circuit-wide ensemble oscillation. This creates essential, network-level emergent properties of precise, high-amplitude oscillation with tightly defined ensemble period and phase. Although synchronised, regional cell groups exhibit differentially phased activity, creating stereotypical spatiotemporal circadian waves of cellular activation across the circuit. The cellular circuit pacemaking components that generate these critical emergent properties are unknown. Using intersectional genetics and real-time imaging, we show that SCN cells expressing vasoactive intestinal polypeptide (VIP) or its cognate receptor, VPAC2, are neurochemically and electrophysiologically distinct, but together they control de novo rhythmicity, setting ensemble period and phase with circuit-level spatiotemporal complexity. The VIP/VPAC2 cellular axis is therefore a neurochemically and topologically specific pacemaker hub that determines the emergent properties of the SCN timekeeper.

Circadian activity modulation in the suprachiasmatic nucleus (SCN) is a network-level emergent property that requires neuropeptide VIP signaling, yet the precise cellular mechanisms are unknown. Patton et al. show that cells expressing VIP or its receptor VPAC2 together determine these emergent properties of the SCN.

Synaptic Diversity Suppresses Complex Collective Behavior in Networks of Theta Neurons

Comprehending how the brain functions requires an understanding of the dynamics of neuronal assemblies. Previous work used a mean-field reduction method to determine the collective dynamics of a large heterogeneous network of uniformly and globally coupled theta neurons, which are a canonical formulation of Type I neurons. However, in modeling neuronal networks, it is unreasonable to assume that the coupling strength between every pair of neurons is identical. The goal in the present work is to analytically examine the collective macroscopic behavior of a network of theta neurons that is more realistic in that it includes heterogeneity in the coupling strength as well as in neuronal excitability. We consider the occurrence of dynamical structures that give rise to complicated dynamics via bifurcations of macroscopic collective quantities, concentrating on two biophysically relevant cases: (1) predominantly excitable neurons with mostly excitatory connections, and (2) predominantly spiking neurons with inhibitory connections. We find that increasing the synaptic diversity moves these dynamical structures to distant extremes of parameter space, leaving simple collective equilibrium states in the physiologically relevant region. We also study the node vs. focus nature of stable macroscopic equilibrium solutions and discuss our results in the context of recent literature.

Subthalamic Single Cell and Oscillatory Neural Dynamics of a Dyskinetic Medicated Patient With Parkinson's Disease

Single cell neuronal activity (SUA) and local field potentials (LFP) in the subthalamic nucleus (STN) of unmedicated Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery have been well-characterized during microelectrode recordings (MER). However, there is limited knowledge about the changes in the firing patterns and oscillations above and within the territories of STN after the intake of dopaminergic medication. Here, for the first time, we report the STN single cell and oscillatory neural dynamics in a medicated patient with idiopathic PD using intraoperative MER. We recorded LFP and SUA with microelectrodes at various depths during bilateral STN-DBS electrode implantation. We isolated 26 neurons in total and observed that tonic and irregular firing patterns of individual neurons predominated throughout the territories of STN. While burst-type firings have been well-characterized in the dorsal territories of STN in unmedicated patients, interestingly, this activity was not observed in our medicated subject. LFP recordings lacked the excessive beta (8-30 Hz) activity, characteristic of the unmedicated state and signal energy was mainly dominated by slow oscillations below 8 Hz. We observed sharp gamma oscillations between 70 and 90 Hz within and above the STN. Despite the presence of a broadband high frequency activity in 200-400 Hz range, no cross-frequency interaction in the form of phase-amplitude coupling was noted between low and high frequency oscillations of LFPs. While our results are in agreement with the previously reported LFP recordings from the DBS lead in medicated PD patients, the sharp gamma peak present throughout the depth recordings and the lack of bursting firings after levodopa intake have not been reported before. The lack of bursting in SUA, the lack of excessive beta activity and cross frequency coupling between HFOs and lower rhythms further validate the link between bursting firing regime of neurons and pathological oscillatory neural activity in PD-STN. Overall, these observations not only validate the existing literature on the PD electrophysiology in healthy/medicated animal models but also provide insights regarding the underlying electro-pathophysiology of levodopa-induced dyskinesias in PD patients through demonstration of multiscale relationships between single cell firings and field potentials.

