Statistical signs of synaptic interaction in neurons

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow

In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. We measured border ownership modulation within large populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that interactions between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, the magnitude of border ownership modulation was predicted by greater information flow from feedback/horizontal to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.

Prelimbic and infralimbic responsivity to amygdala input is modified by gonadal hormones in parallel to low anxiety-like behavior in ovariectomized rats

Gonadal hormones may influence sexual activity by reducing anxiety. The basolateral amygdala (BLA) and prelimbic (PL) and infralimbic (IL) cortical regions comprise a loop that is related to fear, anxiety, and social behavior. In female ovariectomized rats, actions of estradiol, progesterone, and sequential estradiol and progesterone administration were explored in the open field test (OFT) and plus maze test (PMT) to evaluate signs of anxiety-like behavior. The three hormonal treatments reduced indicators of anxiety in the PMT but did not influence behavior in the OFT. In the same behaviorally tested rats under urethane anesthesia, single-unit extracellular recordings were obtained from the PL and IL during electrical stimulation of the BLA. The analysis of 250 ms peristimulus histograms showed that BLA stimulation produced two kinds of response. A small group of neurons increased their firing rate after BLA stimulation. Most neurons exhibited a reduction of spiking. Neurons that increased their firing rate after BLA stimulation did not show any difference with the hormonal treatments. In neurons that were inhibited by BLA stimulation, estradiol reduced the neuronal firing rate in the PL and IL, and progesterone alone and the sequential administration of estradiol followed by progesterone administration 24 h later (priming) increased the firing rate during the 240 ms before BLA stimulation. Analyses of responsivity of the PL and IL during electrical stimulation of the BLA indicated that estradiol, progesterone, and estradiol followed by progesterone administration 24 h later (priming) reduced inhibitory actions of the BLA on the PL but not IL. In the BLA-IL connection, progesterone exacerbated the inhibitory response. These findings indicate that anxiolytic actions of estradiol, progesterone, and estradiol followed by progesterone administration 24 h later (priming) correspond to lower BLA-PL responsivity. Actions of progesterone on BLA-IL responsivity appear to contribute to sexual activity by interacting with other forebrain structures that are also related to sexual receptivity.

Hypoxia evokes a sequence of raphe-pontomedullary network operations for inspiratory drive amplification and gasping

Hypoxia can trigger a sequence of breathing-related behaviors, from tachypnea to apneusis to apnea and gasping, an autoresuscitative behavior that, via large tidal volumes and altered intrathoracic pressure, can enhance coronary perfusion, carotid blood flow, and sympathetic activity, and thereby coordinate cardiac and respiratory functions. We tested the hypothesis that hypoxia-evoked gasps are amplified through a disinhibitory microcircuit within the inspiratory neuron chain and a distributed efference copy mechanism that generates coordinated gasp-like discharges concurrently in other circuits of the raphe-pontomedullary respiratory network. Data were obtained from 6 decerebrate, vagotomized, neuromuscularly-blocked, and artificially ventilated adult cats. Arterial blood pressure, phrenic nerve activity, end-tidal CO2, and other parameters were monitored. Hypoxia was produced by ventilation with a gas mixture of 5% O2 in nitrogen (N2). Neuron spike trains were recorded at multiple pontomedullary sites simultaneously and evaluated for firing rate modulations and short-time scale correlations indicative of functional connectivity. Experimental perturbations evoked reconfiguration of raphe-pontomedullary circuits during tachypnea, apneusis and augmented bursts, apnea, and gasping. The functional connectivity, altered firing rates, efference copy of gasp drive, and coordinated step increments in blood pressure reported here support a distributed brain stem network model for amplification and broadcasting of inspiratory drive during autoresuscitative gasping that begins with a reduction in inhibition by expiratory neurons and an initial loss of inspiratory drive during hypoxic apnea.

Insulin and fluoxetine produce opposite actions on lateral septal nucleus-infralimbic region connection responsivity

Some diabetes patients develop depression, the main treatment for which is antidepressants. Pharmacological interactions between insulin and antidepressants (e.g., fluoxetine) are controversial in the literature. Some authors reported hypoglycemic actions of fluoxetine, whereas others reported antidepressant-like actions. In healthy rats, insulin produces an antidespair-like action in rats through an increase in locomotor and exploratory activity, but differences in actions of insulin and fluoxetine on neuronal activity are unknown. The present study evaluated Wistar healthy rats that were treated with saline, insulin, fluoxetine, or fluoxetine + insulin for 3 days (short-term) or 21 days (long-term). The model consisted of electrical stimulation of the lateral septal nucleus (LSN) while we performed single-unit extracellular response recordings in the prelimbic cortex (PL) and infralimbic cortex (IL) subregions of the medial prefrontal cortex (mPFC). Stimulation of the LSN produced an initial brief excitatory paucisynaptic response and then a long-lasting inhibitory afterdischarge in the PL and IL. Treatment with saline and fluoxetine, but not insulin, minimally affected the paucisynaptic response. Differences were found in LSN-IL responsivity. The inhibitory afterdischarge was clearly enhanced in the long-term fluoxetine group but not by insulin alone or fluoxetine + insulin. These findings suggest that insulin produces some actions that are opposite to fluoxetine on LSN-mPFC connection responsivity, with no synergistic actions between the actions of insulin and fluoxetine.

