Zobrazeno 1 - 10
of 3 714
pro vyhledávání: '"Friston, Karl"'
Autor:
Friston, Karl, Heins, Conor, Verbelen, Tim, Da Costa, Lancelot, Salvatori, Tommaso, Markovic, Dimitrije, Tschantz, Alexander, Koudahl, Magnus, Buckley, Christopher, Parr, Thomas
This paper describes a discrete state-space model -- and accompanying methods -- for generative modelling. This model generalises partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active infe
Externí odkaz:
http://arxiv.org/abs/2407.20292
We discuss an approach to mathematically modelling systems made of objects that are coupled together, using generative models of the dependence relationships between states (or trajectories) of the things comprising such systems. This broad class inc
Externí odkaz:
http://arxiv.org/abs/2406.11630
Epilepsy is one of the most common neurological disorders, and many patients require surgical intervention when medication fails to control seizures. For effective surgical outcomes, precise localisation of the epileptogenic focus - often approximate
Externí odkaz:
http://arxiv.org/abs/2403.20324
Autor:
Friston, Karl J., Salvatori, Tommaso, Isomura, Takuya, Tschantz, Alexander, Kiefer, Alex, Verbelen, Tim, Koudahl, Magnus, Paul, Aswin, Parr, Thomas, Razi, Adeel, Kagan, Brett, Buckley, Christopher L., Ramstead, Maxwell J. D.
Recent advances in theoretical biology suggest that basal cognition and sentient behaviour are emergent properties of in vitro cell cultures and neuronal networks, respectively. Such neuronal networks spontaneously learn structured behaviours in the
Externí odkaz:
http://arxiv.org/abs/2312.07547
Autor:
Ororbia, Alexander, Friston, Karl
This review motivates and synthesizes research efforts in neuroscience-inspired artificial intelligence and biomimetic computing in terms of mortal computation. Specifically, we characterize the notion of mortality by recasting ideas in biophysics, c
Externí odkaz:
http://arxiv.org/abs/2311.09589
Autor:
Friston, Karl J., Da Costa, Lancelot, Tschantz, Alexander, Kiefer, Alex, Salvatori, Tommaso, Neacsu, Victorita, Koudahl, Magnus, Heins, Conor, Sajid, Noor, Markovic, Dimitrije, Parr, Thomas, Verbelen, Tim, Buckley, Christopher L
This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move -
Externí odkaz:
http://arxiv.org/abs/2311.10300
Autor:
Pezzulo, Giovanni, D'Amato, Leo, Mannella, Francesco, Priorelli, Matteo, Van de Maele, Toon, Stoianov, Ivilin Peev, Friston, Karl
This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and
Externí odkaz:
http://arxiv.org/abs/2310.14810
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating
Externí odkaz:
http://arxiv.org/abs/2309.12095
Autor:
Lee, Sungwoo, Oh, Younghyun, An, Hyunhoe, Yoon, Hyebhin, Friston, Karl J., Hong, Seok Jun, Woo, Choong-Wan
Building autonomous --- i.e., choosing goals based on one's needs -- and adaptive -- i.e., surviving in ever-changing environments -- agents has been a holy grail of artificial intelligence (AI). A living organism is a prime example of such an agent,
Externí odkaz:
http://arxiv.org/abs/2309.05999
We characterise the dynamics of neuronal activity, in terms of field theory, using neural units placed on a 2D-lattice modelling the cortical surface. The electrical activity of neuronal units was analysed with the aim of deriving a neural field mode
Externí odkaz:
http://arxiv.org/abs/2308.10645