Striatal network modeling in Huntington's Disease

Medium spiny neurons (MSNs) comprise over 90% of cells in the striatum. In vivo MSNs display coherent burst firing cell assembly activity patterns, even though isolated MSNs do not burst fire intrinsically. This activity is important for the learning and execution of action sequences and is characteristically dysregulated in Huntington's Disease (HD). However, how dysregulation is caused by the various neural pathologies affecting MSNs in HD is unknown. Previous modeling work using simple cell models has shown that cell assembly activity patterns can emerge as a result of MSN inhibitory network interactions. Here, by directly estimating MSN network model parameters from single unit spiking data, we show that a network composed of much more physiologically detailed MSNs provides an excellent quantitative fit to wild type (WT) mouse spiking data, but only when network parameters are appropriate for the striatum. We find the WT MSN network is situated in a regime close to a transition from stable to strongly fluctuating network dynamics. This regime facilitates the generation of low-dimensional slowly varying coherent activity patterns and confers high sensitivity to variations in cortical driving. By re-estimating the model on HD spiking data we discover network parameter modifications are consistent across three very different types of HD mutant mouse models (YAC128, Q175, R6/2). In striking agreement with the known pathophysiology we find feedforward excitatory drive is reduced in HD compared to WT mice, while recurrent inhibition also shows phenotype dependency. We show that these modifications shift the HD MSN network to a sub-optimal regime where higher dimensional incoherent rapidly fluctuating activity predominates. Our results provide insight into a diverse range of experimental findings in HD, including cognitive and motor symptoms, and may suggest new avenues for treatment.

Resonance in the Mouse Ventral Tegmental Area Dopaminergic Network Induced by Regular and Poisson Distributed Optogenetic Stimulation in-vitro

Neurons in many brain regions exhibit spontaneous, intrinsic rhythmic firing activity. This rhythmic firing activity may determine the way in which these neurons respond to extrinsic synaptic inputs. We hypothesized that neurons should be most responsive to inputs at the frequency of the intrinsic oscillation frequency. We addressed this question in the ventral tegmental area (VTA), a dopaminergic nucleus in the midbrain. VTA neurons have a unique propensity to exhibit spontaneous intrinsic rhythmic activity in the 1-5 Hz frequency range, which persists in the in-vitro brain slice, and form a network of weakly coupled oscillators. Here, we combine in-vitro simultaneous recording of action potentials from a 60 channel multi-electrode-array with cell-type-specific optogenetic stimulation of the VTA dopamine neurons. We investigated how VTA neurons respond to wide-band stochastic (Poisson input) as well as regular laser pulses. Strong synchrony was induced between the laser input and the spike timing of the neurons, both for regular pulse trains and Poisson pulse trains. For rhythmically pulsed input, the neurons demonstrated resonant behavior with the strongest phase locking at their intrinsic oscillation frequency, but also at half and double the intrinsic oscillation frequency. Stochastic Poisson pulse stimulation provided a more effective stimulation of the entire population, yet we observed resonance at lower frequencies (approximately half the oscillation frequency) than the neurons' intrinsic oscillation frequency. The non-linear filter characteristics of dopamine neurons could allow the VTA to predict precisely timed future rewards based on past sensory inputs, a crucial component of reward prediction error signaling. In addition, these filter characteristics could contribute to a pacemaker role for the VTA in synchronizing activity with other regions like the prefrontal cortex and the hippocampus.