Alpha-synuclein oligomers alter the spontaneous firing discharge of cultured midbrain neurons

The aim of this work was to monitor the effects of extracellular α-synuclein on the firing activity of midbrain neurons dissociated from substantia nigra TH-GFP mice embryos and cultured on microelectrode arrays (MEA). We monitored the spontaneous firing discharge of the network for 21 days after plating and the role of glutamatergic and GABAergic inputs in regulating burst generation and network synchronism. Addition of GABA A , AMPA and NMDA antagonists did not suppress the spontaneous activity but allowed to identify three types of neurons that exhibited different modalities of firing and response to applied L-DOPA: high-rate (HR) neurons, low-rate pacemaking (LR-p), and low-rate non-pacemaking (LR-np) neurons. Most HR neurons were insensitive to L-DOPA, while the majority of LR-p neurons responded with a decrease of the firing discharge; less defined was the response of LR-np neurons. The effect of exogenous α-synuclein (α-syn) on the firing discharge of midbrain neurons was then studied by varying the exposure time (0-48 h) and the α-syn concentration (0.3-70 μM), while the formation of α-syn oligomers was monitored by means of AFM. Independently of the applied concentration, acute exposure to α-syn monomers did not exert any effect on the spontaneous firing rate of HR, LR-p, and LR-np neurons. On the contrary, after 48 h exposure, the firing activity was drastically altered at late developmental stages (14 days in vitro, DIV, neurons): α-syn oligomers progressively reduced the spontaneous firing discharge (IC50 = 1.03 μM), impaired burst generation and network synchronism, proportionally to the increased oligomer/monomer ratio. Different effects were found on early-stage developed neurons (9 DIV), whose firing discharge remained unaltered, regardless of the applied α-syn concentration and the exposure time. Our findings unravel, for the first time, the variable effects of exogenous α-syn at different stages of midbrain network development and provide new evidence for the early detection of neuronal function impairment associated to aggregated forms of α-syn.

Functional interactions among neurons within single columns of macaque V1

Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.

Hasty sensorimotor decisions rely on an overlap of broad and selective changes in motor activity

Humans and other animals are able to adjust their speed–accuracy trade-off (SAT) at will depending on the urge to act, favoring either cautious or hasty decision policies in different contexts. An emerging view is that SAT regulation relies on influences exerting broad changes on the motor system, tuning its activity up globally when hastiness is at premium. The present study aimed to test this hypothesis. A total of 50 participants performed a task involving choices between left and right index fingers, in which incorrect choices led either to a high or to a low penalty in 2 contexts, inciting them to emphasize either cautious or hasty policies. We applied transcranial magnetic stimulation (TMS) on multiple motor representations, eliciting motor-evoked potentials (MEPs) in 9 finger and leg muscles. MEP amplitudes allowed us to probe activity changes in the corresponding finger and leg representations, while participants were deliberating about which index to choose. Our data indicate that hastiness entails a broad amplification of motor activity, although this amplification was limited to the chosen side. On top of this effect, we identified a local suppression of motor activity, surrounding the chosen index representation. Hence, a decision policy favoring speed over accuracy appears to rely on overlapping processes producing a broad (but not global) amplification and a surround suppression of motor activity. The latter effect may help to increase the signal-to-noise ratio of the chosen representation, as supported by single-trial correlation analyses indicating a stronger differentiation of activity changes in finger representations in the hasty context.

Many have argued that the regulation of the speed-accuracy tradeoff relies on an urgency signal, which implements "collapsing decision thresholds" by tuning neural activity in a global manner in decision-related structures. This study indicates that the reality is more subtle, with several aspects of "urgency" being specifically targeted to particular corticospinal populations within the motor system.

The Cellular Basis for the Generation of Firing Patterns in Human Motor Units

Motor units, which comprise a motoneuron and the set of muscle fibers it innervates, are the fundamental neuromuscular transducers for all motor commands. The one to one relationship between a motoneuron and its innervated muscle fibers allow motoneuron firing patterns to be readily measured in humans. In this chapter, we summarize the current understanding of the cellular basis for the generation of firing patterns in human motor units. We provide a brief review of landmark insights from classic studies and then proceed to consider the features of motor unit firing patterns that are most likely to be sensitive estimators of motoneuron inputs and properties. In addition, we discuss recent advances in technology for recording human motor unit firing patterns and highly realistic computer simulations of motoneurons. The final section presents our recent efforts to use the power of supercomputers for implementation of the motoneuron models, with a goal of achieving a true "reverse engineering" approach that maximizes the insights from motor unit firing patterns into the synaptic structure of motor commands.

Reduced sociability and social agency encoding in adult Shank3-mutant mice are restored through gene re-expression in real time

Despite a growing understanding of the molecular and developmental basis of autism spectrum disorder (ASD), how the neuronal encoding of social information is disrupted in ASD and whether it contributes to abnormal social behavior remains unclear. Here, we disrupted and then restored expression of the ASD-associated gene Shank3 in adult male mice while tracking the encoding dynamics of neurons in the medial prefrontal cortex (mPFC) over weeks. We find that Shank3 disruption led to a reduction of neurons encoding the experience of other mice and an increase in neurons encoding the animal's own experience. This shift was associated with a loss of ability by neurons to distinguish other from self and, therefore, the inability to encode social agency. Restoration of Shank3 expression in the mPFC reversed this encoding imbalance and increased sociability over 5-8 weeks. These findings reveal a neuronal-encoding process that is necessary for social behavior and that may be disrupted in ASD.

Estimating Exercise-Induced Changes in Human Neuronal Networks

Although several methods have been used to estimate exercise-induced changes in human neuronal networks, there are growing doubts about the methodologies used. This review describes a single motor unit-based method that minimizes the errors inherent in classical methods. With this method, it is now possible to identify human neuronal networks' changes due to exercise.

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

Modeling statistical dependencies in multi-region spike train data

Neural computations underlying cognition and behavior rely on the coordination of neural activity across multiple brain areas. Understanding how brain areas interact to process information or generate behavior is thus a central question in neuroscience. Here we provide an overview of statistical approaches for characterizing statistical dependencies in multi-region spike train recordings. We focus on two classes of models in particular: regression-based models and shared latent variable models. Regression-based models describe interactions in terms of a directed transformation of information from one region to another. Shared latent variable models, on the other hand, seek to describe interactions in terms of sources that capture common fluctuations in spiking activity across regions. We discuss the advantages and limitations of each of these approaches and future directions for the field. We intend this review to be an introduction to the statistical methods in multi-region models for computational neuroscientists and experimentalists alike.

