# Journal of Computational Neuroscience This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

**Inference of synaptic connectivity and external variability in neural microcircuits**

AbstractA major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activityin vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. Forin vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many...

**Source: **Journal of Computational Neuroscience - February 21, 2020 **Category: **Neuroscience **Source Type: **research

**Multiscale relevance and informative encoding in neuronal spike trains**

AbstractNeuronal 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 anya priori covariate or invoking ...

**Source: **Journal of Computational Neuroscience - January 28, 2020 **Category: **Neuroscience **Source Type: **research

**Transient neocortical gamma oscillations induced by neuronal response modulation**

AbstractIn this paper a mean field model of spatio-temporal electroencephalographic activity in the neocortex is used to computationally study the emergence of neocortical gamma oscillations as a result of neuronal response modulation. It is shown using a numerical bifurcation analysis that gamma oscillations emerge robustly in the solutions of the model and transition to beta oscillations through coordinated modulation of the responsiveness of inhibitory and excitatory neuronal populations. The spatio-temporal pattern of the propagation of these oscillations across the neocortex is illustrated by solving the equations of ...

**Source: **Journal of Computational Neuroscience - January 28, 2020 **Category: **Neuroscience **Source Type: **research

**A calcium-influx-dependent plasticity model exhibiting multiple STDP curves**

AbstractHebbian plasticity means that if the firing of two neurons is correlated, then their connection is strengthened. Conversely, uncorrelated firing causes a decrease in synaptic strength. Spike-timing-dependent plasticity (STDP) represents one instantiation of Hebbian plasticity. Under STDP, synaptic changes depend on the relative timing of the pre- and post-synaptic firing. By inducing pre- and post-synaptic firing at different relative times the STDP curves of many neurons have been determined, and it has been found that there are different curves for different neuron types or synaptic sites. Biophysically, strength...

**Source: **Journal of Computational Neuroscience - January 24, 2020 **Category: **Neuroscience **Source Type: **research

**A general method to generate artificial spike train populations matching recorded neurons**

AbstractWe developed a general method to generate populations of artificial spike trains (ASTs) that match the statistics of recorded neurons. The method is based on computing a Gaussian local rate function of the recorded spike trains, which results in rate templates from which ASTs are drawn as gamma distributed processes with a refractory period. Multiple instances of spike trains can be sampled from the same rate templates. Importantly, we can manipulate rate-covariances between spike trains by performing simple algorithmic transformations on the rate templates, such as filtering or amplifying specific frequency bands,...

**Source: **Journal of Computational Neuroscience - January 23, 2020 **Category: **Neuroscience **Source Type: **research

**Fast simulation of extracellular action potential signatures based on a morphological filtering approximation**

AbstractSimulating extracellular recordings of neuronal populations is an important and challenging task both for understanding the nature and relationships between extracellular field potentials at different scales, and for the validation of methodological tools for signal analysis such as spike detection and sorting algorithms. Detailed neuronal multicompartmental models with active or passive compartments are commonly used in this objective. Although using such realistic NEURON models could lead to realistic extracellular potentials, it may require a high computational burden making the simulation of large populations d...

**Source: **Journal of Computational Neuroscience - January 17, 2020 **Category: **Neuroscience **Source Type: **research

**Oscillations and concentration dynamics of brain tissue oxygen in neonates and adults**

AbstractThe brain is a metabolically demanding organ and its health directly depends on brain oxygen dynamics to prevent hypoxia and ischemia. Localized brain tissue oxygen is characterized by a baseline level combined with spontaneous oscillations. These oscillations are attributed to spontaneous changes of vascular tone at the level of arterioles and their frequencies depend on age. Specifically, lower frequencies are more typical for neonates than for adults. We have built a mathematical model which analyses the diffusion abilities of oxygen based on the frequency of source brain oxygen oscillations and neuronal demand....

**Source: **Journal of Computational Neuroscience - January 8, 2020 **Category: **Neuroscience **Source Type: **research

**Spatiotemporal model of tripartite synapse with perinodal astrocytic process**

AbstractInformation transfer may not be limited only to synapses. Therefore, the processes and dynamics of biological neuron-astrocyte coupling and intercellular interaction within this domain are worth investigating. Existing models of tripartite synapse consider an astrocyte as a point process. Here, we extended the tripartite synapse model by considering the astrocytic processes (synaptic and perinodal) as compartments. The scattered extrinsic signals in the extracellular space and the presence of calcium stores in different astrocytic sites create local transient [Ca2+]. We investigated the Ca2+ dynamics and found that...

