Journal of Computational Neuroscience 
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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.Functional architecture of M1 cells encoding movement direction
AbstractIn this paper we propose a neurogeometrical model of the behaviour of cells of the arm area of the primary motor cortex (M1). We will mathematically express as a fiber bundle the hypercolumnar organization of this cortical area, first modelled by Georgopoulos (Georgopoulos et al., 1982; Georgopoulos, 2015). On this structure, we will consider the selective tuning of M1 neurons of kinematic variables of positions and directions of movement. We will then extend this model to encode the notion of fragments introduced by Hatsopoulos et al. (2007) which describes the selectivity of neurons to movement direction v...
Source: Journal of Computational Neuroscience - June 7, 2023 Category: Neuroscience Source Type: research
Transmission of delta band (0.5-4 Hz) oscillations from the globus pallidus to the substantia nigra pars reticulata in dopamine depletion
AbstractParkinson ’s disease (PD) and animal models of PD feature enhanced oscillations in several frequency bands in the basal ganglia (BG). Past research has emphasized the enhancement of 13-30 Hz beta oscillations. Recently, however, oscillations in the delta band (0.5-4 Hz) have been identified as a robust pred ictor of dopamine loss and motor dysfunction in several BG regions in mouse models of PD. In particular, delta oscillations in the substantia nigra pars reticulata (SNr) were shown to lead oscillations in motor cortex (M1) and persist under M1 lesion, but it is not clear where these oscillations are initially ...
Source: Journal of Computational Neuroscience - June 2, 2023 Category: Neuroscience Source Type: research
Hierarchical processing underpins competition in tactile perceptual bistability
This study addresses the need for a tactile rivalry model that captures the dynamics of perceptual alternations and that incorporates the structure of the somatosensory system. The model features hierarchical processing with two stages. The first and the second stages of model could be located at the secondary somatosensory cortex (area S2), or in higher areas driven by S2. The model captures dynamical features specific to the tactile rivalry percepts and produces general characteristics of perceptual rivalry: input strength dependence of dominance times (Levelt ’s proposition II), short-tailed skewness of dominance time...
Source: Journal of Computational Neuroscience - May 19, 2023 Category: Neuroscience Source Type: research
Comparing performance between a deep neural network and monkeys with bilateral removals of visual area TE in categorizing feature-ambiguous stimuli
AbstractIn the canonical view of visual processing the neural representation of complex objects emerges as visual information is integrated through a set of convergent, hierarchically organized processing stages, ending in the primate inferior temporal lobe. It seems reasonable to infer that visual perceptual categorization requires the integrity of anterior inferior temporal cortex (area TE). Many deep neural networks (DNNs) are structured to simulate the canonical view of hierarchical processing within the visual system. However, there are some discrepancies between DNNs and the primate brain. Here we evaluated the perfo...
Source: Journal of Computational Neuroscience - May 17, 2023 Category: Neuroscience Source Type: research
Improving a cortical pyramidal neuron model ’s classification performance on a real-world ecg dataset by extending inputs
AbstractPyramidal neurons display a variety of active conductivities and complex morphologies that support nonlinear dendritic computation. Given growing interest in understanding the ability of pyramidal neurons to classify real-world data, in our study we applied both a detailed pyramidal neuron model and the perceptron learning algorithm to classify real-world ECG data. We used Gray coding to generate spike patterns from ECG signals as well as investigated the classification performance of the pyramidal neuron ’s subcellular regions. Compared with the equivalent single-layer perceptron, the pyramidal neuron performed ...
Source: Journal of Computational Neuroscience - May 6, 2023 Category: Neuroscience Source Type: research
A biophysical and statistical modeling paradigm for connecting neural physiology and function
AbstractTo understand single neuron computation, it is necessary to know how specific physiological parameters affect neural spiking patterns that emerge in response to specific stimuli. Here we present a computational pipeline combining biophysical and statistical models that provides a link between variation in functional ion channel expression and changes in single neuron stimulus encoding. More specifically, we create a mapping from biophysical model parameters to stimulus encoding statistical model parameters. Biophysical models provide mechanistic insight, whereas statistical models can identify associations between ...