Blood pressure drives multispectral tuning of inspiration via a linked-loop neural network

The respiratory motor pattern is coordinated with cardiovascular system regulation. Inspiratory drive and respiratory phase durations are tuned by blood pressure and baroreceptor reflexes. We hypothesized that perturbations of systemic arterial blood pressure modulate inspiratory drive through a raphe-pontomedullary network. In 15 adult decerebrate vagotomized neuromuscular-blocked cats, we used multielectrode arrays to record the activities of 704 neurons within the medullary ventral respiratory column, pons, and raphe areas during baroreceptor-evoked perturbations of breathing, as measured by altered peak activity in integrated efferent phrenic nerve activity and changes in respiratory phase durations. Blood pressure was transiently (30 s) elevated or reduced by inflations of an embolectomy catheter in the descending aorta or inferior vena cava. S-transform time-frequency representations were calculated for multiunit phrenic nerve activity and some spike trains to identify changes in rhythmic activity during perturbations. Altered firing rates in response to either or both conditions were detected for 474 of 704 tested cells. Spike trains of 17,805 neuron pairs were evaluated for short-time scale correlational signatures indicative of functional connectivity with standard cross-correlation analysis, supplemented with gravitational clustering; ∼70% of tested (498 of 704) and responding neurons (333 of 474) were involved in a functional correlation with at least one other cell. Changes in high-frequency oscillations in the spiking of inspiratory neurons and the evocation or resetting of slow quasi-periodic fluctuations in the respiratory motor pattern associated with oscillations of arterial pressure were observed. The results support a linked-loop pontomedullary network architecture for multispectral tuning of inspiration.NEW & NOTEWORTHY The brain network that supports cardiorespiratory coupling remains poorly understood. Using multielectrode arrays, we tested the hypothesis that blood pressure and baroreceptor reflexes "tune" the breathing motor pattern via a raphe-pontomedullary network. Neuron responses to changes in arterial pressure and identified functional connectivity, together with altered high frequency and slow Lundberg B-wave oscillations, support a model with linked recurrent inhibitory loops that stabilize the respiratory network and provide a path for transmission of baroreceptor signals.

Inferring neural information flow from spiking data

The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use of dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.

Modeling the Short-Term Dynamics of in Vivo Excitatory Spike Transmission

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.SIGNIFICANCE STATEMENT Although synaptic dynamics have been extensively studied and modeled using intracellular recordings of postsynaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here, we developed a model that, using only observations of presynaptic and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity (STP) and nonsynaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

Spatial resolution of local field potential signals in macaque V4

OBJECTIVE

An important challenge for the development of cortical visual prostheses is to generate spatially localized percepts of light, using artificial stimulation. Such percepts are called phosphenes, and the goal of prosthetic applications is to generate a pattern of phosphenes that matches the structure of the retinal image. A preliminary step in this process is to understand how the spatial positions of phosphene-like visual stimuli are encoded in the distributed activity of cortical neurons. The spatial resolution with which the distributed responses discriminate positions puts a limit on the capability of visual prosthesis devices to induce phosphenes at multiple positions. While most previous prosthetic devices have targeted the primary visual cortex, the extrastriate cortex has the advantage of covering a large part of the visual field with a smaller amount of cortical tissue, providing the possibility of a more compact implant. Here, we studied how well ensembles of Local Field Potentials (LFPs) and Multiunit activity (MUA) responses from extrastriate cortical visual area V4 of a behaving macaque monkey can discriminate between two-dimensional spatial positions.

APPROACH

We used support vector machines (SVM) to determine the capabilities of LFPs and MUA to discriminate responses to phosphene-like stimuli (probes) at different spatial separations. We proposed a selection strategy based on the combined responses of multiple electrodes and used the linear learning weights to find the minimum number of electrodes for fine and coarse discriminations. We also measured the contribution of correlated trial-to-trial variability in the responses to the discrimination performance for MUA and LFP.

MAIN RESULTS

We found that despite the large receptive field sizes in V4, the combined responses from multiple sites, whether MUA or LFP, are capable of fine and coarse discrimination of positions. Our electrode selection procedure significantly increased discrimination performance while reducing the required number of electrodes. Analysis of noise correlations in MUA and LFP responses showed that noise correlations in LFPs carry more information about spatial positions.

SIGNIFICANCE

This study determined the coding strategy for fine discrimination, suggesting that spatial positions could be well localized with patterned stimulation in extrastriate area V4. It also provides a novel approach to build a compact prosthesis with relatively few electrodes, which has the potential advantage of reducing tissue damage in real applications.

Spinal cord neural network interactions: implications for sympathetic control of the porcine heart

Inherent and acquired factors determine the integrated autonomic response to cardiovascular stressors. Excessive sympathoexcitation to ischemic stress is a major contributor to the potential for sudden cardiac death. To define fundamental aspects of cardiac-related autonomic neural network interactions within the thoracic cord, specifically as related to modulating sympathetic preganglionic (SPN) neural activity. Adult, anesthetized Yorkshire pigs (n = 10) were implanted with penetrating high-density microarrays (64 electrodes) at the T2 level of the thoracic spinal cord to record extracellular potentials concurrently from left-sided dorsal horn (DH) and SPN neurons. Electrical stimulation of the T2 paravertebral chain allowed for antidromic identification of SPNs located in the intermediolateral cell column (57 of total 1,760 recorded neurons). Cardiac stressors included epicardial touch, occlusion of great vessels to transiently alter preload/afterload, and transient occlusion of the left anterior descending coronary artery (LAD). Spatial/temporal assessment of network interactions was characterized by cross-correlation analysis. While some DH neurons responded solely to changes in preload/afterload (8.5 ± 1.9%) or ischemic stress (10.5 ± 3.9%), the majority of cardiovascular-related DH neurons were multimodal (30.2 ± 4.7%) with ischemia sensitivity being one of the modalities (26.1 ± 4.7%). The sympathoexcitation associated with transient LAD occlusion was associated with increased correlations from baseline within DH neurons (2.43 ± 0.61 to 7.30 ± 1.84%, P = 0.04) and between SPN to DH neurons (1.32 ± 0.78 to 7.24 ± 1.84%, P = 0.02). DH to SPN network correlations were reduced during great vessel occlusion. In conclusion, increased intrasegmental network coherence within the thoracic spinal cord contributes to myocardial ischemia-induced sympathoexcitation.NEW & NOTEWORTHY In an in vivo pig model, we demonstrate using novel high-resolution neural electrode arrays that increased intrasegmental network coherence within the thoracic spinal cord contributes to myocardial ischemia-induced sympathoexcitation.

