Zobrazeno 1 - 10
of 555
pro vyhledávání: '"Drion P"'
Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which poses questions reg
Externí odkaz:
http://arxiv.org/abs/2405.02038
Spiking neural networks are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes. This property allows neural networks to make asynchronous and sparse computations and therefore drasti
Externí odkaz:
http://arxiv.org/abs/2306.03623
Publikováno v:
Journal of Stem Cells and Regenerative Medicine, Vol 11, Iss 1, Pp 7-17 (2015)
Objectives: The restorative properties of platelets, through the local release of growth factors, are used in various medical areas. This article reviews fundamental and clinical research relating to platelet-rich plasma applied to tendinous lesions.
Externí odkaz:
https://doaj.org/article/2c4e5e7cf7414643b3658baca5e8d50e
Publikováno v:
Wellbeing, Space and Society, Vol 7, Iss , Pp 100220- (2024)
Using data representing the Australian community (n=1083), this study examines whether there is a link between the way individuals perceive their natural living environment and their mental health state. Linear mixed model regressions are used to ass
Externí odkaz:
https://doaj.org/article/dd34651ef06c4bfba602f8238372b8f4
Publikováno v:
Neural Networks, 2023
Training recurrent neural networks is known to be difficult when time dependencies become long. In this work, we show that most standard cells only have one stable equilibrium at initialisation, and that learning on tasks with long time dependencies
Externí odkaz:
http://arxiv.org/abs/2106.01001
Autor:
Broek, Jantine A. C., Drion, Guillaume
Neuronal excitability is the phenomena that describes action potential generation due to a stimulus input. Commonly, neuronal excitability is divided into two classes: Type I and Type II, both having different properties that affect information proce
Externí odkaz:
http://arxiv.org/abs/2011.00297
This paper proposes a methodology to extract a low-dimensional integrate-and-fire model from an arbitrarily detailed single-compartment biophysical model. The method aims at relating the modulation of maximal conductance parameters in the biophysical
Externí odkaz:
http://arxiv.org/abs/2006.10113
Recurrent neural networks (RNNs) provide state-of-the-art performances in a wide variety of tasks that require memory. These performances can often be achieved thanks to gated recurrent cells such as gated recurrent units (GRU) and long short-term me
Externí odkaz:
http://arxiv.org/abs/2006.05252
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. S
Externí odkaz:
http://arxiv.org/abs/1812.09113
Publikováno v:
Neuromorphic Computing and Engineering, Vol 4, Iss 2, p 024007 (2024)
Spiking neural networks (SNNs) are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes. This property allows neural networks to make asynchronous and sparse computations and therefore
Externí odkaz:
https://doaj.org/article/7c98751a5d434b98ad131d1d2641b8e1