In the realm of hybrid Brain: Human Brain and AI
- URL: http://arxiv.org/abs/2210.01461v4
- Date: Wed, 17 Jan 2024 16:15:10 GMT
- Title: In the realm of hybrid Brain: Human Brain and AI
- Authors: Hoda Fares, Margherita Ronchini, Milad Zamani, Hooman Farkhani, and
Farshad Moradi
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: With the recent developments in neuroscience and engineering, it is now
possible to record brain signals and decode them. Also, a growing number of
stimulation methods have emerged to modulate and influence brain activity.
Current brain-computer interface (BCI) technology is mainly on therapeutic
outcomes, it already demonstrated its efficiency as assistive and
rehabilitative technology for patients with severe motor impairments. Recently,
artificial intelligence (AI) and machine learning (ML) technologies have been
used to decode brain signals. Beyond this progress, combining AI with advanced
BCIs in the form of implantable neurotechnologies grants new possibilities for
the diagnosis, prediction, and treatment of neurological and psychiatric
disorders. In this context, we envision the development of closed loop,
intelligent, low-power, and miniaturized neural interfaces that will use brain
inspired AI techniques with neuromorphic hardware to process the data from the
brain. This will be referred to as Brain Inspired Brain Computer Interfaces
(BI-BCIs). Such neural interfaces would offer access to deeper brain regions
and better understanding for brain's functions and working mechanism, which
improves BCIs operative stability and system's efficiency. On one hand, brain
inspired AI algorithms represented by spiking neural networks (SNNs) would be
used to interpret the multimodal neural signals in the BCI system. On the other
hand, due to the ability of SNNs to capture rich dynamics of biological neurons
and to represent and integrate different information dimensions such as time,
frequency, and phase, it would be used to model and encode complex information
processing in the brain and to provide feedback to the users. This paper
provides an overview of the different methods to interface with the brain,
presents future applications and discusses the merger of AI and BCIs.
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) - 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) - 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) - 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) - Digital twin brain: a bridge between biological intelligence and
artificial intelligence [12.55159053727258]
We propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence.
The DTB consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications.
arXiv Detail & Related papers (2023-08-03T03:36:22Z) - Constraints on the design of neuromorphic circuits set by the properties
of neural population codes [61.15277741147157]
In the brain, information is encoded, transmitted and used to inform behaviour.
Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
arXiv Detail & Related papers (2022-12-08T15:16:04Z) - BrainCog: A Spiking Neural Network based Brain-inspired Cognitive
Intelligence Engine for Brain-inspired AI and Brain Simulation [16.83583563493804]
Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience.
We present the Brain-inspired Cognitive Intelligence Engine (BrainCog) for creating brain-inspired AI and brain simulation models.
arXiv Detail & Related papers (2022-07-18T11:53:31Z) - 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) - POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with
Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations.
The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
arXiv Detail & Related papers (2022-01-19T09:26:34Z) - 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) - Brain Co-Processors: Using AI to Restore and Augment Brain Function [2.3986080077861787]
We introduce brain co-processors, devices that combine decoding and encoding in a unified framework using artificial intelligence (AI)
Brain co-processors can be used for a range of applications, from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory.
We describe a new framework for developing brain co-processors based on artificial neural networks, deep learning and reinforcement learning.
arXiv Detail & Related papers (2020-12-06T21:06:28Z)
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.