Transcranial magnetic stimulation induced early silent period and rebound activity re-examined

Despite being widely studied, the underlying mechanisms of transcranial magnetic brain stimulation (TMS) induced motor evoked potential (MEP), early cortical silent period (CSP) and rebound activity are not fully understood. Our aim is to better characterize these phenomena by combining various analysis tools on firing motor units. Responses of 29 tibialis anterior (TA) and 8 abductor pollicis brevis (APB) motor units to TMS pulses were studied using discharge rate and probability-based tools to illustrate the profile of the synaptic potentials as they develop on motoneurons in 24 healthy volunteers. According to probability-based methods, TMS pulse produces a short-latency MEP which is immediately followed by CSP that terminates at rebound activity. Discharge rate analysis, however, revealed not three, but just two events with distinct time courses; a long-lasting excitatory period (71.2 ± 9.0 ms for TA and 42.1 ± 11.2 ms for APB) and a long-latency inhibitory period with duration of 57.9 ± 9.5 ms for TA and 67.3 ± 13.8 ms for APB. We propose that part of the CSP may relate to the falling phase of net excitatory postsynaptic potential induced by TMS. Rebound activity, on the other hand, may represent tendon organ inhibition induced by MEP activated soleus contraction and/or long-latency intracortical inhibition. Due to generation of field potentials when high intensity TMS is used, this study is limited to investigate the events evoked by low intensity TMS only and does not provide information about later parts of much longer CSPs induced by high intensity TMS. Adding discharge rate analysis contributes to obtain a more accurate picture about the characteristics of TMS-induced events. These results have implications for interpreting motor responses following TMS for diagnosis and overseeing recovery from various neurological conditions.

Mid-cervical interneuron networks following high cervical spinal cord injury

Spinal interneuron (IN) networks can facilitate respiratory motor recovery after spinal cord injury (SCI). We hypothesized that excitatory synaptic connectivity between INs located immediately caudal to unilateral cervical SCI would be most prevalent in a contra- to ipsilateral direction. Adult rats were studied following chronic C2 spinal cord hemisection (C2Hx) injury. Rats were anesthetized and ventilated and a multi-electrode array was used to simultaneously record INs on both sides of the C4-5 spinal cord. The temporal firing relationship between IN pairs was evaluated using cross-correlation with directionality of synaptic connections inferred based on electrode location. During baseline recordings, the majority of detectable excitatory IN connections occurred in a contra- to- ipsilateral direction. However, acute respiratory stimulation with hypoxia abolished this directionality, while simultaneously increasing the detectable inhibitory connections within the ipsilateral cord. We conclude that propriospinal networks caudal to SCI can display a contralateral-to-ipsilateral directionality of synaptic connections and that these connections are modulated by acute exposure to hypoxia.

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.

Electrophysiological monitoring of inhibition in mammalian species, from rodents to humans

GABAergic interneurons constitute a highly diverse family of neurons that play a critical role in cortical functions. Due to their prominent role in cortical network dynamics, genetic, developmental, or other dysfunctions in GABAergic neurons have been linked to neurological disorders such as epilepsy. Thus, it is crucial to investigate the interaction of these various neurons and to develop methods to specifically and directly monitor inhibitory activity in vivo. While research in small mammals has benefited from a wealth of recent technological development, bridging the gap to large mammals and humans remains a challenge. This is of particular interest since single neuron monitoring with intracranial electrodes in epileptic patients is developing quickly, opening new avenues for understanding the role of different cell types in epilepsy. Here, we review currently available techniques that monitor inhibitory activity in the brain and the respective validations in rodents. Finally, we discuss the future developments of these techniques and how knowledge from animal research can be translated to the study of neuronal circuit dynamics in the human brain.

Predicting responses to inhibitory synaptic input in substantia nigra pars reticulata neurons

The changes in firing probability produced by a synaptic input are usually visualized using the poststimulus time histogram (PSTH). It would be useful if postsynaptic firing patterns could be predicted from patterns of afferent synaptic activation, but attempts to predict the PSTH from synaptic potential waveforms using reasoning based on voltage trajectory and spike threshold have not been successful, especially for inhibitory inputs. We measured PSTHs for substantia nigra pars reticulata (SNr) neurons inhibited by optogenetic stimulation of striato-nigral inputs or by matching artificial inhibitory conductances applied by dynamic clamp. The PSTH was predicted by a model based on each SNr cell's phase-resetting curve (PRC). Optogenetic activation of striato-nigral input or artificial synaptic inhibition produced a PSTH consisting of an initial depression of firing followed by oscillatory increases and decreases repeating at the SNr cell's baseline firing rate. The phase resetting model produced PSTHs closely resembling the cell data, including the primary pause in firing and the oscillation. Key features of the PSTH, including the onset rate and duration of the initial inhibitory phase, and the subsequent increase in firing probability could be explained from the characteristic shape of the SNr cell's PRC. The rate of damping of the late oscillation was explained by the influence of asynchronous phase perturbations producing firing rate jitter and wander. Our results demonstrate the utility of phase-resetting models as a general method for predicting firing in spontaneously active neurons and their value in interpretation of the striato-nigral PSTH. NEW & NOTEWORTHY The coupling of patterned presynaptic input to sequences of postsynaptic firing is a Gordian knot, complicated by the multidimensionality of neuronal state and the diversity of potential initial states. Even so, it is fundamental for even the simplest understanding of network dynamics. We show that a simple phase-resetting model constructed from experimental measurements can explain and predict the sequence of spike rate changes following synaptic inhibition of an oscillating basal ganglia output neuron.

Rewiring the connectome: Evidence and effects

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Rewiring is a plasticity mechanism that alters connectivity between neurons.

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Evidence for rewiring has been difficult to obtain.

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New evidence indicates that local circuitry is rewired during learning.

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Harnessing rewiring offers new ways to treat psychiatric and neurological diseases.

Neuronal connections form the physical basis for communication in the brain. Recently, there has been much interest in mapping the “connectome” to understand how brain structure gives rise to brain function, and ultimately, to behaviour. These attempts to map the connectome have largely assumed that connections are stable once formed. Recent studies, however, indicate that connections in mammalian brains may undergo rewiring during learning and experience-dependent plasticity. This suggests that the connectome is more dynamic than previously thought. To what extent can neural circuitry be rewired in the healthy adult brain? The connectome has been subdivided into multiple levels of scale, from synapses and microcircuits through to long-range tracts. Here, we examine the evidence for rewiring at each level. We then consider the role played by rewiring during learning. We conclude that harnessing rewiring offers new avenues to treat brain diseases.

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the notion of pre-synaptic and post-synaptic neurons, stimulus correlations and noise correlations have a clear time order. Therefore, a biologically realistic statistical model for the spiking activity should be able to capture some degree of time irreversibility. We use the thermodynamic formalism to build a framework in the context maximum entropy models to quantify the degree of time irreversibility, providing an explicit formula for the information entropy production of the inferred maximum entropy Markov chain. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.

Discovering Brain Mechanisms Using Network Analysis and Causal Modeling

Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain's anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain's anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it.

Receptive field properties of cat perigeniculate neurons correlate with excitatory and inhibitory connectivity to LGN relay neurons

The cat perigeniculate nucleus (PGN) is a visual sector of the thalamic reticular nucleus that consists of GABAergic neurons. It receives excitatory axon-collateral input from relay neurons of the dorsal lateral geniculate nucleus (LGN) to which it provides inhibitory input. Thus, it is usually argued that the PGN works as feedback inhibition to the LGN. At the single neuron level, however, this circuit can also provide lateral inhibition. Which inhibition dominates in the visual circuit of the thalamus has yet to be well characterized. In this study, we conducted cross-correlation analysis of single spike trains simultaneously recorded from PGN and LGN neurons in anesthetized cats. For 12 pairs of functionally connected PGN and LGN neurons with overlapped receptive fields (RF), we quantitatively compared RF properties including the spatial frequency (SF) and temporal frequency (TF) tunings of each neuron. We found the SF and TF tunings of PGN neurons and LGN neurons were similar when there was only excitatory input from the LGN neuron to the PGN neuron, but different when the PGN neuron returned inhibitory inputs back, suggesting the circuit between PGN and LGN neurons works as lateral inhibition for these properties.

Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat

The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model.

Intermittent Hypoxia Enhances Functional Connectivity of Midcervical Spinal Interneurons

Brief, intermittent oxygen reductions [acute intermittent hypoxia (AIH)] evokes spinal plasticity. Models of AIH-induced neuroplasticity have focused on motoneurons; however, most midcervical interneurons (C-INs) also respond to hypoxia. We hypothesized that AIH would alter the functional connectivity between C-INs and induce persistent changes in discharge. Bilateral phrenic nerve activity was recorded in anesthetized and ventilated adult male rats and a multielectrode array was used to record C4/5 spinal discharge before [baseline (BL)], during, and 15 min after three 5 min hypoxic episodes (11% O₂, H1-H3). Most C-INs (94%) responded to hypoxia by either increasing or decreasing firing rate. Functional connectivity was examined by cross-correlating C-IN discharge. Correlograms with a peak or trough were taken as evidence for excitatory or inhibitory connectivity between C-IN pairs. A subset of C-IN pairs had increased excitatory cross-correlations during hypoxic episodes (34%) compared with BL (19%; p < 0.0001). Another subset had a similar response following each episode (40%) compared with BL (19%; p < 0.0001). In the latter group, connectivity remained elevated 15 min post-AIH (30%; p = 0.0002). Inhibitory C-IN connectivity increased during H1-H3 (4.5%; p = 0.0160), but was reduced 15 min post-AIH (0.5%; p = 0.0439). Spike-triggered averaging indicated that a subset of C-INs is synaptically coupled to phrenic motoneurons and excitatory inputs to these "pre-phrenic" cells increased during AIH. We conclude that AIH alters connectivity of the midcervical spinal network. To our knowledge, this is the first demonstration that AIH induces plasticity within the propriospinal network.SIGNIFICANCE STATEMENT Acute intermittent hypoxia (AIH) can trigger spinal plasticity associated with sustained increases in respiratory, somatic, and/or autonomic motor output. The impact of AIH on cervical spinal interneuron (C-IN) discharge and connectivity is unknown. Our results demonstrate that AIH recruits excitatory C-INs into the spinal respiratory (phrenic) network. AIH also enhances excitatory and reduces inhibitory connections among the C-IN network. We conclude that C-INs are part of the respiratory, somatic, and/or autonomic response to AIH, and that propriospinal plasticity may contribute to sustained increases in motor output after AIH.