**Source: **Journal of Computational Neuroscience - December 3, 2019 **Category: **Neuroscience **Source Type: **research

**Analyzing dynamic decision-making models using Chapman-Kolmogorov equations**

AbstractDecision-making in dynamic environments typically requires adaptive evidence accumulation that weights new evidence more heavily than old observations. Recent experimental studies of dynamic decision tasks require subjects to make decisions for which the correct choice switches stochastically throughout a single trial. In such cases, an ideal observer ’s belief is described by an evolution equation that is doubly stochastic, reflecting stochasticity in the both observations and environmental changes. In these contexts, we show that the probability density of the belief can be represented using differential Ch...

**Source: **Journal of Computational Neuroscience - November 16, 2019 **Category: **Neuroscience **Source Type: **research

**Reducing variability in motor cortex activity at a resting state by extracellular GABA for reliable perceptual decision-making**

AbstractInteraction between sensory and motor cortices is crucial for perceptual decision-making, in which intracortical inhibition might have an important role. We simulated a neural network model consisting of a sensory network (NS) and a motor network (NM) to elucidate the significance of their interaction in perceptual decision-making in association with the level of GABA in extracellular space: extracellular GABA concentration. Extracellular GABA molecules acted on extrasynaptic receptors embedded in membranes of pyramidal cells and suppressed them. A reduction in extracellular GABA concentration either in NS or NM in...

**Source: **Journal of Computational Neuroscience - November 13, 2019 **Category: **Neuroscience **Source Type: **research

**Spontaneous synaptic drive in detrusor smooth muscle: computational investigation and implications for urinary bladder function**

AbstractThe detrusor, a key component of the urinary bladder wall, is a densely innervated syncytial smooth muscle tissue. Random spontaneous release of neurotransmitter at neuromuscular junctions (NMJs) in the detrusor gives rise to spontaneous excitatory junction potentials (SEJPs). These sub-threshold passive signals not only offer insights into the syncytial nature of the tissue, their spatio-temporal integration is critical to the generation of spontaneous neurogenic action potentials which lead to focal contractions during the filling phase of the bladder. Given the structural complexity and the contractile nature of...

**Source: **Journal of Computational Neuroscience - November 12, 2019 **Category: **Neuroscience **Source Type: **research

**Reduced order models of myelinated axonal compartments**

AbstractThe paper presents a hierarchical series of computational models for myelinated axonal compartments. Three classes of models are considered, either with distributed parameters (2.5D EQS –ElectroQuasi Static, 1D TL-Transmission Lines) or with lumped parameters (0D). They are systematically analyzed with both analytical and numerical approaches, the main goal being to identify the best procedure for order reduction of each case. An appropriate error estimator is proposed in order t o assess the accuracy of the models. This is the foundation of a procedure able to find the simplest reduced model having an impose...

**Source: **Journal of Computational Neuroscience - October 28, 2019 **Category: **Neuroscience **Source Type: **research

**Modeling cortical spreading depression induced by the hyperactivity of interneurons**

AbstractCortical spreading depression (CSD) is a wave of transient intense neuronal firing leading to a long lasting depolarizing block of neuronal activity. It is a proposed pathological mechanism of migraine with aura. Some forms of migraine are associated with a genetic mutation of the Nav1.1 channel, resulting in its gain of function and implying hyperexcitability of interneurons. This leads to the counterintuitive hypothesis that intense firing of interneurons can cause CSD ignition. To test this hypothesis in silico, we developed a computational model of an E-I pair (a pyramidal cell and an interneuron), in which the...

**Source: **Journal of Computational Neuroscience - October 17, 2019 **Category: **Neuroscience **Source Type: **research

**Correction to: From receptive profiles to a metric model of V1**

The authors would like to note an omission, in the published paper, of the Matlab code initially included as Electronic Supplementary Material. Therefore, we hereby re-submit the code in question. (Source: Journal of Computational Neuroscience)

**Source: **Journal of Computational Neuroscience - September 14, 2019 **Category: **Neuroscience **Source Type: **research

**A novel neural computational model of generalized periodic discharges in acute hepatic encephalopathy**

AbstractAcute hepatic encephalopathy (AHE) due to acute liver failure is a common form of delirium, a state of confusion, impaired attention, and decreased arousal. The electroencephalogram (EEG) in AHE often exhibits a striking abnormal pattern of brain activity, which epileptiform discharges repeat in a regular repeating pattern. This pattern is known as generalized periodic discharges, or triphasic-waves (TPWs). While much is known about the neurophysiological mechanisms underlying AHE, how these mechanisms relate to TPWs is poorly understood. In order to develop hypotheses how TPWs arise, our work builds a computationa...

**Source: **Journal of Computational Neuroscience - September 11, 2019 **Category: **Neuroscience **Source Type: **research

**Mitochondrial dysfunction and role in spreading depolarization and seizure**

AbstractThe effect of pathological phenomena such as epileptic seizures and spreading depolarization (SD) on mitochondria and the potential feedback of mitochondrial dysfunction into the dynamics of those phenomena are complex and difficult to study experimentally due to the simultaneous changes in many variables governing neuronal behavior. By combining a model that accounts for a wide range of neuronal behaviors including seizures, normoxic SD, and hypoxic SD (HSD), together with a detailed model of mitochondrial function and intracellular Ca2+ dynamics, we investigate mitochondrial dysfunction and its potential role in ...

**Source: **Journal of Computational Neuroscience - September 11, 2019 **Category: **Neuroscience **Source Type: **research

**Introducing double bouquet cells into a modular cortical associative memory model**

We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale ’s principle. We show that learning ability and resulting effec...

**Source: **Journal of Computational Neuroscience - September 9, 2019 **Category: **Neuroscience **Source Type: **research

**Biophysically interpretable inference of single neuron dynamics**

AbstractIdentification of key ionic channel contributors to the overall dynamics of a neuron is an important problem in experimental neuroscience. Such a problem is challenging since even in the best cases, identification relies on noisy recordings of membrane potential only, and strict inversion to the constituent channel dynamics is mathematically ill-posed. In this work, we develop a biophysically interpretable, learning-based strategy for data-driven inference of neuronal dynamics. In particular, we propose two optimization frameworks to learn and approximate neural dynamics from an observed voltage trajectory. In both...

**Source: **Journal of Computational Neuroscience - August 29, 2019 **Category: **Neuroscience **Source Type: **research

**Electrodiffusion models of synaptic potentials in dendritic spines**

AbstractThe biophysical properties of dendritic spines play a critical role in neuronal integration but are still poorly understood, due to experimental difficulties in accessing them. Spine biophysics has been traditionally explored using theoretical models based on cable theory. However, cable theory generally assumes that concentration changes associated with ionic currents are negligible and, therefore, ignores electrodiffusion,i.e. the interaction between electric fields and ionic diffusion. This assumption, while true for large neuronal compartments, could be incorrect when applied to femto-liter size structures such...

**Source: **Journal of Computational Neuroscience - August 13, 2019 **Category: **Neuroscience **Source Type: **research

**Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study**

AbstractElectrophysiological signals (electroencephalography, EEG, and magnetoencephalography, MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with diffe...

**Source: **Journal of Computational Neuroscience - July 11, 2019 **Category: **Neuroscience **Source Type: **research

**Modeling grid fields instead of modeling grid cells**

AbstractA neuron ’s firing correlates are defined as the features of the external world to which its activity is correlated. In many parts of the brain, neurons have quite simple such firing correlates. A striking example are grid cells in the rodent medial entorhinal cortex: their activity correlates with the ani mal’s position in space, defining ‘grid fields’ arranged with a remarkable periodicity. Here, we show that the organization and evolution of grid fields relate very simply to physical space. To do so, we use an effective model and consider grid fields as point objects (particles) moving ar...

**Source: **Journal of Computational Neuroscience - July 8, 2019 **Category: **Neuroscience **Source Type: **research

**How to correctly quantify neuronal phase-response curves from noisy recordings**

AbstractAt the level of individual neurons, various coding properties can be inferred from the input-output relationship of a cell. For small inputs, this relation is captured by the phase-response curve (PRC), which measures the effect of a small perturbation on the timing of the subsequent spike. Experimentally, however, an accurate experimental estimation of PRCs is challenging. Despite elaborate measurement efforts, experimental PRC estimates often cannot be related to those from modeling studies. In particular, experimental PRCs rarely resemble the characteristic theoretical PRC expected close to spike initiation, whi...

