TY - JOUR AU - Berkes, P AU - Orbán, Gergő AU - Lengyel, M AU - Fiser, J TI - Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment JF - SCIENCE J2 - SCIENCE VL - 331 PY - 2011 IS - 6013 SP - 83 EP - 87 PG - 5 SN - 0036-8075 DO - 10.1126/science.1195870 UR - https://m2.mtmt.hu/api/publication/1426264 ID - 1426264 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Fiser, J AU - Berkes, P AU - Orbán, Gergő AU - Lengyel, M TI - Statistically optimal perception and learning: from behavior to neural representations JF - TRENDS IN COGNITIVE SCIENCES J2 - TRENDS COGN SCI VL - 14 PY - 2010 IS - 3 SP - 119 EP - 130 PG - 12 SN - 1364-6613 DO - 10.1016/j.tics.2010.01.003 UR - https://m2.mtmt.hu/api/publication/1324635 ID - 1324635 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Nepusz, Tamás AU - Petróczi, Andrea AU - Négyessy, László AU - Bazsó, Fülöp TI - Fuzzy communities and the concept of bridgeness in complex networks JF - PHYSICAL REVIEW E - STATISTICAL, NONLINEAR AND SOFT MATTER PHYSICS (2001-2015) J2 - PHYS REV E STAT NONLIN VL - 77 PY - 2008 IS - 1 PG - 13 SN - 1539-3755 DO - 10.1103/PhysRevE.77.016107 UR - https://m2.mtmt.hu/api/publication/1740276 ID - 1740276 N1 - Part number: 2 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Palla, Gergely AU - Derényi, Imre AU - Farkas, Illés AU - Vicsek, Tamás TI - Uncovering the overlapping community structure of complex networks in nature and society JF - NATURE J2 - NATURE VL - 435 PY - 2005 IS - 7043 SP - 814 EP - 818 PG - 5 SN - 0028-0836 DO - 10.1038/nature03607 UR - https://m2.mtmt.hu/api/publication/1012091 ID - 1012091 N1 - Biological Physics Research Group, Hungarian Academy of Sciences, Pázmány P. stny. 1A, H-1117 Budapest, Hungary Department of Biological Physics, Eötvös University, Pézmény P. stny. 1A, H-1117 Budapest, Hungary Cited By :3882 Export Date: 25 March 2022 CODEN: NATUA Correspondence Address: Vicsek, T.; Biological Physics Research Group, Pázmány P. stny. 1A, H-1117 Budapest, Hungary; email: vicsek@angel.elte.hu LA - English DB - MTMT ER - TY - JOUR AU - Jánossy, Lajos TI - An unpublished idea of D. R. 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AU - Rochester, G.D. TI - Cloud chamber investigation of penetrating showers [1] JF - NATURE J2 - NATURE VL - 148 PY - 1941 IS - 3761 SP - 660 SN - 0028-0836 DO - 10.1038/148660a0 UR - https://m2.mtmt.hu/api/publication/31942067 ID - 31942067 LA - English DB - MTMT ER - TY - JOUR AU - Jánossy, Lajos TI - The penetrating power of cosmic-ray shower particles JF - MATHEMATICAL PROCEEDINGS OF THE CAMBRIDGE PHILOSOPHICAL SOCIETY J2 - MATH PROC CAMBRIDGE VL - 34 PY - 1938 IS - 4 SP - 614 EP - 619 PG - 6 SN - 0305-0041 DO - 10.1017/S0305004100020636 UR - https://m2.mtmt.hu/api/publication/31942070 ID - 31942070 LA - English DB - MTMT ER -