Coupling multielectrode array recordings with silver labeling of recording sites to study cervical spinal network connectivity

Midcervical spinal interneurons form a complex and diffuse network and may be involved in modulating phrenic motor output. The intent of the current work was to enable a better understanding of midcervical "network-level" connectivity by pairing the neurophysiological multielectrode array (MEA) data with histological verification of the recording locations. We first developed a method to deliver 100-nA currents to electroplate silver onto and subsequently deposit silver from electrode tips after obtaining midcervical (C3-C5) recordings using an MEA in anesthetized and ventilated adult rats. Spinal tissue was then fixed, harvested, and histologically processed to "develop" the deposited silver. Histological studies verified that the silver deposition method discretely labeled (50-μm resolution) spinal recording locations between laminae IV and X in cervical segments C3-C5. Using correlative techniques, we next tested the hypothesis that midcervical neuronal discharge patterns are temporally linked. Cross-correlation histograms produced few positive peaks (5.3%) in the range of 0-0.4 ms, but 21.4% of neuronal pairs had correlogram peaks with a lag of ≥0.6 ms. These results are consistent with synchronous discharge involving mono- and polysynaptic connections among midcervical neurons. We conclude that there is a high degree of synaptic connectivity in the midcervical spinal cord and that the silver-labeling method can reliably mark metal electrode recording sites and "map" interneuron populations, thereby providing a low-cost and effective tool for use in MEA experiments. We suggest that this method will be useful for further exploration of midcervical network connectivity.NEW & NOTEWORTHY We describe a method that reliably identifies the locations of multielectrode array (MEA) recording sites while preserving the surrounding tissue for immunohistochemistry. To our knowledge, this is the first cost-effective method to identify the anatomic locations of neuronal ensembles recorded with a MEA during acute preparations without the requirement of specialized array electrodes. In addition, evaluation of activity recorded from silver-labeled sites revealed a previously unappreciated degree of connectivity between midcervical interneurons.

Dynamics of excitatory and inhibitory networks are differentially altered by selective attention

Inhibition and excitation form two fundamental modes of neuronal interaction, yet we understand relatively little about their distinct roles in service of perceptual and cognitive processes. We developed a multidimensional waveform analysis to identify fast-spiking (putative inhibitory) and regular-spiking (putative excitatory) neurons in vivo and used this method to analyze how attention affects these two cell classes in visual area V4 of the extrastriate cortex of rhesus macaques. We found that putative inhibitory neurons had both greater increases in firing rate and decreases in correlated variability with attention compared with putative excitatory neurons. Moreover, the time course of attention effects for putative inhibitory neurons more closely tracked the temporal statistics of target probability in our task. Finally, the session-to-session variability in a behavioral measure of attention covaried with the magnitude of this effect. Together, these results suggest that selective targeting of inhibitory neurons and networks is a critical mechanism for attentional modulation.

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

DOI:http://dx.doi.org/10.7554/eLife.15719.001

The network of neurons in our brain generates all of our actions, yet it is not well understood how these neurons coordinate their activity with each other. Rhythmic electrical activity that happens at the same time across many different neurons is thought to be crucial for allowing different areas of the brain to communicate. However, it is still unclear what purpose rhythmic activity serves for communication. Are there groups of ‘hub’ neurons in different brain regions that coordinate overall activity by rhythmically synchronizing the network of neurons? Or is rhythmic activity insignificant for network coordination?

Dann et al. trained three monkeys to follow specific instructions to grasp a handle in different ways. While the monkeys performed the task, the activity of about 100 neurons was recorded simultaneously in three brain regions that are involved in planning and carrying out grasping movements. This revealed that the activity of the neurons was coordinated by a group of strongly connected hub neurons, which were distributed across all three of the brain regions. Nearly all of the hub neurons were rhythmically synchronized with each other, and also communicated with other neurons using rhythmic electrical activity.

Overall, the results presented by Dann et al. suggest that rhythmically synchronized activity is essential for neurons to coordinate how information is processed across the brain. Further studies into this method of communicating information will help to reveal how the primate brain can generate an immense range of behaviors.

DOI:http://dx.doi.org/10.7554/eLife.15719.002

Correlations and Neuronal Population Information

Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.

Vestibular contribution to balance control in the medial gastrocnemius and soleus

The soleus (Sol) and medial gastrocnemius (mGas) muscles have different patterns of activity during standing balance and may have distinct functional roles. Using surface electromyography we previously observed larger responses to galvanic vestibular stimulation (GVS) in the mGas compared with the Sol muscle. However, it is unclear whether this difference is an artifact that reflects limitations associated with surface electromyography recordings or whether a compensatory balance response to a vestibular error signal activates the mGas to a greater extent than the Sol. In the present study, we compared the effect of GVS on the discharge behavior of 9 Sol and 21 mGas motor units from freely standing subjects. In both Sol and mGas motor units, vestibular stimulation induced biphasic responses in measures of discharge timing [11 ± 5.0 (mGas) and 5.6 ± 3.8 (Sol) counts relative to the sham (mean ± SD)], and frequency [0.86 ± 0.6 Hz (mGas), 0.34 ± 0.2 Hz (Sol) change relative to the sham]. Peak-to-trough response amplitudes were significantly larger in the mGas (62% in the probability-based measure and 160% in the frequency-based measure) compared with the Sol (multiple P < 0.05). Our results provide direct evidence that vestibular signals have a larger influence on the discharge activity of motor units in the mGas compared with the Sol. More tentatively, these results indicate the mGas plays a greater role in vestibular-driven balance corrections during standing balance.

Role of the dorsal medulla in the neurogenesis of airway protection

The dorsal medulla encompassing the nucleus of the tractus solitarius (NTS) and surrounding reticular formation (RF) has an important role in processing sensory information from the upper and lower airways for the generation and control of airway protective behaviors. These behaviors, such as cough and swallow, historically have been studied in isolation. However, recent information indicates that these and other airway protective behaviors are coordinated to minimize risk of aspiration. The dorsal medullary neural circuits that include the NTS are responsible for rhythmogenesis for repetitive swallowing, but previous models have assigned a role for this portion of the network for coughing that is restricted to monosynaptic sensory processing. We propose a more complex NTS/RF circuit that controls expression of swallowing and coughing and the coordination of these behaviors. The proposed circuit is supported by recordings of activity patterns of selected neural elements in vivo and simulations of a computational model of the brainstem circuit for breathing, coughing, and swallowing. This circuit includes separate rhythmic sub-circuits for all three behaviors. The revised NTS/RF circuit can account for the mode of action of antitussive drugs on the cough motor pattern, as well as the unique coordination of cough and swallow by a meta-behavioral control system for airway protection.