**Source: **Journal of Computational Neuroscience - June 24, 2019 **Category: **Neuroscience **Source Type: **research

**Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries**

AbstractWe introduce a computational model for the cellular level effects of firing rate filtering due to the major forms of neuronal injury, including demyelination and axonal swellings. Based upon experimental and computational observations, we posit simple phenomenological input/output rules describing spike train distortions and demonstrate that slow-gamma frequencies in the 38 –41 Hz range emerge as the most robust to injury. Our signal-processing model allows us to derive firing rate filters at the cellular level for impaired neural activity with minimal assumptions. Specifically, we model eight experimentally ...

**Source: **Journal of Computational Neuroscience - June 4, 2019 **Category: **Neuroscience **Source Type: **research

**Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity**

AbstractWe demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network contains units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections rendering each unit bi-stable. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding o...

**Source: **Journal of Computational Neuroscience - May 27, 2019 **Category: **Neuroscience **Source Type: **research

**Neural network model of an amphibian ventilatory central pattern generator**

AbstractThe neuronal multiunit model presented here is a formal model of the central pattern generator (CPG) of the amphibian ventilatory neural network, inspired by experimental data fromPelophylax ridibundus. The kernel of the CPG consists of three pacemakers and two follower neurons (buccal and lung respectively). This kernel is connected to a chain of excitatory and inhibitory neurons organized in loops. Simulations are performed with Izhikevich-type neurons. When driven by the buccal follower, the excitatory neurons transmit and reorganize the follower activity pattern along the chain, and when driven by the lung foll...

**Source: **Journal of Computational Neuroscience - May 22, 2019 **Category: **Neuroscience **Source Type: **research

**Short term memory properties of sensory neural architectures**

AbstractA functional role of the cerebral cortex is to form and hold representations of the sensory world for behavioral purposes. This is achieved by a sheet of neurons, organized in modules called cortical columns, that receives inputs in a peculiar manner, with only a few neurons driven by sensory inputs through thalamic projections, and a vast majority of neurons receiving mainly cortical inputs. How should cortical modules be organized, with respect to sensory inputs, in order for the cortex to efficiently hold sensory representations in memory? To address this question we investigate the memory performance of trees o...

**Source: **Journal of Computational Neuroscience - May 18, 2019 **Category: **Neuroscience **Source Type: **research

**A computational model of large conductance voltage and calcium activated potassium channels: implications for calcium dynamics and electrophysiology in detrusor smooth muscle cells**

AbstractThe large conductance voltage and calcium activated potassium (BK) channels play a crucial role in regulating the excitability of detrusor smooth muscle, which lines the wall of the urinary bladder. These channels have been widely characterized in terms of their molecular structure, pharmacology and electrophysiology. They control the repolarising and hyperpolarising phases of the action potential, thereby regulating the firing frequency and contraction profiles of the smooth muscle. Several groups have reported varied profiles of BK currents and I-V curves under similar experimental conditions. However, no single ...

**Source: **Journal of Computational Neuroscience - April 25, 2019 **Category: **Neuroscience **Source Type: **research

**From receptive profiles to a metric model of V1**

AbstractIn this work we show how to construct connectivity kernels induced by the receptive profiles of simple cells of the primary visual cortex (V1). These kernels are directly defined by the shape of such profiles: this provides a metric model for the functional architecture of V1, whose global geometry is determined by the reciprocal interactions between local elements. Our construction adapts to any bank of filters chosen to represent a set of receptive profiles, since it does not require any structure on the parameterization of the family. The connectivity kernel that we define carries a geometrical structure consist...

**Source: **Journal of Computational Neuroscience - April 12, 2019 **Category: **Neuroscience **Source Type: **research

**Slowdown of BCM plasticity with many synapses**

We present a mathematical analysis of the slowdown that shows also how the slowdown can be avoided. (Source: Journal of Computational Neuroscience)

**Source: **Journal of Computational Neuroscience - April 5, 2019 **Category: **Neuroscience **Source Type: **research

**Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations**

AbstractSeveral neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these...

**Source: **Journal of Computational Neuroscience - March 20, 2019 **Category: **Neuroscience **Source Type: **research

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

AbstractHomogeneously structured, fluctuation-driven networks of spiking neurons can exhibit a wide variety of dynamical behaviors, ranging from homogeneity to synchrony. We extend our partitioned-ensemble average (PEA) formalism proposed in Zhang et al. (Journal of Computational Neuroscience, 37(1), 81 –104,2014a) to systematically coarse grain the heterogeneous dynamics of strongly coupled, conductance-based integrate-and-fire neuronal networks. The population dynamics models derived here successfully capture the so-called multiple-firing events (MFEs), which emerge naturally in fluctuation-driven networks of stron...

**Source: **Journal of Computational Neuroscience - February 16, 2019 **Category: **Neuroscience **Source Type: **research

**Outgrowing seizures in Childhood Absence Epilepsy: time delays and bistability**

AbstractWe formulate a conductance-based model for a 3-neuron motif associated with Childhood Absence Epilepsy (CAE). The motif consists of neurons from the thalamic relay (TC) and reticular nuclei (RT) and the cortex (CT). We focus on a genetic defect common to the mouse homolog of CAE which is associated with loss of GABAA receptors on the TC neuron, and the fact that myelination of axons as children age can increase the conduction velocity between neurons. We show the combination of low GABAA mediated inhibition of TC neurons and the long corticothalamic loop delay gives rise to a variety of complex dynamics in the moti...

**Source: **Journal of Computational Neuroscience - February 9, 2019 **Category: **Neuroscience **Source Type: **research

**Emerging techniques in statistical analysis of neural data**

(Source: Journal of Computational Neuroscience)

**Source: **Journal of Computational Neuroscience - February 9, 2019 **Category: **Neuroscience **Source Type: **research

**Network structure and input integration in competing firing rate models for decision-making**

AbstractMaking a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characterizing decision making in the face of a large set of alternatives remains challenging. We explore this issue by formulating a scalable mechanistic network model for decision making and analyzing the dynamics evoked given various potential network structures. In the case of a fully-connected network, we provide an analytical characterization of the model fixed points and t...

**Source: **Journal of Computational Neuroscience - January 19, 2019 **Category: **Neuroscience **Source Type: **research

**Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity**

AbstractMany neurons possess dendrites enriched with sodium channels and are capable of generating action potentials. However, the role of dendritic sodium spikes remain unclear. Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the ...

**Source: **Journal of Computational Neuroscience - December 13, 2018 **Category: **Neuroscience **Source Type: **research

**An exploratory data analysis method for identifying brain regions and frequencies of interest from large-scale neural recordings**

AbstractHigh-resolution whole brain recordings have the potential to uncover unknown functionality but also present the challenge of how to find such associations between brain and behavior when presented with a large number of regions and spectral frequencies. In this paper, we propose an exploratory data analysis method that sorts through a massive quantity of multivariate neural recordings to quickly extract a subset of brain regions and frequencies that encode behavior. This approach combines existing tools and exploits low-rank approximation of matrices withouta priori selection of regions and frequency bands for anal...

**Source: **Journal of Computational Neuroscience - December 4, 2018 **Category: **Neuroscience **Source Type: **research

**Motor imagery and mental fatigue: inter-relationship and EEG based estimation**

This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue. The growth of fatigue was monitored using the Kernel Partial Least Square (KPLS) algorithm on the remaining 6 participants which shows that MI induces substantial mental fatigue. Statistical analysis of the effect of fatigue on motor imagery performance shows that high fatigue ...

**Source: **Journal of Computational Neuroscience - November 29, 2018 **Category: **Neuroscience **Source Type: **research

**Modeling the interactions between stimulation and physiologically induced APs in a mammalian nerve fiber: dependence on frequency and fiber diameter**

In this study, we aim to quantify the effects of stimulation frequency and fiber diameter on AP interactions involving collisions and loss of excitability. We constructed a mechanistic model of a myelinated nerve fiber receiving two inputs: the underlying physiological activity at the terminal end of the fiber, and an external stimulus applied to the middle of the fiber. We define conduction reliability as the percentage of physiological APs that make it to the somatic end of the nerve fiber. At low input frequencies, conduction reliability is greater than 95% and decreases with increasing frequency due to an increase in A...