Asymmetric dynamic interaction shifts synchronized frequency of coupled oscillators

Interacting dynamic agents can often exhibit synchronization. It has been reported that the rhythm all agents agree on in the synchronized state could be different from the average of intrinsic rhythms of individual agents. Hinted by such a real-world behavior of the interaction-driven change of the average phase velocity, we propose a modified version of the Kuramoto model, in which the ith oscillator of the phase ϕ_i interacts with other oscillator j only when the phase difference \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\phi }}_{{j}}$$\end{document}ϕj − \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\phi }}_{i}$$\end{document}ϕi is in a limited range [−βπ, απ]. From extensive numerical investigations, we conclude that the asymmetric dynamic interaction characterized by β ≠ α leads to the shift of the synchronized frequency with respect to the original distribution of the intrinsic frequency. We also perform and report our computer-based synchronization experiment, which exhibits the expected shift of the synchronized frequency of human participants. In analogy to interacting runners, our result roughly suggests that agents tend to run faster if they are more influenced by runners ahead than behind. We verify the observation by using a simple model of interacting runners.

Multiscale relevance and informative encoding in neuronal spike trains

Neuronal responses to complex stimuli and tasks can encompass a wide range of time scales. Understanding these responses requires measures that characterize how the information on these response patterns are represented across multiple temporal resolutions. In this paper we propose a metric - which we call multiscale relevance (MSR) - to capture the dynamical variability of the activity of single neurons across different time scales. The MSR is a non-parametric, fully featureless indicator in that it uses only the time stamps of the firing activity without resorting to any a priori covariate or invoking any specific structure in the tuning curve for neural activity. When applied to neural data from the mEC and from the ADn and PoS regions of freely-behaving rodents, we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded and significantly better than the set of neurons with high local variations in their interspike intervals. Given these results, we propose that the MSR can be used as a measure to rank and select neurons for their information content without the need to appeal to any a priori covariate.

Grouping Neuronal Spiking Patterns in the Subthalamic Nucleus of Parkinsonian Patients

The subthalamic nucleus (STN) is a commonly used target in deep brain stimulation (DBS) to control the motor symptoms of Parkinson's Disease (PD). Identification of the spiking patterns in the STN is important in order to understand the neuropathophysiology of PD and can also assist in electrophysiological mapping of the structure. This study aims to provide a tool for grouping these firing patterns based on several extracted features from the spiking data. Single neuronal activity from the STN of PD subjects was detected and sorted to compute the binary spike trains. Several features including loca variation, bursting index and the prominence of the peak frequency of the power spectrum were extracted. Clustering of spike train segments was performed based on combination of features in 3D space to scrutinize how well they describe different firing regimes. The results show that this approach could be used to automate the grouping of stereotypic firing patterns in STN.

Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

GABAergic interneurons can be subdivided into three subclasses: parvalbumin positive (PV), somatostatin positive (SOM) and serotonin positive neurons. With principal cells (PCs) they form complex networks. We examine PCs and PV responses in mouse anterior lateral motor cortex (ALM) and barrel cortex (S1) upon PV photostimulation in vivo. In ALM layer five and S1, the PV response is paradoxical: photoexcitation reduces their activity. This is not the case in ALM layer 2/3. We combine analytical calculations and numerical simulations to investigate how these results constrain the architecture. Two-population models cannot explain the results. Four-population networks with V1-like architecture account for the data in ALM layer 2/3 and layer 5. Our data in S1 can be explained if SOM neurons receive inputs only from PCs and PV neurons. In both four-population models, the paradoxical effect implies not too strong recurrent excitation. It is not evidence for stabilization by inhibition.

Unsupervised clustering reveals spatially varying single neuronal firing patterns in the subthalamic nucleus of patients with Parkinson's disease

Introduction

Subthalamic nucleus (STN) is an effective target for deep brain stimulation (DBS) to reduce the motor symptoms of Parkinson's disease (PD). It is important to identify firing patterns within the structure for a better understanding of the electro-pathophysiology of the disease. Using recently established metrics, our study aims to autonomously identify the discharge patterns of individual cells and examine their spatial distribution within the STN.

Methods

We recorded single unit activity (SUA) from 12 awake PD patients undergoing a standard clinical DBS surgery. Three extracted features from raw SUA (local variation, bursting index and prominence of peak) were used with k-means clustering to achieve the aforementioned unsupervised grouping of firing patterns.

