Zobrazeno 1 - 10
of 622
pro vyhledávání: '"Rajesh, P. N."'
Autor:
N'dri, Antony W., Gebhardt, William, Teulière, Céline, Zeldenrust, Fleur, Rao, Rajesh P. N., Triesch, Jochen, Ororbia, Alexander
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding. Specifically, we review the general framework of predictive processing in the context of neurons that emit discrete a
Externí odkaz:
http://arxiv.org/abs/2409.05386
Autor:
Maheshwari, Shishir, Rajesh, Kandala N V P S, Kanhangad, Vivek, Acharya, U Rajendra, Kumar, T Sunil
Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)- based encephalogram (EEG) channel
Externí odkaz:
http://arxiv.org/abs/2404.09493
Autor:
Oliveira, Nigini, Li, Jasmine, Khalvati, Koosha, Barragan, Rodolfo Cortes, Reinecke, Katharina, Meltzoff, Andrew N., Rao, Rajesh P. N.
Constructing a universal moral code for artificial intelligence (AI) is difficult or even impossible, given that different human cultures have different definitions of morality and different societal norms. We therefore argue that the value system of
Externí odkaz:
http://arxiv.org/abs/2312.17479
In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neu
Externí odkaz:
http://arxiv.org/abs/2308.11809
Autor:
Salvatori, Tommaso, Mali, Ankur, Buckley, Christopher L., Lukasiewicz, Thomas, Rao, Rajesh P. N., Friston, Karl, Ororbia, Alexander
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the
Externí odkaz:
http://arxiv.org/abs/2308.07870
Predictive coding has emerged as a prominent model of how the brain learns through predictions, anticipating the importance accorded to predictive learning in recent AI architectures such as transformers. Here we propose a new framework for predictiv
Externí odkaz:
http://arxiv.org/abs/2210.13461
Objective: A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Approach: To achieve goal-directed closed-loop neu
Externí odkaz:
http://arxiv.org/abs/2210.11478
Autor:
Rémi Philippe, Rémi Janet, Koosha Khalvati, Rajesh P. N. Rao, Daeyeol Lee, Jean-Claude Dreher
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or
Externí odkaz:
https://doaj.org/article/96781795e0164e34b116ff3523d0a835
Inspired by Gibson's notion of object affordances in human vision, we ask the question: how can an agent learn to predict an entire action policy for a novel object or environment given only a single glimpse? To tackle this problem, we introduce the
Externí odkaz:
http://arxiv.org/abs/2207.03593