TY - JOUR AU - Shirani, Farshad AU - Choi, Hannah TI - On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI PY - 2023 PG - 35 SN - 0929-5313 DO - 10.1007/s10827-023-00863-x UR - https://m2.mtmt.hu/api/publication/34578298 ID - 34578298 AB - Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a network's baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. Our study based on extensive bifurcation analyses thus reveal the functional optimality and criticality of structural and physiological parameters in establishing the baseline operating state of local cortical networks. LA - English DB - MTMT ER - TY - JOUR AU - Begeng, James AU - Tong, Wei AU - Ibbotson, Michael AU - Stoddart, Paul AU - Kameneva, Tatiana TI - Modelling stimulation and inhibition of retinal ganglion cells during nanoparticle-enhanced infrared neural modulation JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI VL - 51 PY - 2023 SP - S57 EP - S57 PG - 1 SN - 0929-5313 UR - https://m2.mtmt.hu/api/publication/34338958 ID - 34338958 LA - English DB - MTMT ER - TY - JOUR AU - Garrido-Pena, Alicia AU - Sanchez-Martin, Pablo AU - Reyes-Sanchez, Manuel AU - Castilla, Javier AU - Tornero, Jesus AU - Levi, Rafael AU - Rodriguez, Francisco B. AU - Varona, Pablo TI - Activity-dependent infrared laser stimulation to assess its biophysical effects on single neurons JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI VL - 51 PY - 2023 SP - S6 EP - S6 PG - 1 SN - 0929-5313 UR - https://m2.mtmt.hu/api/publication/34318265 ID - 34318265 LA - English DB - MTMT ER - TY - JOUR AU - Eskikand, Parvin Zarei AU - Soto-Breceda, Artemio AU - Cook, Mark AU - Burkitt, Anthony AU - Grayden, David TI - Inhibitory stabilization in a cortical neural mass model JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI VL - 51 PY - 2023 SP - S16 EP - S16 PG - 1 SN - 0929-5313 UR - https://m2.mtmt.hu/api/publication/34294111 ID - 34294111 LA - English DB - MTMT ER - TY - JOUR AU - Naudin, Lois TI - Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI PY - 2023 PG - 6 SN - 0929-5313 DO - 10.1007/s10827-023-00848-w UR - https://m2.mtmt.hu/api/publication/33903524 ID - 33903524 LA - English DB - MTMT ER - TY - JOUR AU - Azzalini, Loic J. AU - Crompton, David AU - D'Eleuterio, Gabriele M. T. AU - Skinner, Frances AU - Lankarany, Milad TI - Adaptive unscented Kalman filter for neuronal state and parameter estimation JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI PY - 2023 PG - 15 SN - 0929-5313 DO - 10.1007/s10827-023-00845-z UR - https://m2.mtmt.hu/api/publication/33878757 ID - 33878757 AB - Data 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 Kalman filters and explore the sensitivity of the filter parameters to the system being modelled. To evaluate the robustness of the proposed solution, we simulate practical settings that challenge tracking performance, such as a model mismatch and measurement faults. Compared to standard variants of the Kalman filter the adaptive variant implemented here is more accurate and robust to faults. LA - English DB - MTMT ER - TY - JOUR AU - Kayikcioglu, Bozkir I. AU - Ozcan, Z. AU - Kose, C. AU - Kayikcioglu, T. AU - Cetin, A.E. TI - Improving a cortical pyramidal neuron model’s classification performance on a real-world ecg dataset by extending inputs JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI VL - 51 PY - 2023 SP - 329 EP - 341 PG - 13 SN - 0929-5313 DO - 10.1007/s10827-023-00851-1 UR - https://m2.mtmt.hu/api/publication/33853767 ID - 33853767 N1 - Department of Computer Engineering, Karadeniz Technical University, Trabzon, Turkey Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey Department of Computer Engineering, Bulent Ecevit University, Zonguldak, Turkey Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, United States Export Date: 27 October 2023 CODEN: JCNEF Correspondence Address: Kayikcioglu Bozkir, I.; Department of Computer Engineering, Turkey; email: ilknurkayikcioglu@ktu.edu.tr Funding details: Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, BIDEB-2219 Funding text 1: Dr. Temel Kayikcioglu is supported by Scientific and Technological Research Council of Turkey (TUBITAK) through BIDEB-2219 International Postdoctoral Research Fellowship Program. The authors thank the anonymous reviewers for their careful reading of the manuscript and their many insightful comments and suggestions. LA - English DB - MTMT ER - TY - JOUR AU - Aranyi, Sándor Csaba AU - Képes, Zita AU - Nagy, Marianna AU - Opposits, Gábor AU - Garai, Ildikó AU - Káplár, Miklós AU - Emri, Miklós TI - Topological dissimilarities of hierarchical resting networks in type 2 diabetes mellitus and obesity JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI VL - 51 PY - 2023 SP - 71 EP - 86 PG - 16 SN - 0929-5313 DO - 10.1007/s10827-022-00833-9 UR - https://m2.mtmt.hu/api/publication/33130191 ID - 33130191 N1 - 337030 AB - Type 2 diabetes mellitus (T2DM) is reported to cause widespread changes in brain function, leading to cognitive impairments. Research using resting-state functional magnetic resonance imaging data already aims to understand functional changes in complex brain connectivity systems. However, no previous studies with dynamic causal modelling (DCM) tried to investigate large-scale effective connectivity in diabetes. We aimed to examine the differences in large-scale resting state networks in diabetic and obese patients using combined DCM and graph theory methodologies. With the participation of 70 subjects (43 diabetics, 27 obese), we used cross-spectra DCM to estimate connectivity between 36 regions, subdivided into seven resting networks (RSN) commonly recognized in the literature. We assessed group-wise connectivity of T2DM and obesity, as well as group differences, with parametric empirical Bayes and Bayesian model reduction techniques. We analyzed network connectivity globally, between RSNs, and regionally. We found that average connection strength was higher in T2DM globally and between RSNs, as well. On the network level, the salience network shows stronger total within-network connectivity in diabetes (8.07) than in the obese group (4.02). Regionally, we measured the most significant average decrease in the right middle temporal gyrus (-0.013 Hz) and the right inferior parietal lobule (-0.01 Hz) relative to the obese group. In comparison, connectivity increased most notably in the left anterior prefrontal cortex (0.01 Hz) and the medial dorsal thalamus (0.009 Hz). 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. LA - English DB - MTMT ER - TY - JOUR AU - Naudin, Lois AU - Raison-Aubry, Laetitia AU - Buhry, Laure TI - A general pattern of non-spiking neuron dynamics under the effect of potassium and calcium channel modifications JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI VL - 51 PY - 2023 IS - 1 SP - 173 EP - 186 PG - 14 SN - 0929-5313 DO - 10.1007/s10827-022-00840-w UR - https://m2.mtmt.hu/api/publication/33872077 ID - 33872077 AB - Electrical 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 affect the dynamics of non-spiking neurons has never been assessed. Based on experimental observations reported in the biological literature and on mathematical considerations, we first propose a phenotypic classification of non-spiking neurons. Then, we determine a general pattern of the phenotypic evolution of non-spiking neurons as a function of changes in calcium and potassium conductances. Furthermore, we study the homeostatic compensatory mechanisms of ion channels in a well-posed non-spiking retinal cone model. We show that there is a restricted range of ion conductance values for which the behavior and phenotype of the neuron are maintained. Finally, we discuss the implications of the phenotypic changes of individual cells at the level of neuronal network functioning of the C. elegans worm and the retina, which are two non-spiking nervous tissues composed of neurons with various phenotypes. LA - English DB - MTMT ER - TY - JOUR AU - Mitchell-Heggs, Rufus AU - Prado, Selgfred AU - Gava, Giuseppe P. AU - Go, Mary Ann AU - Schultz, Simon R. TI - Neural manifold analysis of brain circuit dynamics in health and disease JF - JOURNAL OF COMPUTATIONAL NEUROSCIENCE J2 - J COMPUT NEUROSCI VL - 51 PY - 2023 IS - 1 SP - 1 EP - 21 PG - 21 SN - 0929-5313 DO - 10.1007/s10827-022-00839-3 UR - https://m2.mtmt.hu/api/publication/33838021 ID - 33838021 AB - Recent 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 subspaces are referred to as "neural manifolds ", and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer's Disease, and speculate that neural manifold analysis may help us to understand the circuit-level consequences of molecular and cellular neuropathology. LA - English DB - MTMT ER -