Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments

• URL: http://arxiv.org/abs/2406.15488v1
• Date: Tue, 18 Jun 2024 01:41:57 GMT
• Title: Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments
• Authors: Yong Xie,
• Abstract summary: This paper introduces a novel brain-inspired AI framework, Orangutan. It simulates the structure and computational mechanisms of biological brains on multiple scales. I have developed a sensorimotor model that simulates human saccadic eye movements during object observation.
• Score: 2.8137865669570297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Achieving General Artificial Intelligence (AGI) has long been a grand challenge in the field of AI, and brain-inspired computing is widely acknowledged as one of the most promising approaches to realize this goal. This paper introduces a novel brain-inspired AI framework, Orangutan. It simulates the structure and computational mechanisms of biological brains on multiple scales, encompassing multi-compartment neuron architectures, diverse synaptic connection modalities, neural microcircuits, cortical columns, and brain regions, as well as biochemical processes including facilitation, feedforward inhibition, short-term potentiation, and short-term depression, all grounded in solid neuroscience. Building upon these highly integrated brain-like mechanisms, I have developed a sensorimotor model that simulates human saccadic eye movements during object observation. The model's algorithmic efficacy was validated through testing with the observation of handwritten digit images.

Related papers

• A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case [7.885957968654851]
  Insect visual systems excel at rapid and precise collision detection, despite relying on only tens of thousands of neurons. Researchers have identified collision-selective neurons in the locust's optic lobe, called lobula giant movement detectors (LGMDs) With a deeper understanding of LGMD neurons, LGMD-based models have significantly improved collision-free navigation in mobile robots.
  arXiv  Detail & Related papers  (2025-01-06T12:44:48Z)
• Brain-like Functional Organization within Large Language Models [58.93629121400745]
  The human brain has long inspired the pursuit of artificial intelligence (AI) Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli. In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs) This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
  arXiv  Detail & Related papers  (2024-10-25T13:15:17Z)
• Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
  It has long been known in both neuroscience and AI that ''binding'' between neurons leads to a form of competitive learning. We introduce Artificial rethinking together with arbitrary connectivity designs such as fully connected convolutional, or attentive mechanisms. We show that this idea provides performance improvements across a wide spectrum of tasks such as unsupervised object discovery, adversarial robustness, uncertainty, and reasoning.
  arXiv  Detail & Related papers  (2024-10-17T17:47:54Z)
• Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits [2.649646793770068]
  The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. This paper argues the possibility of reverse-engineering the brain through architecting a prototype of a brain-derived neuromorphic computing system.
  arXiv  Detail & Related papers  (2024-03-28T05:24:04Z)
• A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence [0.0]
  This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods. Despite the impressive advancements achieved by deep learning models, they still have shortcomings in abstract reasoning and causal understanding.
  arXiv  Detail & Related papers  (2024-01-03T09:46:36Z)
• 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)
• Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey) [9.14580723964253]
  Can artificial intelligence unlock the secrets of the human brain? Is it possible to enhance AI by tapping into the power of brain recordings? Our survey focuses on human brain recording studies and cutting-edge cognitive neuroscience datasets.
  arXiv  Detail & Related papers  (2023-07-17T06:54:36Z)
• In the realm of hybrid Brain: Human Brain and AI [0.0]
  Current brain-computer interface (BCI) technology is mainly on therapeutic outcomes. Recently, artificial intelligence (AI) and machine learning (ML) technologies have been used to decode brain signals. We envision the development of closed loop, intelligent, low-power, and miniaturized neural interfaces.
  arXiv  Detail & Related papers  (2022-10-04T08:35:34Z)
• Multimodal foundation models are better simulators of the human brain [65.10501322822881]
  We present a newly-designed multimodal foundation model pre-trained on 15 million image-text pairs. We find that both visual and lingual encoders trained multimodally are more brain-like compared with unimodal ones.
  arXiv  Detail & Related papers  (2022-08-17T12:36:26Z)
• Neuromorphic Artificial Intelligence Systems [58.1806704582023]
  Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. It presents an overview of currently available neuromorphic AI projects in which these limitations are overcome.
  arXiv  Detail & Related papers  (2022-05-25T20:16:05Z)

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.