Software Libraries

Sep 1, 2021 ... We find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both ... The brain is a deep and complex recurrent neural network. The models of information processing that have dominated computational neuroscience, by contrast, are ... Jan 9, 2024 ... 43. Francis, N.A. ... Sequential transmission of task-relevant information in cortical neuronal networks. Cell Rep. 2022; 39, 110878. Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ... Sep 4, 2023 ... ... neuronal networks or whether it is related to informational load. ... To explore this topic, we used an in vitro neural network of cortical ... The recent surge of graph embedding and graph neural networks also call for renewed and novel methodologies for heterogeneous information network analysis. As a ... Abstract. The concept of Learning by Stimulus Avoidance (LSA) has been proposed in recent literature, and the methods of avoiding stimuli: action, prediction, ... Neural networks, decision trees, simulation and ... The machine learning approach and the Information Virtue – Information Token – Informational. Jan 10, 2024 ... (2007, 2010), for instance, used a network composed of two coupled rings, representing sensory and frontal cortical modules, to model the ... Dec 19, 2019 ... Abstract: Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks.