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001-es BibID:	BIBFORM037166
Első szerző:	Stelescu András
Cím:	Somato-dendritic morphology and dendritic signal transfer properties differentiate between fore- and hindlimb innervating motoneurons in the frog Rana esculenta / Stelescu András, Sümegi János, Wéber Ildikó, Birinyi András, Wolf Ervin
Dátum:	2012
ISSN:	1471-2202
Megjegyzések:	BACKGROUND: The location specific motor pattern generation properties of the spinal cord along its rostrocaudal axis have been demonstrated. However, it is still unclear that these differences are due to the different spinal interneuronal networks underlying locomotions or there are also segmental differences in motoneurons innervating different limbs. Frogs use their fore- and hindlimbs differently during jumping and swimming. Therefore we hypothesized that limb innervating motoneurons, located in the cervical and lumbar spinal cord, are different in their morphology and dendritic signal transfer properties. The test of this hypothesis what we report here.RESULTS: Discriminant analysis classified segmental origin of the intracellularly labeled and threedimensionally reconstructed motoneurons 100% correctly based on twelve morphological variables. Somata of lumbar motoneurons were rounder; the dendrites had bigger total length, more branches with higher branching orders and different spatial distributions of branch points. The ventro-medial extent of cervical dendrites was bigger than in lumbar motoneurons. Computational models of the motoneurons showed that dendritic signal transfer properties were also different in the two groups of motoneurons. Whether log attenuations were higher or lower in cervical than in lumbar motoneurons depended on the proximity of dendritic input to the soma. To investigate dendritic voltage and current transfer properties imposed by dendritic architecture rather than by neuronal size we used standardized distributions of transfer variables. We introduced a novel combination of cluster analysis and homogeneity indexes to quantify segmental segregation tendencies of motoneurons based on their dendritic transfer properties. A segregation tendency of cervical and lumbar motoneurons was detected by the rates of steady-state and transient voltageamplitude transfers from dendrites to soma at all levels of synaptic background activities, modeled by varying the specific dendritic membrane resistance. On the other hand no segregation was observed by the steady-state current transfer except under high background activity.CONCLUSIONS: We found size-dependent and size-independent differences in morphology and electrical structure of the limb moving motoneurons based on their spinal segmental location in frogs. Location specificity of locomotor networks is therefore partly due to segmental differences in motoneurons driving fore-, and hindlimbs.
Tárgyszavak:	Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
Megjelenés:	Bmc Neuroscience [electronic resource]. - 13 : 1 (2012), p. 68. -
További szerzők:	Sümegi János Wéber Ildikó (1972-) (biológus, neurobiológus) Birinyi András (1960-) (anatómus, neurobiológus) Wolf Ervin (1961-) (fizikus, neurobiológus)
Pályázati támogatás:	K67747 OTKA
Internet cím:	DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM067698
Első szerző:	Zsuga Judit (neurológus, pszichoterapeuta, egészségügyi szakmanager)
Cím:	'Proactive' use of cue-context congruence for building reinforcement learning's reward function / Zsuga Judit, Biró Klára, Tajti Gábor, Szilasi Magdolna Emma, Papp Csaba, Juhasz Béla, Gesztelyi Rudolf
Dátum:	2016
ISSN:	1471-2202
Megjegyzések:	BACKGROUND: Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability. The central objective of reinforcement learning is to solve these two functions outside the agent's control either using, or not using a model.RESULTS:In the present paper, using the proactive model of reinforcement learning we offer insight on how the brain creates simplified representations of the environment, and how these representations are organized to support the identification of relevant stimuli and action. Furthermore, we identify neurobiological correlates of our model by suggesting that the reward and policy functions, attributes of the Bellman equitation, are built by the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), respectively.CONCLUSIONS:Based on this we propose that the OFC assesses cue-context congruence to activate the most context frame. Furthermore given the bidirectional neuroanatomical link between the OFC and model-free structures, we suggest that model-based input is incorporated into the reward prediction error (RPE) signal, and conversely RPE signal may be used to update the reward-related information of context frames and the policy underlying action selection in the OFC and ACC, respectively. Furthermore clinical implications for cognitive behavioral interventions are discussed.
Tárgyszavak:	Orvostudományok Egészségtudományok idegen nyelvű folyóiratközlemény külföldi lapban Model-based reinforcement learning Proactive brain Bellman equation Reward function Policy function Cue-context congruence
Megjelenés:	Bmc Neuroscience. - 17 : 70 (2016), p. 70. -
További szerzők:	Bíró Klára (1970-) (egészségügyi menedzsment) Tajti Gábor (1988-) (gyógyszerész, biofizikus, sejtbiológus) Szilasi Magdolna Emma (1983-) (pulmonológus) Papp Csaba (1966-) (aneszteziológus és intenzív terápiás szakorvos) Juhász Béla (1978-) (kísérletes farmakológus) Gesztelyi Rudolf (1969-) (kísérletes farmakológus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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