**Source: **Journal of Computational Neuroscience - November 15, 2018 **Category: **Neuroscience **Source Type: **research

**A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations**

AbstractThe mechanisms underlying the broad variety of oscillatory rhythms measured in the hippocampus during the sleep-wake cycle are not yet fully understood. In this article, we propose a computational model of the hippocampal formation based on a realistic topology and synaptic connectivity, and we analyze the effect of different changes on the network, namely the variation of synaptic conductances, the variations of the CAN channel conductance and the variation of inputs. By using a detailed simulation of intracerebral recordings, we show that this is able to reproduce both the theta-nested gamma oscillations that are...

**Source: **Journal of Computational Neuroscience - October 31, 2018 **Category: **Neuroscience **Source Type: **research

**Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail**

AbstractReplicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that mod el replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve...

**Source: **Journal of Computational Neuroscience - October 31, 2018 **Category: **Neuroscience **Source Type: **research

**An intracerebral exploration of functional connectivity during word production**

AbstractLanguage is mediated by pathways connecting distant brain regions that have diverse functional roles. For word production, the network includes a ventral pathway, connecting temporal and inferior frontal regions, and a dorsal pathway, connecting parietal and frontal regions. Despite the importance of word production for scientific and clinical purposes, the functional connectivity underlying this task has received relatively limited attention, and mostly from techniques limited in either spatial or temporal resolution. Here, we exploited data obtained from depth intra-cerebral electrodes stereotactically implanted ...

**Source: **Journal of Computational Neuroscience - October 13, 2018 **Category: **Neuroscience **Source Type: **research

**Predicting state transitions in brain dynamics through spectral difference of phase-space graphs**

AbstractNetworks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy. This res...

**Source: **Journal of Computational Neuroscience - October 12, 2018 **Category: **Neuroscience **Source Type: **research

**A numerical simulation of neural fields on curved geometries**

AbstractDespite the highly convoluted nature of the human brain, neural field models typically treat the cortex as a planar two-dimensional sheet of ne;urons. Here, we present an approach for solving neural field equations on surfaces more akin to the cortical geometries typically obtained from neuroimaging data. Our approach involves solving the integral form of the partial integro-differential equation directly using collocation techniques alongside efficient numerical procedures for determining geodesic distances between neural units. To illustrate our methods, we study localised activity patterns in a two-dimensional n...

**Source: **Journal of Computational Neuroscience - October 11, 2018 **Category: **Neuroscience **Source Type: **research

**Linear-nonlinear-time-warp-poisson models of neural activity**

AbstractProminent models of spike trains assume only one source of variability – stochastic (Poisson) spiking – when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When ap...

**Source: **Journal of Computational Neuroscience - October 8, 2018 **Category: **Neuroscience **Source Type: **research

**A common goodness-of-fit framework for neural population models using marked point process time-rescaling**

AbstractA critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more rece...

**Source: **Journal of Computational Neuroscience - October 8, 2018 **Category: **Neuroscience **Source Type: **research

**Stability of point process spiking neuron models**

AbstractPoint process regression models, based on generalized linear model (GLM) technology, have been widely used for spike train analysis, but a recent paper by Gerhard et al. described a kind of instability, in which fitted models can generate simulated spike trains with explosive firing rates. We analyze the problem by extending the methods of Gerhard et al. First, we improve their instability diagnostic and extend it to a wider class of models. Next, we point out some common situations in which instability can be traced to model lack of fit. Finally, we investigate distinctions between models that use a single filter ...

**Source: **Journal of Computational Neuroscience - September 15, 2018 **Category: **Neuroscience **Source Type: **research

**Ensembles of change-point detectors: implications for real-time BMI applications**

AbstractBrain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed “ensembles of change-point detectors” (ECPDs) integrate multiple decisions from independent detectors, whic...

**Source: **Journal of Computational Neuroscience - September 12, 2018 **Category: **Neuroscience **Source Type: **research

**Adjusted regularization of cortical covariance**

AbstractIt is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an\(L_{1}\) penalty. However, the partial correlations found in neural spike count data a...

**Source: **Journal of Computational Neuroscience - September 6, 2018 **Category: **Neuroscience **Source Type: **research