@article{MTMT:34578298,
	title = {On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks},
	url = {https://m2.mtmt.hu/api/publication/34578298},
	author = {Shirani, Farshad and Choi, Hannah},
	doi = {10.1007/s10827-023-00863-x},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	unique-id = {34578298},
	issn = {0929-5313},
	abstract = {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.},
	keywords = {Network Stability; synaptic modulation; excitation-inhibition balance; cortical dynamics},
	year = {2023},
	eissn = {1573-6873},
	orcid-numbers = {Choi, Hannah/0000-0002-8192-1121}
}

@article{MTMT:34338958,
	title = {Modelling stimulation and inhibition of retinal ganglion cells during nanoparticle-enhanced infrared neural modulation},
	url = {https://m2.mtmt.hu/api/publication/34338958},
	author = {Begeng, James and Tong, Wei and Ibbotson, Michael and Stoddart, Paul and Kameneva, Tatiana},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {51},
	unique-id = {34338958},
	issn = {0929-5313},
	year = {2023},
	eissn = {1573-6873},
	pages = {S57-S57}
}

@article{MTMT:34318265,
	title = {Activity-dependent infrared laser stimulation to assess its biophysical effects on single neurons},
	url = {https://m2.mtmt.hu/api/publication/34318265},
	author = {Garrido-Pena, Alicia and Sanchez-Martin, Pablo and Reyes-Sanchez, Manuel and Castilla, Javier and Tornero, Jesus and Levi, Rafael and Rodriguez, Francisco B. and Varona, Pablo},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {51},
	unique-id = {34318265},
	issn = {0929-5313},
	year = {2023},
	eissn = {1573-6873},
	pages = {S6-S6}
}

@article{MTMT:34294111,
	title = {Inhibitory stabilization in a cortical neural mass model},
	url = {https://m2.mtmt.hu/api/publication/34294111},
	author = {Eskikand, Parvin Zarei and Soto-Breceda, Artemio and Cook, Mark and Burkitt, Anthony and Grayden, David},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {51},
	unique-id = {34294111},
	issn = {0929-5313},
	year = {2023},
	eissn = {1573-6873},
	pages = {S16-S16}
}

@article{MTMT:33903524,
	title = {Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate},
	url = {https://m2.mtmt.hu/api/publication/33903524},
	author = {Naudin, Lois},
	doi = {10.1007/s10827-023-00848-w},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	unique-id = {33903524},
	issn = {0929-5313},
	keywords = {NEURON; DEGENERACY; BIFURCATION STRUCTURE; conductance-based models; Ion channel variability},
	year = {2023},
	eissn = {1573-6873}
}

@article{MTMT:33878757,
	title = {Adaptive unscented Kalman filter for neuronal state and parameter estimation},
	url = {https://m2.mtmt.hu/api/publication/33878757},
	author = {Azzalini, Loic J. and Crompton, David and D'Eleuterio, Gabriele M. T. and Skinner, Frances and Lankarany, Milad},
	doi = {10.1007/s10827-023-00845-z},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	unique-id = {33878757},
	issn = {0929-5313},
	abstract = {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.},
	keywords = {Adaptability; Model mismatch; Nonlinear Kalman filtering; Conductance-based model},
	year = {2023},
	eissn = {1573-6873}
}

@article{MTMT:33853767,
	title = {Improving a cortical pyramidal neuron model’s classification performance on a real-world ecg dataset by extending inputs},
	url = {https://m2.mtmt.hu/api/publication/33853767},
	author = {Kayikcioglu, Bozkir I. and Ozcan, Z. and Kose, C. and Kayikcioglu, T. and Cetin, A.E.},
	doi = {10.1007/s10827-023-00851-1},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {51},
	unique-id = {33853767},
	issn = {0929-5313},
	year = {2023},
	eissn = {1573-6873},
	pages = {329-341}
}

@article{MTMT:33130191,
	title = {Topological dissimilarities of hierarchical resting networks in type 2 diabetes mellitus and obesity},
	url = {https://m2.mtmt.hu/api/publication/33130191},
	author = {Aranyi, Sándor Csaba and Képes, Zita and Nagy, Marianna and Opposits, Gábor and Garai, Ildikó and Káplár, Miklós and Emri, Miklós},
	doi = {10.1007/s10827-022-00833-9},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {51},
	unique-id = {33130191},
	issn = {0929-5313},
	abstract = {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.},
	keywords = {OBESITY; type 2 diabetes mellitus; Graph theory; Effective connectivity; Dynamic causal modelling},
	year = {2023},
	eissn = {1573-6873},
	pages = {71-86},
	orcid-numbers = {Aranyi, Sándor Csaba/0000-0001-9569-5404; Képes, Zita/0000-0003-2889-8521}
}

@article{MTMT:33872077,
	title = {A general pattern of non-spiking neuron dynamics under the effect of potassium and calcium channel modifications},
	url = {https://m2.mtmt.hu/api/publication/33872077},
	author = {Naudin, Lois and Raison-Aubry, Laetitia and Buhry, Laure},
	doi = {10.1007/s10827-022-00840-w},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {51},
	unique-id = {33872077},
	issn = {0929-5313},
	abstract = {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.},
	keywords = {BIFURCATION; Caenorhabditis elegans; Retina; non-spiking neurons; Conductance variations},
	year = {2023},
	eissn = {1573-6873},
	pages = {173-186}
}

@article{MTMT:33838021,
	title = {Neural manifold analysis of brain circuit dynamics in health and disease},
	url = {https://m2.mtmt.hu/api/publication/33838021},
	author = {Mitchell-Heggs, Rufus and Prado, Selgfred and Gava, Giuseppe P. and Go, Mary Ann and Schultz, Simon R.},
	doi = {10.1007/s10827-022-00839-3},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {51},
	unique-id = {33838021},
	issn = {0929-5313},
	abstract = {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.},
	keywords = {neurological disorders; Dimensionality reduction; manifold learning; Neural population analysis; Neural manifolds},
	year = {2023},
	eissn = {1573-6873},
	pages = {1-21}
}