Morphological Computation and Learning to Learn In Natural Intelligent Systems And AI

• URL: http://arxiv.org/abs/2004.02304v1
• Date: Sun, 5 Apr 2020 20:11:42 GMT
• Title: Morphological Computation and Learning to Learn In Natural Intelligent Systems And AI
• Authors: Gordana Dodig-Crnkovic
• Abstract summary: Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science.
• Score: 2.487445341407889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: At present, artificial intelligence in the form of machine learning is making impressive progress, especially the field of deep learning (DL) [1]. Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. Learning from nature is a two-way process as discussed in [2][3][4], computing is learning from neuroscience, while neuroscience is quickly adopting information processing models. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science and to practical applications of artificial intelligence.

Related papers

• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Neuronal Auditory Machine Intelligence (NEURO-AMI) In Perspective [0.0]
  We present an overview of a new competing bio-inspired continual learning neural tool Neuronal Auditory Machine Intelligence (Neuro-AMI) In this report, we present an overview of a new competing bio-inspired continual learning neural tool Neuronal Auditory Machine Intelligence (Neuro-AMI)
  arXiv  Detail & Related papers  (2023-10-14T13:17:58Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural Networks [2.0646127669654826]
  We present NeuroCERIL, a brain-inspired neurocognitive architecture that uses a novel hypothetico-deductive reasoning procedure. We show that NeuroCERIL can learn various procedural skills in a simulated robotic imitation learning domain. We conclude that NeuroCERIL is a viable neural model of human-like imitation learning.
  arXiv  Detail & Related papers  (2022-11-11T19:56:11Z)
• From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks [56.20123080771364]
  Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning. CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
  arXiv  Detail & Related papers  (2022-01-20T17:07:03Z)
• A brain basis of dynamical intelligence for AI and computational neuroscience [0.0]
  More brain-like capacities may demand new theories, models, and methods for designing artificial learning systems. This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
  arXiv  Detail & Related papers  (2021-05-15T19:49:32Z)
• To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI [1.0152838128195467]
  One of the ambitions of artificial intelligence is to root artificial intelligence deeply in basic science. This paper presents the grand challenges of artificial intelligence research for the next 20 years.
  arXiv  Detail & Related papers  (2020-09-11T22:38:38Z)
• A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits [91.3755431537592]
  Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics.
  arXiv  Detail & Related papers  (2020-03-23T14:55:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.