Results

279 neurons were isolated and four distinct firing patterns were identified across patients: tonic (11%), irregular (55%), periodic (9%) and non-periodic bursts (25%). The mean firing rates for irregular discharges were significantly lower (p < 0.05) than the rest. Tonic firings were significantly ventral (p < 0.05) while periodic (p < 0.05) and non-periodic (p < 0.01) bursts were dorsal. The percentage of periodically bursting neurons in dorsal region and entire STN were significantly correlated with off state UPDRS tremor scores (r = 0.51, p = 0.04) and improvement in bradykinesia and rigidity (r = 0.57, p = 0.02) respectively.

Conclusion

Strengthening the application of unsupervised clustering for firing patterns of individual cells, this study shows a unique spatial affinity of tonic activity towards the ventral and bursting activity towards the dorsal region of STN in PD patients. This spatial preference, together with the correlation of clinical scores, can provide a clue towards understanding Parkinsonian symptom generation.

Individual Differences in Amphetamine Locomotor Sensitization are Accompanied with Changes in Dopamine Release and Firing Pattern in the Dorsolateral Striatum of Rats

Not all the people that consume drugs of abuse develop addiction. In this sense, just a percentage of rats express locomotor sensitization after repeated psychostimulant exposure. Neurochemical evidence has shown that locomotor sensitization is associated with changes in dorsolateral striatum (DLS) activity. However, it is unknown if individual differences observed in locomotor sensitization are related to differential neuro-adaptations in DLS activity. In this study, we measured basal dopamine (DA) levels and single unit activity in the DLS of anesthetized rats, after repeated amphetamine (AMPH) administration. Rats were treated with AMPH 1.0 mg/kg ip or saline ip for 5 days. Following 5 days of withdrawal, a challenge dose of AMPH 1.0 mg/kg ip was injected. In-vivo microdialysis experiments and single unit recording were carried out twenty-four hours after the last AMPH injection. Sensitized rats showed increased basal DA levels and baseline firing rate of medium spiny neurons (MSNs) compared to non-sensitized rats. The local variation index (Lv) was used to measure the firing pattern of MSNs. In saline rats, a bursty firing pattern was observed in MSNs. A decrease in MSNs baseline Lv accompanies the expression of AMPH locomotor sensitization. Moreover, a decrease in Lv after an acute AMPH 1.0 mg/kg injection was only observed in saline and sensitized rats. Our results show individual differences in DLS basal DA levels and firing pattern after repeated AMPH administration, suggesting that an hyperfunction of nigrostriatal pathway, accompanied by a decrease in DLS MSNs firing irregularity underlies the expression of AMPH locomotor sensitization.

Portraits of communication in neuronal networks

The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

Differential Effects of Open- and Closed-Loop Intracortical Microstimulation on Firing Patterns of Neurons in Distant Cortical Areas

Intracortical microstimulation can be used successfully to modulate neuronal activity. Activity-dependent stimulation (ADS), in which action potentials recorded extracellularly from a single neuron are used to trigger stimulation at another cortical location (closed-loop), is an effective treatment for behavioral recovery after brain lesion, but the related neurophysiological changes are still not clear. Here, we investigated the ability of ADS and random stimulation (RS) to alter firing patterns of distant cortical locations. We recorded 591 neuronal units from 23 Long-Evan healthy anesthetized rats. Stimulation was delivered to either forelimb or barrel field somatosensory cortex, using either RS or ADS triggered from spikes recorded in the rostral forelimb area (RFA). Both RS and ADS stimulation protocols rapidly altered spike firing within RFA compared with no stimulation. We observed increase in firing rates and change of spike patterns. ADS was more effective than RS in increasing evoked spikes during the stimulation periods, by producing a reliable, progressive increase in stimulus-related activity over time and an increased coupling of the trigger channel with the network. These results are critical for understanding the efficacy of closed-loop electrical microstimulation protocols in altering activity patterns in interconnected brain networks, thus modulating cortical state and functional connectivity.

Passing the Message: Representation Transfer in Modular Balanced Networks

Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems' computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust, and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge toward which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks.