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pro vyhledávání: '"Lansner, A"'
Neural networks that can capture key principles underlying brain computation offer exciting new opportunities for developing artificial intelligence and brain-like computing algorithms. Such networks remain biologically plausible while leveraging loc
Externí odkaz:
http://arxiv.org/abs/2406.04733
Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brains spiking neural networks have the computational capacity to achieve complex pattern recognition and cognitive functions effor
Externí odkaz:
http://arxiv.org/abs/2406.03054
Associative memory or content addressable memory is an important component function in computer science and information processing and is a key concept in cognitive and computational brain science. Many different neural network architectures and lear
Externí odkaz:
http://arxiv.org/abs/2401.00335
We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We achieved this by transforming the non-spiking feedforward Bayesian Confidence Propagation Neural Network (B
Externí odkaz:
http://arxiv.org/abs/2305.03866
Theories and models of working memory (WM) were at least since the mid-1990s dominated by the persistent activity hypothesis. The past decade has seen rising concerns about the shortcomings of sustained activity as the mechanism for short-term mainte
Externí odkaz:
http://arxiv.org/abs/2304.06626
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brain’s spiking neural networks have the computational capacity to achieve complex pattern recognition and cognitive functions ef
Externí odkaz:
https://doaj.org/article/20a0669079014af5aae0ef1063c7fd5d
Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. How
Externí odkaz:
http://arxiv.org/abs/2206.15036
Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data. In this work, we use the Bayesian Confidence Propagation Neural Network (BCPNN) model de
Externí odkaz:
http://arxiv.org/abs/2106.15546
Autor:
Podobas, Artur, Svedin, Martin, Chien, Steven W. D., Peng, Ivy B., Ravichandran, Naresh Balaji, Herman, Pawel, Lansner, Anders, Markidis, Stefano
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other -- less-known -- machine learning algorithms with a mature and solid theo
Externí odkaz:
http://arxiv.org/abs/2106.05373
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently exte
Externí odkaz:
http://arxiv.org/abs/2005.03476