Source: Journal of Computational Neuroscience - May 4, 2023 Category: Neuroscience Source Type: research
Slow negative feedback enhances robustness of square-wave bursting
AbstractSquare-wave bursting is an activity pattern common to a variety of neuronal and endocrine cell models that has been linked to central pattern generation for respiration and other physiological functions. Many of the reduced mathematical models that exhibit square-wave bursting yield transitions to an alternative pseudo-plateau bursting pattern with small parameter changes. This susceptibility to activity change could represent a problematic feature in settings where the release events triggered by spike production are necessary for function. In this work, we analyze how model bursting and other activity patterns va...
Source: Journal of Computational Neuroscience - April 17, 2023 Category: Neuroscience Source Type: research
Responses in fast-spiking interneuron firing rates to parameter variations associated with degradation of perineuronal nets
AbstractThe perineuronal nets (PNNs) are sugar coated protein structures that encapsulate certain neurons in the brain, such as parvalbumin positive (PV) inhibitory neurons. As PNNs are theorized to act as a barrier to ion transport, they may effectively increase the membrane charge-separation distance, thereby affecting the membrane capacitance. Tewari et al. (2018) found that degradation of PNNs induced a 25%-50% increase in membrane capacitance\(c_\text {m}\) and a reduction in the firing rates of PV-cells. In the current work, we explore how changes in\(c_\text {m}\) affects the firing rate in a selection of computatio...
Source: Journal of Computational Neuroscience - April 14, 2023 Category: Neuroscience Source Type: research
Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate
(Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - March 11, 2023 Category: Neuroscience Source Type: research
Adaptive unscented Kalman filter for neuronal state and parameter estimation
AbstractData assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter implementations, the robust adaptive unscented Kalman filter (RAUKF) jointly estimates the states and parameters of the neuronal model while adjusting noise covariance matrices online based on innovation and residual information. We benchmark the adaptive filter ’s performance against existing nonlinear Kalm...
Source: Journal of Computational Neuroscience - March 1, 2023 Category: Neuroscience Source Type: research
Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex
In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 µm, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), ov erlapping also with the associated representation in the p...
Source: Journal of Computational Neuroscience - January 25, 2023 Category: Neuroscience Source Type: research
Introduction to the proceedings of the CNS*2022 meeting
(Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - January 17, 2023 Category: Neuroscience Source Type: research
Deciphering functional roles of synaptic plasticity and intrinsic neural firing in developing mouse visual cortex layer IV microcircuit
AbstractBetween the onset of the critical period of mouse primary visual cortex and eye opening at postnatal day 14 is a complex process and that is vital for the cognitive function of vision. The onset of the critical period of mouse primary visual cortex involves changes of the intrinsic firing property of each neuron and short term plasticity of synapses. In order to investigate the functional role of each factor in regulating the circuit firing activity during the critical period plasticity, we adopted the Markram ’s model for short term plasticity and Wilson’s model for intrinsic neuron firing activity, and constr...
Source: Journal of Computational Neuroscience - January 15, 2023 Category: Neuroscience Source Type: research
31st Annual Computational Neuroscience Meeting: CNS*2022
(Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - January 3, 2023 Category: Neuroscience Source Type: research
Variations of the spontaneous electrical activities of the neuronal networks imposed by the exposure of electromagnetic radiations using computational map-based modeling
In this study, a ring of three coupled 1-dimensional Rulkov neurons and the generated electromagnetic field (EMF) are considered to investigate how the spontaneous activities might change regarding the EMF exposure. By employing the bifurcation analysis and time series, a comprehensive view of neuronal behavioral changes due to electromagnetic inductions is provided. The main findings of this study are as follows: 1) When a neuronal network is showing a spontaneous chaotic firing manner (without any external stimuli), a generated magnetic field inhibits this type of behavior. In fact, EMF completely eliminated the chaotic ...
Source: Journal of Computational Neuroscience - December 21, 2022 Category: Neuroscience Source Type: research
