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.

Short-term depression and transient memory in sensory cortex
AbstractPersistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive fe...
Source: Journal of Computational Neuroscience - October 13, 2017 Category: Neuroscience Source Type: research

Stimulus-induced transitions between spike-wave discharges and spindles with the modulation of thalamic reticular nucleus
AbstractIt is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation. Specially, it is shown that stimulations a...
Source: Journal of Computational Neuroscience - September 22, 2017 Category: Neuroscience Source Type: research

A recurrent neural model for proto-object based contour integration and figure-ground segregation
AbstractVisual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use ofgrouping neurons whose activity represents tentative objects ( “proto-objects”) based on the integration of local feature information. Grouping neurons receive input from an organized set of local...
Source: Journal of Computational Neuroscience - September 19, 2017 Category: Neuroscience Source Type: research

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks
AbstractCorrelated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uni...
Source: Journal of Computational Neuroscience - September 12, 2017 Category: Neuroscience Source Type: research

Pre-processing and transfer entropy measures in motor neurons controlling limb movements
AbstractDirected information transfer measures are increasingly being employed in modeling neural system behavior due to their model-free approach, applicability to nonlinear and stochastic signals, and the potential to integrate repetitions of an experiment. Intracellular physiological recordings of graded synaptic potentials provide a number of additional challenges compared to spike signals due to non-stationary behaviour generated through extrinsic processes. We therefore propose a method to overcome this difficulty by using a preprocessing step based on Singular Spectrum Analysis (SSA) to remove nonlinear trends and d...
Source: Journal of Computational Neuroscience - August 9, 2017 Category: Neuroscience Source Type: research

A mean field model for movement induced changes in the beta rhythm
AbstractIn electrophysiological recordings of the brain, the transition from high amplitude to low amplitude signals are most likely caused by a change in the synchrony of underlying neuronal population firing patterns. Classic examples of such modulations are the strong stimulus-related oscillatory phenomena known as the movement related beta decrease (MRBD) and post-movement beta rebound (PMBR). A sharp decrease in neural oscillatory power is observed during movement (MRBD) followed by an increase above baseline on movement cessation (PMBR). MRBD and PMBR represent important neuroscientific phenomena which have been show...
Source: Journal of Computational Neuroscience - July 26, 2017 Category: Neuroscience Source Type: research

Decision-making neural circuits mediating social behaviors
AbstractWe propose a mathematical model of a continuous attractor network that controls social behaviors. The model is examined with bifurcation analysis and computer simulations. The results show that the model exhibits stable steady states and thresholds for steady state transitions corresponding to some experimentally observed behaviors, such as aggression control. The performance of the model and the relation with experimental evidence are discussed. (Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - June 29, 2017 Category: Neuroscience Source Type: research

The difficult legacy of Turing ’s wager
AbstractDescribing the human brain in mathematical terms is an important ambition of neuroscience research, yet the challenges remain considerable. It was Alan Turing, writing in 1950, who first sought to demonstrate how time-consuming such an undertaking would be. Through analogy to the computer program, Turing argued that arriving at a complete mathematical description of the mind would take well over a thousand years. In this opinion piece, we argue that — despite seventy years of progress in the field — his arguments remain both prescient and persuasive. (Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - June 22, 2017 Category: Neuroscience Source Type: research

Intermittency in the Hodgkin-Huxley model
AbstractWe show that action potentials in the Hodgkin-Huxley neuron model result from a type I intermittency phenomenon that occurs in the proximity of a saddle-node bifurcation of limit cycles. For the Hodgkin-Huxley spatially extended model, describing propagation of action potential along axons, we show the existence of type I intermittency and a new type of chaotic intermittency, as well as space propagating regular and chaotic diffusion waves. Chaotic intermittency occurs in the transition from a turbulent regime to the resting regime of the transmembrane potential and is characterised by the existence of a sequence o...
Source: Journal of Computational Neuroscience - June 14, 2017 Category: Neuroscience Source Type: research

Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise
AbstractA neuron receives input from other neurons via electrical pulses, so-called spikes. The pulse-like nature of the input is frequently neglected in analytical studies; instead, the input is usually approximated to be Gaussian. Recent experimental studies have shown, however, that an assumption underlying this approximation is often not met: Individual presynaptic spikes can have a significant effect on a neuron ’s dynamics. It is thus desirable to explicitly account for the pulse-like nature of neural input, i.e. consider neurons driven by a shot noise – a long-standing problem that is mathematically challenging....
Source: Journal of Computational Neuroscience - June 6, 2017 Category: Neuroscience Source Type: research

Optimal feedback control to describe multiple representations of primary motor cortex neurons
In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor out...
Source: Journal of Computational Neuroscience - June 1, 2017 Category: Neuroscience Source Type: research

Resonance modulation, annihilation and generation of anti-resonance and anti-phasonance in 3D neuronal systems: interplay of resonant and amplifying currents with slow dynamics
AbstractSubthreshold (membrane potential) resonance and phasonance (preferred amplitude and zero-phase responses to oscillatory inputs) in single neurons arise from the interaction between positive and negative feedback effects provided by relatively fast amplifying currents and slower resonant currents. In 2D neuronal systems, amplifying currents are required to be slave to voltage (instantaneously fast) for these phenomena to occur. In higher dimensional systems, additional currents operating at various effective time scales may modulate and annihilate existing resonances and generate antiresonance (minimum amplitude res...
Source: Journal of Computational Neuroscience - May 31, 2017 Category: Neuroscience Source Type: research

The influence of depolarization block on seizure-like activity in networks of excitatory and inhibitory neurons
AbstractThe inhibitory restraint necessary to suppress aberrant activity can fail when inhibitory neurons cease to generate action potentials as they enter depolarization block. We investigate possible bifurcation structures that arise at the onset of seizure-like activity resulting from depolarization block in inhibitory neurons. Networks of conductance-based excitatory and inhibitory neurons are simulated to characterize different types of transitions to the seizure state, and a mean field model is developed to verify the generality of the observed phenomena of excitatory-inhibitory dynamics. Specifically, the inhibitory...
Source: Journal of Computational Neuroscience - May 20, 2017 Category: Neuroscience Source Type: research

Tonic regulation of stationary asynchronous firing of a neural network
AbstractThe impact of tonic conductance upon population activity was investigated. An extra tonic transmembrane current through GABA-activated extrasynaptic GABAA-receptors was found to control stationary asynchronous firing both quantitatively and qualitatively. Quantitative regulation consisted in alterating a current level of stationary population activity while qualitative regulation manifested itself in appearance of resilient asynchronous spiking in case GABA reversal potential exceeded a certain threshold. The study was based on a modified rate model after Wilson and Cowan and backed up with a computer simulation of...
Source: Journal of Computational Neuroscience - May 16, 2017 Category: Neuroscience Source Type: research

Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings
AbstractHippocampus stores spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. Across different environments of similar geometry, these representations show strong orthogonality in CA3 of hippocampus, whereas in the CA1 subfield a considerable overlap between the maps can be seen. The lower orthogonality decreases reliability of various decoders developed in an attempt to identify which of the stored maps is active at the moment. Especially, the problem with decoding emerges with a need to analyze data at high temporal resolution. Here, we introduce a functio...
Source: Journal of Computational Neuroscience - May 8, 2017 Category: Neuroscience Source Type: research