@article{MTMT:1426264,
	title = {Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment},
	url = {https://m2.mtmt.hu/api/publication/1426264},
	author = {Berkes, P and Orbán, Gergő and Lengyel, M and Fiser, J},
	doi = {10.1126/science.1195870},
	journal-iso = {SCIENCE},
	journal = {SCIENCE},
	volume = {331},
	unique-id = {1426264},
	issn = {0036-8075},
	abstract = {The brain maintains internal models of its environment to interpret sensory inputs and to prepare actions. Although behavioral studies have demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.},
	keywords = {POPULATION; VISUAL-CORTEX; PERCEPTION; emergence; NATURAL SCENES; BAYESIAN-INFERENCE},
	year = {2011},
	eissn = {1095-9203},
	pages = {83-87}
}

@article{MTMT:1324635,
	title = {Statistically optimal perception and learning: from behavior to neural representations},
	url = {https://m2.mtmt.hu/api/publication/1324635},
	author = {Fiser, J and Berkes, P and Orbán, Gergő and Lengyel, M},
	doi = {10.1016/j.tics.2010.01.003},
	journal-iso = {TRENDS COGN SCI},
	journal = {TRENDS IN COGNITIVE SCIENCES},
	volume = {14},
	unique-id = {1324635},
	issn = {1364-6613},
	abstract = {Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty. © 2010 Elsevier Ltd. All rights reserved.},
	year = {2010},
	eissn = {1879-307X},
	pages = {119-130}
}

@article{MTMT:1740276,
	title = {Fuzzy communities and the concept of bridgeness in complex networks},
	url = {https://m2.mtmt.hu/api/publication/1740276},
	author = {Nepusz, Tamás and Petróczi, Andrea and Négyessy, László and Bazsó, Fülöp},
	doi = {10.1103/PhysRevE.77.016107},
	journal-iso = {PHYS REV E STAT NONLIN},
	journal = {PHYSICAL REVIEW E - STATISTICAL, NONLINEAR AND SOFT MATTER PHYSICS (2001-2015)},
	volume = {77},
	unique-id = {1740276},
	issn = {1539-3755},
	abstract = {We consider the problem of fuzzy community detection in 
		networks, which complements and expands the concept of 
		overlapping community structure. Our approach allows each vertex 
		of the graph to belong to multiple communities at the same time, 
		determined by exact numerical membership degrees, even in the 
		presence of uncertainty in the data being analyzed. We create an 
		algorithm for determining the optimal membership degrees with 
		respect to a given goal function. Based on the membership 
		degrees, we introduce a measure that is able to identify outlier 
		vertices that do not belong to any of the communities, bridge 
		vertices that have significant membership in more than one 
		single community, and regular vertices that fundamentally 
		restrict their interactions within their own community, while 
		also being able to quantify the centrality of a vertex with 
		respect to its dominant community. The method can also be used 
		for prediction in case of uncertainty in the data set analyzed. 
		The number of communities can be given in advance, or determined 
		by the algorithm itself, using a fuzzified variant of the 
		modularity function. The technique is able to discover the fuzzy 
		community structure of different real world networks including, 
		but not limited to, social networks, scientific collaboration 
		networks, and cortical networks, with high confidence.},
	year = {2008},
	eissn = {1550-2376},
	orcid-numbers = {Nepusz, Tamás/0000-0002-1451-338X; Petróczi, Andrea/0000-0002-8365-6173; Négyessy, László/0000-0002-4369-0406}
}

@article{MTMT:1012091,
	title = {Uncovering the overlapping community structure of complex networks in nature and society},
	url = {https://m2.mtmt.hu/api/publication/1012091},
	author = {Palla, Gergely and Derényi, Imre and Farkas, Illés and Vicsek, Tamás},
	doi = {10.1038/nature03607},
	journal-iso = {NATURE},
	journal = {NATURE},
	volume = {435},
	unique-id = {1012091},
	issn = {0028-0836},
	year = {2005},
	eissn = {1476-4687},
	pages = {814-818},
	orcid-numbers = {Palla, Gergely/0000-0002-3406-4200; Derényi, Imre/0000-0003-1171-1214; Farkas, Illés/0000-0001-5341-5582; Vicsek, Tamás/0000-0003-1431-2884}
}

@article{MTMT:31941980,
	title = {An unpublished idea of D. R. Hartree and its extension},
	url = {https://m2.mtmt.hu/api/publication/31941980},
	author = {Jánossy, Lajos},
	doi = {10.1007/BF03159407},
	journal-iso = {ACTA PHYS ACAD SCI HUNG},
	journal = {ACTA PHYSICA ACADEMIAE SCIENTIARUM HUNGARICAE},
	volume = {41},
	unique-id = {31941980},
	issn = {0001-6705},
	year = {1976},
	pages = {211-218}
}

@article{MTMT:31941993,
	title = {A new approach to the theory of relativity. II. The general theory of relativity},
	url = {https://m2.mtmt.hu/api/publication/31941993},
	author = {Jánossy, Lajos},
	doi = {10.1007/BF00708611},
	journal-iso = {FOUND PHYS},
	journal = {FOUNDATIONS OF PHYSICS},
	volume = {1},
	unique-id = {31941993},
	issn = {0015-9018},
	year = {1971},
	eissn = {1572-9516},
	pages = {251-267}
}

@article{MTMT:31942057,
	title = {Extensive penetrating showers [5]},
	url = {https://m2.mtmt.hu/api/publication/31942057},
	author = {Jánossy, Lajos and Rochester, G.D. and Broadbent, D.},
	doi = {10.1038/155142a0},
	journal-iso = {NATURE},
	journal = {NATURE},
	volume = {155},
	unique-id = {31942057},
	issn = {0028-0836},
	year = {1945},
	eissn = {1476-4687},
	pages = {142-143}
}

@article{MTMT:31942067,
	title = {Cloud chamber investigation of penetrating showers [1]},
	url = {https://m2.mtmt.hu/api/publication/31942067},
	author = {Jánossy, Lajos and Mccusker, C.B. and Rochester, G.D.},
	doi = {10.1038/148660a0},
	journal-iso = {NATURE},
	journal = {NATURE},
	volume = {148},
	unique-id = {31942067},
	issn = {0028-0836},
	year = {1941},
	eissn = {1476-4687},
	pages = {660}
}

@article{MTMT:31942070,
	title = {The penetrating power of cosmic-ray shower particles},
	url = {https://m2.mtmt.hu/api/publication/31942070},
	author = {Jánossy, Lajos},
	doi = {10.1017/S0305004100020636},
	journal-iso = {MATH PROC CAMBRIDGE},
	journal = {MATHEMATICAL PROCEEDINGS OF THE CAMBRIDGE PHILOSOPHICAL SOCIETY},
	volume = {34},
	unique-id = {31942070},
	issn = {0305-0041},
	year = {1938},
	eissn = {1469-8064},
	pages = {614-619}
}