Synchronization of motor unit firings: an epiphenomenon of firing rate characteristics not common inputs

Synchronous motor unit firing instances have been attributed to anatomical inputs shared by motoneurons. Yet, there is a lack of empirical evidence confirming the notion that common inputs elicit synchronization under voluntary conditions. We tested this notion by measuring synchronization between motor unit action potential trains (MUAPTs) as their firing rates progressed within a contraction from a relatively low force level to a higher one. On average, the degree of synchronization decreased as the force increased. The common input notion provides no empirically supported explanation for the observed synchronization behavior. Therefore, we investigated a more probable explanation for synchronization. Our data set of 17,546 paired MUAPTs revealed that the degree of synchronization varies as a function of two characteristics of the motor unit firing rate: the similarity and the slope as a function of force. Both are measures of the excitation of the motoneurons. As the force generated by the muscle increases, the firing rate slope decreases, and the synchronization correspondingly decreases. Different muscles have motor units with different firing rate characteristics and display different amounts of synchronization. Although this association is not proof of causality, it consistently explains our observations and strongly suggests further investigation. So viewed, synchronization is likely an epiphenomenon, subject to countless unknown neural interactions. As such, synchronous firing instances may not be the product of a specific design and may not serve a specific physiological purpose. Our explanation for synchronization has the advantage of being supported by empirical evidence, whereas the common input does not.

Functional connectivity in raphé-pontomedullary circuits supports active suppression of breathing during hypocapnic apnea

Hyperventilation is a common feature of disordered breathing. Apnea ensues if CO2 drive is sufficiently reduced. We tested the hypothesis that medullary raphé, ventral respiratory column (VRC), and pontine neurons have functional connectivity and persistent or evoked activities appropriate for roles in the suppression of drive and rhythm during hyperventilation and apnea. Phrenic nerve activity, arterial blood pressure, end-tidal CO2, and other parameters were monitored in 10 decerebrate, vagotomized, neuromuscularly-blocked, and artificially ventilated cats. Multielectrode arrays recorded spiking activity of 649 neurons. Loss and return of rhythmic activity during passive hyperventilation to apnea were identified with the S-transform. Diverse fluctuating activity patterns were recorded in the raphé-pontomedullary respiratory network during the transition to hypocapnic apnea. The firing rates of 160 neurons increased during apnea; the rates of 241 others decreased or stopped. VRC inspiratory neurons were usually the last to cease firing or lose rhythmic activity during the transition to apnea. Mayer wave-related oscillations (0.04-0.1 Hz) in firing rate were also disrupted during apnea. Four-hundred neurons (62%) were elements of pairs with at least one hyperventilation-responsive neuron and a correlational signature of interaction identified by cross-correlation or gravitational clustering. Our results support a model with distinct groups of chemoresponsive raphé neurons contributing to hypocapnic apnea through parallel processes that incorporate disfacilitation and active inhibition of inspiratory motor drive by expiratory neurons. During apnea, carotid chemoreceptors can evoke rhythm reemergence and an inspiratory shift in the balance of reciprocal inhibition via suppression of ongoing tonic expiratory neuron activity.

Cortical state determines global variability and correlations in visual cortex

The response of neurons in sensory cortex to repeated stimulus presentations is highly variable. To investigate the nature of this variability, we compared the spike activity of neurons in the primary visual cortex (V1) of cats with that of their afferents from lateral geniculate nucleus (LGN), in response to similar stimuli. We found variability to be much higher in V1 than in LGN. To investigate the sources of the additional variability, we measured the spiking activity of large V1 populations and found that much of the variability was shared across neurons: the variable portion of the responses of one neuron could be well predicted from the summed activity of the rest of the neurons. Variability thus mostly reflected global fluctuations affecting all neurons. The size and prevalence of these fluctuations, both in responses to stimuli and in ongoing activity, depended on cortical state, being larger in synchronized states than in more desynchronized states. Contrary to previous reports, these fluctuations invested the overall population, regardless of preferred orientation. The global fluctuations substantially increased variability in single neurons and correlations among pairs of neurons. Once this effect was removed, pairwise correlations were reduced and were similar regardless of cortical state. These results highlight the importance of cortical state in controlling cortical operation and can help reconcile previous studies, which differed widely in their estimate of neuronal variability and pairwise correlations.

Improved estimation and interpretation of correlations in neural circuits

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150-350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive 'excitatory' interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative 'inhibitory' interactions were less selective. Because of its superior performance, this 'sparse+latent' estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix.

Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments

Accurately describing synaptic interactions between neurons and how interactions change over time are key challenges for systems neuroscience. Although intracellular electrophysiology is a powerful tool for studying synaptic integration and plasticity, it is limited by the small number of neurons that can be recorded simultaneously in vitro and by the technical difficulty of intracellular recording in vivo. One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons. These techniques have the potential to reveal large-scale connectivity structure based on the spike timing alone. However, the interpretation of functional connectivity is often approximate, since only a small fraction of presynaptic inputs are typically observed. Here we use in vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled. In experiments with partially-defined input, we inject a single simulated input with known amplitude on a background of fluctuating noise. In a fully-defined input paradigm, we then control the synaptic weights and timing of many simulated presynaptic neurons. By analyzing the firing of neurons in response to these artificial inputs, we ask 1) How does functional connectivity inferred from spikes relate to simulated synaptic input? and 2) What are the limitations of connectivity inference? We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions. Detectability depends on input amplitude and output firing rate, and excitatory inputs are detected more readily than inhibitory. Moreover, as we model increasing numbers of presynaptic inputs, we are able to estimate connection strengths more accurately and detect the presence of connections more quickly. These results illustrate the possibilities and outline the limits of inferring synaptic input from spikes.

