Journal of Computational Neuroscience最新文献

{"title":"Hierarchical processing underpins competition in tactile perceptual bistability.","authors":"Farzaneh Darki, Andrea Ferrario, James Rankin","doi":"10.1007/s10827-023-00852-0","DOIUrl":"10.1007/s10827-023-00852-0","url":null,"abstract":"<p><p>Ambiguous sensory information can lead to spontaneous alternations between perceptual states, recently shown to extend to tactile perception. The authors recently proposed a simplified form of tactile rivalry which evokes two competing percepts for a fixed difference in input amplitudes across antiphase, pulsatile stimulation of the left and right fingers. 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 distributions and the ratio of distribution moments. The presented modelling work leads to experimentally testable predictions. The same hierarchical model could generalise to account for percept formation, competition and alternations for bistable stimuli that involve pulsatile inputs from the visual and auditory domains.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"343-360"},"PeriodicalIF":1.2,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9956697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing performance between a deep neural network and monkeys with bilateral removals of visual area TE in categorizing feature-ambiguous stimuli.","authors":"Narihisa Matsumoto,&nbsp;Mark A G Eldridge,&nbsp;J Megan Fredericks,&nbsp;Kaleb A Lowe,&nbsp;Barry J Richmond","doi":"10.1007/s10827-023-00854-y","DOIUrl":"10.1007/s10827-023-00854-y","url":null,"abstract":"<p><p>In 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 performance of a simulated hierarchical model of vision in discriminating the same categorization problems presented to monkeys with TE removals. The model was able to simulate the performance of monkeys with TE removals in the categorization task but performed poorly when challenged with visually degraded stimuli. We conclude that further development of the model is required to match the level of visual flexibility present in the monkey visual system.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"381-387"},"PeriodicalIF":1.2,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10305678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selective neural stimulation by leveraging electrophysiological differentiation and using pre-pulsing and non-rectangular waveforms.","authors":"Bemin Ghobreal, Farzan Nadim, Mesut Sahin","doi":"10.1007/s10827-022-00818-8","DOIUrl":"10.1007/s10827-022-00818-8","url":null,"abstract":"<p><p>Efforts on selective neural stimulation have concentrated on segregating axons based on their size and geometry. Nonetheless, axons of the white matter or peripheral nerves may also differ in their electrophysiological properties. The primary objective of this study was to investigate the possibility of selective activation of axons by leveraging an assumed level of diversity in passive (C_m & G_leak) and active membrane properties (K_temp & G_namax). First, the stimulus waveforms with hyperpolarizing (HPP) and depolarizing pre-pulsing (DPP) were tested on selectivity in a local membrane model. The default value of membrane capacitance (C_m) was found to play a critical role in sensitivity of the chronaxie time (Chr) and rheobase (Rhe) to variations of all the four membrane parameters. Decreasing the default value of C_m, and thus the passive time constant of the membrane, amplified the sensitivity to the active parameters, K_temp and G_Namax, on Chr. The HPP waveform could selectively activate neurons even if they were diversified by membrane leakage (G_leak) only, and produced higher selectivity than DPP when parameters are varied in pairs. Selectivity measures were larger when the passive parameters (C_m & G_leak) were varied together, compared to the active parameters. Second, this novel mechanism of selectivity was investigated with non-rectangular waveforms for the stimulating phase (and HPP) in the same local membrane model. Simulation results suggest that Kt² is the most selective waveform followed by Linear and Gaussian waveforms. Traditional rectangular pulse was among the least selective of all. Finally, a compartmental axon model confirmed the main findings of the local model that Kt² is the most selective, but rank ordered the other waveforms differently. These results suggest a potentially novel mechanism of stimulation selectivity, leveraging electrophysiological variations in membrane properties, that can lead to various neural prosthetic applications.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 1","pages":"313-330"},"PeriodicalIF":1.5,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52406907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models.","authors":"Jonathan Oesterle,&nbsp;Nicholas Krämer,&nbsp;Philipp Hennig,&nbsp;Philipp Berens","doi":"10.1007/s10827-023-00856-w","DOIUrl":"10.1007/s10827-023-00856-w","url":null,"abstract":"","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"405"},"PeriodicalIF":1.2,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9957605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transmission of delta band (0.5-4 Hz) oscillations from the globus pallidus to the substantia nigra pars reticulata in dopamine depletion.","authors":"Timothy C Whalen, John E Parker, Aryn H Gittis, Jonathan E Rubin","doi":"10.1007/s10827-023-00853-z","DOIUrl":"10.1007/s10827-023-00853-z","url":null,"abstract":"<p><p>Parkinson'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 predictor 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 generated. In this paper, we use a computational