9 papers · ranked by Valyu relevance
Felix Weissenberger, Marcelo Matheus Gauy, Johannes Lengler, Florian Meier + 1 more
'Florian Meier' 'Angelika Steger'] In computational neuroscience, synaptic plasticity rules are often formulated in terms of firing rates. The predominant description of in vivo neuronal activity, however, is the instantaneous rate (or spiking probability). In this article we resolve this discrepancy by showing that…
Lawrence C. Udeigwe, Paul W. Munro, G. Bard Ermentrout
The Bienenstock-Cooper-Munro (BCM) learning rule provides a simple setup for synaptic modification that combines a Hebbian product rule with a homeostatic mechanism that keeps the weights bounded. The homeostatic part of the learning rule depends on the time average of the post-synaptic activity and provides a sliding…
Lawrence C Udeigwe, G Bard Ermentrout, Paul W Munro
The BCM learning rule originally arose from experiments intended for measuring the selectivity of neurons in the primary visual cortex, and it dependence on input stimuli. This learning rule incorporates a dynamic LTP threshold, which depends on the time averaged postsynaptic activity. Although the BCM learning rule…
Matthias Kohler, Florian Röhrbein, Alois Knoll, Alin Albu-Schäffer + 1 more
'Henrik Jörntell'] Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However…
Peter Jedlicka, Lubica Benuskova, Wickliffe C. Abraham, Abigail Morrison
'Abigail Morrison'] Long-term potentiation (LTP) and long-term depression (LTD) are widely accepted to be synaptic mechanisms involved in learning and memory. It remains uncertain, however, which particular activity rules are utilized by hippocampal neurons to induce LTP and LTD in behaving animals. Recent experiments…
Wenxuan Pan, Feifei Zhao, Yi Zeng, Bing Han
The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking neural network based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its…
Fernando Aguilar-Canto, Hiram Calvo, Peter König
This research integrates key concepts of Computational Neuroscience, including the Bienestock-CooperMunro (BCM) rule, Spike Timing-Dependent Plasticity Rules (STDP), and the Temporal Difference Learning algorithm, with an important structure of Deep Learning (Convolutional Networks) to create an architecture with the…
Indranil Dasgupta, David Keane, Elizabeth Lindley, Ihab Shaheen + 10 more
'Kay Tyerman' 'Franz Schaefer' 'Elke Wühl' 'Manfred J. Müller' 'Anja Bosy-Westphal' 'Hans Fors' 'Jovanna Dahlgren' 'Paul Chamney' 'Peter Wabel' 'Ulrich Moissl'] Background Bioimpedance spectroscopy (BIS) with a whole-body model to distinguish excess fluid from major body tissue hydration can provide objective…
Stephen P Povoski, Rafael E Jimenez, Wenle P Wang, Ronald X Xu
Background The primary goal of breast-conserving surgery (BCS) is to completely excise the tumor and achieve "adequate" or "negative" surgical resection margins while maintaining an acceptable level of postoperative cosmetic outcome. Nevertheless, precise determination of the adequacy of BCS has long been debated. In…