Cortical Specializations Underlying Fast Computations

The time course of behaviorally relevant environmental events sets temporal constraints on neuronal processing. How does the mammalian brain make use of the increasingly complex networks of the neocortex, while making decisions and executing behavioral reactions within a reasonable time? The key parameter determining the speed of computations in neuronal networks is a time interval that neuronal ensembles need to process changes at their input and communicate results of this processing to downstream neurons. Theoretical analysis identified basic requirements for fast processing: use of neuronal populations for encoding, background activity, and fast onset dynamics of action potentials in neurons. Experimental evidence shows that populations of neocortical neurons fulfil these requirements. Indeed, they can change firing rate in response to input perturbations very quickly, within 1 to 3 ms, and encode high-frequency components of the input by phase-locking their spiking to frequencies up to 300 to 1000 Hz. This implies that time unit of computations by cortical ensembles is only few, 1 to 3 ms, which is considerably faster than the membrane time constant of individual neurons. The ability of cortical neuronal ensembles to communicate on a millisecond time scale allows for complex, multiple-step processing and precise coordination of neuronal activity in parallel processing streams, while keeping the speed of behavioral reactions within environmentally set temporal constraints.

Modulation of motoneuron firing by recurrent inhibition in the adult rat in vivo

Recent reports show that synaptic inhibition can modulate postsynaptic spike timing without having strong effects on firing rate. Thus synaptic inhibition can achieve multiplicity in neural circuit operation through variable modulation of postsynaptic firing rate vs. timing. We tested this possibility for recurrent inhibition (RI) of spinal motoneurons. In in vivo electrophysiological studies of adult Wistar rats anesthetized by isoflurane, we examined repetitive firing of individual lumbosacral motoneurons recorded in current clamp and modulated by synchronous antidromic electrical stimulation of multiple motor axons and their centrally projecting collateral branches. Antidromic stimulation produced recurrent inhibitory postsynaptic potentials (RIPSPs) having properties similar to those detailed in the cat. Although synchronous RI produced marked short-term modulation of motoneuron spike timing and instantaneous firing rate, there was little or no suppression of average firing rate. The bias in firing modulation of timing over average rate was observed even for high-frequency RI stimulation (100 Hz), perhaps because of the brevity of RIPSPs, which were more than twofold shorter during motoneuron firing compared with rest. These findings demonstrate that RI in the mammalian spinal cord has the capacity to support and not impede heightened motor pool activity, possibly during rapid, forceful movements.

Peripheral chemoreceptors tune inspiratory drive via tonic expiratory neuron hubs in the medullary ventral respiratory column network

Models of brain stem ventral respiratory column (VRC) circuits typically emphasize populations of neurons, each active during a particular phase of the respiratory cycle. We have proposed that "tonic" pericolumnar expiratory (t-E) neurons tune breathing during baroreceptor-evoked reductions and central chemoreceptor-evoked enhancements of inspiratory (I) drive. The aims of this study were to further characterize the coordinated activity of t-E neurons and test the hypothesis that peripheral chemoreceptors also modulate drive via inhibition of t-E neurons and disinhibition of their inspiratory neuron targets. Spike trains of 828 VRC neurons were acquired by multielectrode arrays along with phrenic nerve signals from 22 decerebrate, vagotomized, neuromuscularly blocked, artificially ventilated adult cats. Forty-eight of 191 t-E neurons fired synchronously with another t-E neuron as indicated by cross-correlogram central peaks; 32 of the 39 synchronous pairs were elements of groups with mutual pairwise correlations. Gravitational clustering identified fluctuations in t-E neuron synchrony. A network model supported the prediction that inhibitory populations with spike synchrony reduce target neuron firing probabilities, resulting in offset or central correlogram troughs. In five animals, stimulation of carotid chemoreceptors evoked changes in the firing rates of 179 of 240 neurons. Thirty-two neuron pairs had correlogram troughs consistent with convergent and divergent t-E inhibition of I cells and disinhibitory enhancement of drive. Four of 10 t-E neurons that responded to sequential stimulation of peripheral and central chemoreceptors triggered 25 cross-correlograms with offset features. The results support the hypothesis that multiple afferent systems dynamically tune inspiratory drive in part via coordinated t-E neurons.

Statistically rigorous calculations do not support common input and long-term synchronization of motor-unit firings

Over the past four decades, various methods have been implemented to measure synchronization of motor-unit firings. In this work, we provide evidence that prior reports of the existence of universal common inputs to all motoneurons and the presence of long-term synchronization are misleading, because they did not use sufficiently rigorous statistical tests to detect synchronization. We developed a statistically based method (SigMax) for computing synchronization and tested it with data from 17,736 motor-unit pairs containing 1,035,225 firing instances from the first dorsal interosseous and vastus lateralis muscles--a data set one order of magnitude greater than that reported in previous studies. Only firing data, obtained from surface electromyographic signal decomposition with >95% accuracy, were used in the study. The data were not subjectively selected in any manner. Because of the size of our data set and the statistical rigor inherent to SigMax, we have confidence that the synchronization values that we calculated provide an improved estimate of physiologically driven synchronization. Compared with three other commonly used techniques, ours revealed three types of discrepancies that result from failing to use sufficient statistical tests necessary to detect synchronization. 1) On average, the z-score method falsely detected synchronization at 16 separate latencies in each motor-unit pair. 2) The cumulative sum method missed one out of every four synchronization identifications found by SigMax. 3) The common input assumption method identified synchronization from 100% of motor-unit pairs studied. SigMax revealed that only 50% of motor-unit pairs actually manifested synchronization.

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.