Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Walter Senn"'
Publikováno v:
Clinical and Translational Neuroscience, Vol 8, Iss 2, p 21 (2024)
The importance of sleep for healthy brain function is widely acknowledged. However, it remains unclear how the internal generation of dreams might facilitate cognitive processes. In this perspective, we review a computational approach inspired by art
Externí odkaz:
https://doaj.org/article/924cd8aae4544b298054952e9073b4ba
Autor:
Chang Zhao, Yves F. Widmer, Sören Diegelmann, Mihai A. Petrovici, Simon G. Sprecher, Walter Senn
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
Abstract Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon
Externí odkaz:
https://doaj.org/article/c2f90342db924b4eaf22516f0a87b60f
Publikováno v:
eLife, Vol 11 (2022)
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen parametrization of the weights. This problem relates, for example, to neuronal morphology: synapses which are functionally equivalent in terms of their
Externí odkaz:
https://doaj.org/article/55dfa7beb3a94fbc9d08ea7e465f2b01
Publikováno v:
eLife, Vol 11 (2022)
Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the c
Externí odkaz:
https://doaj.org/article/eddd849147f843f3ae988d3cf3cdcde4
Autor:
Agnes Korcsak-Gorzo, Michael G Müller, Andreas Baumbach, Luziwei Leng, Oliver J Breitwieser, Sacha J van Albada, Walter Senn, Karlheinz Meier, Robert Legenstein, Mihai A Petrovici
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 3, p e1009753 (2022)
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately. Often, this requires visiting multiple interpretations of the available information or mult
Externí odkaz:
https://doaj.org/article/d015a94d58014b4395aad6d959afd880
Publikováno v:
eLife, Vol 10 (2021)
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic coupling strength between neurons are essential for this capability, setting us apart from simpler, hard-wired organisms. How these changes can be mathematic
Externí odkaz:
https://doaj.org/article/cea12b1c7ec54738a8e7d6767708714a
Autor:
Willem AM Wybo, Jakob Jordan, Benjamin Ellenberger, Ulisses Marti Mengual, Thomas Nevian, Walter Senn
Publikováno v:
eLife, Vol 10 (2021)
Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. Through carefully chosen parameter fits, solvable in the least-squares sense, we obtain accurate reduced compartment
Externí odkaz:
https://doaj.org/article/df69fbacd79e4afc850dd0a88b684ae6
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 6, p e1005003 (2016)
Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their l
Externí odkaz:
https://doaj.org/article/61b88b93a1d64899be015585894048d3
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 2, p e1004638 (2016)
In the last decade dendrites of cortical neurons have been shown to nonlinearly combine synaptic inputs by evoking local dendritic spikes. It has been suggested that these nonlinearities raise the computational power of a single neuron, making it com
Externí odkaz:
https://doaj.org/article/ffb7e67df7fb4620b0563242211eae5a
Autor:
Aaron Michael Clarke, Johannes Friedrich, Elisa M Tartaglia, Silvia Marchesotti, Walter Senn, Michael H Herzog
Publikováno v:
PLoS ONE, Vol 10, Iss 4, p e0123105 (2015)
Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly
Externí odkaz:
https://doaj.org/article/ed97feca3ec6479fa29c7fe9f4b5d986