@article{MTMT:34764210,
	title = {Design of oscillatory neural networks by machine learning},
	url = {https://m2.mtmt.hu/api/publication/34764210},
	author = {Rudner, Tamás and Porod, Wolfgang and Csaba, György},
	doi = {10.3389/fnins.2024.1307525},
	journal-iso = {FRONT NEUROSCI-SWITZ},
	journal = {FRONTIERS IN NEUROSCIENCE},
	volume = {18},
	unique-id = {34764210},
	issn = {1662-4548},
	abstract = {We demonstrate the utility of machine learning algorithms for the design of oscillatory neural networks (ONNs). After constructing a circuit model of the oscillators in a machine-learning-enabled simulator and performing Backpropagation through time (BPTT) for determining the coupling resistances between the ring oscillators, we demonstrate the design of associative memories and multi-layered ONN classifiers. The machine-learning-designed ONNs show superior performance compared to other design methods (such as Hebbian learning), and they also enable significant simplifications in the circuit topology. We also demonstrate the design of multi-layered ONNs that show superior performance compared to single-layer ones. We argue that machine learning can be a valuable tool to unlock the true computing potential of ONNs hardware.},
	year = {2024},
	eissn = {1662-453X}
}

@mastersthesis{MTMT:34750920,
	title = {Advanced Methods for Environment Analysis Based on Lidar Data},
	url = {https://m2.mtmt.hu/api/publication/34750920},
	author = {H. Zováthi, Örkény Ádám},
	unique-id = {34750920},
	year = {2024}
}

@article{MTMT:34749015,
	title = {Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons},
	url = {https://m2.mtmt.hu/api/publication/34749015},
	author = {Tresóné Takács, Virág and Bardóczi, Zsuzsanna and Orosz, Áron and Major, Ábel and Tar, Luca and Berki, Péter and Papp, Péter and Mayer, Márton István and Sebők, Hunor and Zsolt, Luca and Sós, Katalin Eszter and Káli, Szabolcs and Freund, Tamás and Nyíri, Gábor},
	doi = {10.1371/journal.pbio.3002539},
	journal-iso = {PLOS BIOL},
	journal = {PLOS BIOLOGY},
	volume = {22},
	unique-id = {34749015},
	issn = {1544-9173},
	abstract = {GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning, and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo, and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed, and make available several key data sets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The data set includes detailed scanning electron microscopy (SEM)-based 3D reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core data set with other anatomical data, patch-clamp electrophysiology, and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For example, we estimated that an OLM cell receives about 8,400, whereas an HS cell about 15,600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.},
	year = {2024},
	eissn = {1545-7885},
	orcid-numbers = {Tresóné Takács, Virág/0000-0002-3276-4131}
}

@article{MTMT:34622806,
	title = {A Low Side Lobe Level Parabolic Antenna for Meteorological Applications},
	url = {https://m2.mtmt.hu/api/publication/34622806},
	author = {Eszes, András and Szabó, Zsolt and Ladányi-Turóczy, Béla and Kalácska, István},
	journal-iso = {PROG ELECTROMAG RES LETT},
	journal = {PROGRESS IN ELECTROMAGNETIC RESEARCH LETTERS},
	volume = {Vol. 115},
	unique-id = {34622806},
	issn = {1937-6480},
	year = {2024},
	pages = {91-98}
}

@article{MTMT:34555193,
	title = {Rodent models of dermatological disorders},
	url = {https://m2.mtmt.hu/api/publication/34555193},
	author = {ASBÓTH, Dorottya and BÁNFI, Barnabás and Kocsis, Dorottya and Erdő, Franciska},
	doi = {10.23736/S2784-8671.23.07700-9},
	journal-iso = {ITAL J DERMATOL VENEREOL},
	journal = {ITALIAN JOURNAL OF DERMATOLOGY AND VENEREOLOGY},
	unique-id = {34555193},
	issn = {2784-8671},
	year = {2024},
	eissn = {2784-8450}
}

@mastersthesis{MTMT:34529745,
	title = {Development of Brain-Computer Interfaces by using Deep Learning Technologies},
	url = {https://m2.mtmt.hu/api/publication/34529745},
	author = {Köllőd, Csaba Márton},
	doi = {10.15774/PPKE.ITK.2024.001},
	publisher = {PPKE},
	unique-id = {34529745},
	year = {2024},
	orcid-numbers = {Köllőd, Csaba Márton/0000-0003-3817-6709}
}

@article{MTMT:34527228,
	title = {A Kinetic Finite Volume Discretization of the Multidimensional PIDE Model for Gene Regulatory Networks},
	url = {https://m2.mtmt.hu/api/publication/34527228},
	author = {Vághy, Mihály András and Otero-Muras, I and Pájaro, M and Szederkényi, Gábor},
	doi = {10.1007/s11538-023-01251-3},
	journal-iso = {B MATH BIOL},
	journal = {BULLETIN OF MATHEMATICAL BIOLOGY},
	volume = {86},
	unique-id = {34527228},
	issn = {0092-8240},
	abstract = {In this paper, a finite volume discretization scheme for partial integro-differential equations (PIDEs) describing the temporal evolution of protein distribution in gene regulatory networks is proposed. It is shown that the obtained set of ODEs can be formally represented as a compartmental kinetic system with a strongly connected reaction graph. This allows the application of the theory of nonnegative and compartmental systems for the qualitative analysis of the approximating dynamics. In this framework, it is straightforward to show the existence, uniqueness and stability of equilibria. Moreover, the computation of the stationary probability distribution can be traced back to the solution of linear equations. The discretization scheme is presented for one and multiple dimensional models separately. Illustrative computational examples show the precision of the approach, and good agreement with previous results in the literature.},
	year = {2024},
	eissn = {1522-9602},
	orcid-numbers = {Szederkényi, Gábor/0000-0003-4199-6089}
}

@article{MTMT:34463676,
	title = {Large time behavior of nonautonomous linear differential equations with Kirchhoff coefficients},
	url = {https://m2.mtmt.hu/api/publication/34463676},
	author = {Diblík, J and Pituk, Mihály and Szederkényi, Gábor},
	doi = {10.1016/j.automatica.2023.111473},
	journal-iso = {AUTOMATICA},
	journal = {AUTOMATICA},
	volume = {161},
	unique-id = {34463676},
	issn = {0005-1098},
	year = {2024},
	eissn = {1873-2836},
	orcid-numbers = {Diblík, J/0000-0001-5009-316X; Szederkényi, Gábor/0000-0003-4199-6089}
}

@article{MTMT:34415352,
	title = {The Traffic Reaction Model: A kinetic compartmental approach to road traffic modeling},
	url = {https://m2.mtmt.hu/api/publication/34415352},
	author = {Pereira, M and Kulcsar, Balazs and Lipták, György and Kovács, Mihály and Szederkényi, Gábor},
	doi = {10.1016/j.trc.2023.104435},
	journal-iso = {TRANSPORT RES C-EMER},
	journal = {TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES},
	volume = {158},
	unique-id = {34415352},
	issn = {0968-090X},
	year = {2024},
	eissn = {1879-2359},
	orcid-numbers = {Pereira, M/0000-0002-7899-2690; Kovács, Mihály/0000-0001-7977-9114; Szederkényi, Gábor/0000-0003-4199-6089}
}

@{MTMT:34735022,
	title = {Transfer learning for microbiome based predictive disease classification},
	url = {https://m2.mtmt.hu/api/publication/34735022},
	author = {Molnár, Zsófia},
	booktitle = {Hungarian Molecular Life Sciences 2023: Book of Abstracts},
	unique-id = {34735022},
	year = {2023}
}