model to study how delta oscillations may arise in the SNr due to projections from the globus pallidus externa (GPe). We propose a network architecture that incorporates inhibition in SNr from oscillating GPe neurons and other SNr neurons. In our simulations, this configuration yields firing patterns in model SNr neurons that match those measured in vivo. In particular, we see the spontaneous emergence of near-antiphase active-predicting and inactive-predicting neural populations in the SNr, which persist under the inclusion of STN inputs based on experimental recordings. These results demonstrate how delta oscillations can propagate through BG nuclei despite imperfect oscillatory synchrony in the source site, narrowing down potential targets for the source of delta oscillations in PD models and giving new insight into the dynamics of SNr oscillations.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"361-380"},"PeriodicalIF":1.5,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527635/pdf/nihms-1908672.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9949699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving a cortical pyramidal neuron model's classification performance on a real-world ecg dataset by extending inputs.","authors":"Ilknur Kayikcioglu Bozkir,&nbsp;Zubeyir Ozcan,&nbsp;Cemal Kose,&nbsp;Temel Kayikcioglu,&nbsp;Ahmet Enis Cetin","doi":"10.1007/s10827-023-00851-1","DOIUrl":"10.1007/s10827-023-00851-1","url":null,"abstract":"<p><p>Pyramidal 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 poorly due to a weight constraint. A proposed mirroring approach for inputs, however, significantly boosted the classification performance of the neuron. We thus conclude that pyramidal neurons can classify real-world data and that the mirroring approach affects performance in a way similar to non-constrained learning.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"329-341"},"PeriodicalIF":1.2,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9940667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional architecture of M1 cells encoding movement direction.","authors":"Caterina Mazzetti,&nbsp;Alessandro Sarti,&nbsp;Giovanna Citti","doi":"10.1007/s10827-023-00850-2","DOIUrl":"10.1007/s10827-023-00850-2","url":null,"abstract":"<p><p>In 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 varying in time. This leads to consider a higher dimensional geometrical structure where fragments are represented as integral curves. A comparison with the curves obtained through numerical simulations and experimental data will be presented. Moreover, neural activity shows coherent behaviours represented in terms of movement trajectories pointing to a specific pattern of movement decomposition Kadmon Harpaz et al. (2019). Here, we will recover this pattern through a spectral clustering algorithm in the subriemannian structure we introduced, and compare our results with the neurophysiological one of Kadmon Harpaz et al. (2019).</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"299-327"},"PeriodicalIF":1.2,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10329174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of cortical extracellular GABA on motor response","authors":"O. Hoshino, M. Zheng, Y. Fukuoka","doi":"10.1007/s10827-022-00821-z","DOIUrl":"https://doi.org/10.1007/s10827-022-00821-z","url":null,"abstract":"","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 1","pages":"375 - 393"},"PeriodicalIF":1.2,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42913724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulations of one- and two-dimensional stochastic neural field equations with delay","authors":"Tiago F. Sequeira, P. Lima","doi":"10.1007/s10827-022-00816-w","DOIUrl":"https://doi.org/10.1007/s10827-022-00816-w","url":null,"abstract":"","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 1","pages":"299 - 311"},"PeriodicalIF":1.2,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43905192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics of ramping bursts in a respiratory neuron model.","authors":"Muhammad U Abdulla,&nbsp;Ryan S Phillips,&nbsp;Jonathan E Rubin","doi":"10.1007/s10827-021-00800-w","DOIUrl":"https://doi.org/10.1007/s10827-021-00800-w","url":null,"abstract":"<p><p>Intensive computational and theoretical work has led to the development of multiple mathematical models for bursting in respiratory neurons in the pre-Bötzinger Complex (pre-BötC) of the mammalian brainstem. Nonetheless, these previous models have not captured the pre-inspiratory ramping aspects of these neurons' activity patterns, in which relatively slow tonic spiking gradually progresses to faster spiking and a full-blown burst, with a corresponding gradual development of an underlying plateau potential. In this work, we show that the incorporation of the dynamics of the extracellular potassium ion concentration into an existing model for pre-BötC neuron bursting, along with some parameter adjustments, suffices to induce this ramping behavior. Using fast-slow decomposition, we show that this activity can be considered as a form of parabolic bursting, but with burst termination at a homoclinic bifurcation rather than as a SNIC bifurcation. We also investigate the parameter-dependence of these solutions and show that the proposed model yields a greater dynamic range of burst frequencies, durations, and duty cycles than those produced by other models in the literature.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"161-180"},"PeriodicalIF":1.2,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39561469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}