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.

Neural manifold analysis of brain circuit dynamics in health and disease
AbstractRecent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural sub...
Source: Journal of Computational Neuroscience - December 16, 2022 Category: Neuroscience Source Type: research

A general pattern of non-spiking neuron dynamics under the effect of potassium and calcium channel modifications
AbstractElectrical activity of excitable cells results from ion exchanges through cell membranes, so that genetic or epigenetic changes in genes encoding ion channels are likely to affect neuronal electrical signaling throughout the brain. There is a large literature on the effect of variations in ion channels on the dynamics of spiking neurons that represent the main type of neurons found in the vertebrate nervous systems. Nevertheless, non-spiking neurons are also ubiquitous in many nervous tissues and play a critical role in the processing of some sensory systems. To our knowledge, however, how conductance variations af...
Source: Journal of Computational Neuroscience - November 12, 2022 Category: Neuroscience Source Type: research

Scale free avalanches in excitatory-inhibitory populations of spiking neurons with conductance based synaptic currents
AbstractWe investigate spontaneous critical dynamics of excitatory and inhibitory (EI) sparsely connected populations of spiking leaky integrate-and-fire neurons with conductance-based synapses. We use a bottom-up approach to derive a single neuron gain function and a linear Poisson neuron approximation which we use to study mean-field dynamics of the EI population and its bifurcations. In the low firing rate regime, the quiescent state loses stability due to saddle-node or Hopf bifurcations. In particular, at the Bogdanov-Takens (BT) bifurcation point which is the intersection of the Hopf bifurcation and the saddle-node b...
Source: Journal of Computational Neuroscience - October 25, 2022 Category: Neuroscience Source Type: research

The steady state and response to a periodic stimulation of the firing rate for a theta neuron with correlated noise
AbstractThe stochastic activity of neurons is caused by various sources of correlated fluctuations and can be described in terms of simplified, yet biophysically grounded, integrate-and-fire models. One paradigmatic model is the quadratic integrate-and-fire model and its equivalent phase description by the theta neuron. Here we study the theta neuron model driven by a correlated Ornstein-Uhlenbeck noise and by periodic stimuli. We apply the matrix-continued-fraction method to the associated Fokker-Planck equation to develop an efficient numerical scheme to determine the stationary firing rate as well as the stimulus-induce...
Source: Journal of Computational Neuroscience - October 22, 2022 Category: Neuroscience Source Type: research

Intersegmental coordination of the central pattern generator via interleaved electrical and chemical synapses in zebrafish spinal cord
AbstractA significant component of the repetitive dynamics during locomotion in vertebrates is generated within the spinal cord. The legged locomotion of mammals is most likely controled by a hierarchical, multi-layer spinal network structure, while the axial circuitry generating the undulatory swimming motion of animals like lamprey is thought to have only a single layer in each segment. Recent experiments have suggested a hybrid network structure in zebrafish larvae in which two types of excitatory interneurons (V2a-I and V2a-II) both make first-order connections to the brain and last-order connections to the motor pool....
Source: Journal of Computational Neuroscience - October 13, 2022 Category: Neuroscience Source Type: research

Computational modeling predicts regulation of central pattern generator oscillations by size and density of the underlying heterogenous network
AbstractCentral pattern generators are characterized by a heterogeneous cellular composition, with different cell types playing distinct roles in the production and transmission of rhythmic signals. However, little is known about the functional implications of individual variation in the relative distributions of cells and their connectivity patterns. Here, we addressed this question through a combination of morphological data analysis and computational modeling, using the pacemaker nucleus of the weakly electric fishApteronotus leptorhynchus as case study. A neural network comprised of 60 –110 interconnected pacemaker c...
Source: Journal of Computational Neuroscience - October 6, 2022 Category: Neuroscience Source Type: research

Topological dissimilarities of hierarchical resting networks in type 2 diabetes mellitus and obesity
In conclusion, we find the usage of complex analysis of large-scale networks suitable for diabetes instead of focusing on specific changes in brain function. (Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - September 2, 2022 Category: Neuroscience Source Type: research

Dynamical response of Autaptic Izhikevich Neuron disturbed by Gaussian white noise
AbstractUsing the improved memristive Izhikevich neuron model, the effects of autaptic connection as well as electromagnetic induction are studied on the dynamical behavior of neuronal spiking. Using bifurcation analysis for membrane potentials, the effects of autaptic and electromagnetic parameters on the mode transition in electrical activities of the neuron model are investigated. Furthermore, white Gaussian noise is considered in the neuron model, to evaluate the effect of electromagnetic disturbance on the firing pattern of the neuron using the coefficient of variation. The bifurcation diagram versus autaptic conducta...
Source: Journal of Computational Neuroscience - August 30, 2022 Category: Neuroscience Source Type: research

Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations
AbstractThe mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and sy...
Source: Journal of Computational Neuroscience - August 16, 2022 Category: Neuroscience Source Type: research

Dynamic branching in a neural network model for probabilistic prediction of sequences
AbstractAn important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuron...
Source: Journal of Computational Neuroscience - August 10, 2022 Category: Neuroscience Source Type: research

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
AbstractUnderstanding neural computation on the mechanistic level requires models of neurons and neuronal networks. To analyze such models one typically has to solve coupled ordinary differential equations (ODEs), which describe the dynamics of the underlying neural system. These ODEs are solved numerically with deterministic ODE solvers that yield single solutions with either no, or only a global scalar error indicator on precision. It can therefore be challenging to estimate the effect of numerical uncertainty on quantities of interest, such as spike-times and the number of spikes. To overcome this problem, we propose to...
Source: Journal of Computational Neuroscience - August 6, 2022 Category: Neuroscience Source Type: research

Temporal filters in response to presynaptic spike trains: interplay of cellular, synaptic and short-term plasticity time scales
AbstractTemporal filters, the ability of postsynaptic neurons to preferentially select certain presynaptic input patterns over others, have been shown to be associated with the notion of information filtering and coding of sensory inputs. Short-term plasticity (depression and facilitation; STP) has been proposed to be an important player in the generation of temporal filters. We carry out a systematic modeling, analysis and computational study to understand how characteristic postsynaptic (low-, high- and band-pass) temporal filters are generated in response to periodic presynaptic spike trains in the presence STP. We inve...
Source: Journal of Computational Neuroscience - July 23, 2022 Category: Neuroscience Source Type: research

Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks
AbstractReconstructing therecurrent structural connectivity of neuronal networks is a challenge crucial to address in characterizing neuronal computations. While directly measuring the detailed connectivity structure is generally prohibitive for large networks, we develop a novel framework for reverse-engineering large-scale recurrent network connectivity matrices from neuronal dynamics by utilizing the widespread sparsity of neuronal connections. We derive a linear input-output mapping that underlies the irregular dynamics of a model network composed of both excitatory and inhibitory integrate-and-fire neurons with pulse ...
Source: Journal of Computational Neuroscience - July 18, 2022 Category: Neuroscience Source Type: research

The role of astrocytes in place cell formation: A computational modeling study
AbstractPlace cells develop spatially-tuned receptive fields during the early stages of novel environment exploration. The generative mechanism underlying these spatially-selective responses remains largely elusive, but has been associated with theta rhythmicity. An important factor implicating the transformation of silent cells to place cells is a spatially-uniform depolarization that is mediated by a persistent sodium current. This neuronal current is modulated by extracellular calcium concentration, which, in turn, is actively controlled by astrocytes. However, there is no established relationship between the neuronal d...
Source: Journal of Computational Neuroscience - July 15, 2022 Category: Neuroscience Source Type: research

Exact mean-field models for spiking neural networks with adaptation
AbstractNetworks of spiking neurons with adaption have been shown to be able to reproduce a wide range of neural activities, including the emergent population bursting and spike synchrony that underpin brain disorders and normal function. Exact mean-field models derived from spiking neural networks are extremely valuable, as such models can be used to determine how individual neurons and the network they reside within interact to produce macroscopic network behaviours. In the paper, we derive and analyze a set of exact mean-field equations for the neural network with spike frequency adaptation. Specifically, our model is a...
Source: Journal of Computational Neuroscience - July 14, 2022 Category: Neuroscience Source Type: research