Towards Reverse-Engineering the Brain: A Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D Integrated Circuits

Core Concepts

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 consisting of artificial electronic, ionic, photonic materials, devices, and circuits with dynamicity resembling the bio-plausible molecular, neuro/synaptic, neuro-circuit, and multi-structural hierarchical macro-circuits of the brain.

Abstract

The content discusses the possibility of reverse-engineering the brain through a brain-derived neuromorphic computing approach. It highlights the key challenges faced by previous efforts in neuromorphic computing and proposes four fundamental scientific and technological gaps that need to be addressed. The paper outlines the following key aspects: Nano-electronic Brain-derived Computing: Explores the implementation of spatio-temporal activity patterns following the networked learning principle at different levels of the hierarchy (materials, devices, networks, and computational theory). Designs a hierarchical and reconfigurable network of interacting nano-electronic devices and circuits to build a continuous-time dynamical system (CTDS) where the properties of the neural computation task are encoded within the dynamics of the material or the network itself. Nano-optoelectronic Neuromorphic Computing: Investigates nano-optoelectronic neuromorphic computing features such as bio-derived artificial optoelectronic neurons, nanophotonic synapses, and photonic neural networks. Explores photonic memristive materials and devices capable of self-reconfiguration to emulate biological synaptic plasticity. 3D Integrated Neuromorphic Computing Circuits and Architecture: Integrates electronic neural networks, photonic neural networks, and learning algorithms into a 3D electronic-photonic integrated circuit (EPIC) neural network. Leverages the high density, connectivity, and energy efficiency of 3D EPIC to achieve brain-derived neuromorphic computing. Learning System Integration and Experimental Testbed: Creates a prototype neuromorphic computing hardware system that incorporates brain-derived algorithms and structure to perform complex tasks. Enables iterative development of neuro-morphic computing technology and mechanistic models of human brain function through an experimental testbed. The paper argues that the proposed brain-derived neuromorphic computing approach can potentially reproduce the uniquely flexible and adaptive nature of human intelligence with extreme scalability and energy efficiency.

Stats

The human brain recognizes or associates features from partial and conflicting information at ~20 W power levels. Each neuron may be connected to up to ~10,000 other neurons, passing signals via as many as 1,000 trillion synaptic connections, equivalent by some estimates to a computer with a 1 trillion bit per second processor at ~10 MW power level. The Generative Pre-trained Transformer (GPT) requires pretraining with more than 12 million USD worth of energy on GPU-based systems just for training for GPT-3, and far more for GPT-4.

Quotes

"The human brain has immense learning capabilities at extreme energy efficiencies and scales that no artificial system has been able to match." "Here, we opine that a brain-derived—rather than a brain-inspired—architecture will lead to a paradigm shift, enabling the development of intelligent agents that can work in tandem with humans on complex tasks in noisy, unpredictable environments." "If successful, the resulting neuromorphic computing paradigm may reproduce the uniquely flexible and adaptive nature of human intelligence with extreme scalability and energy efficiency."

Key Insights Distilled From

Towards Reverse-Engineering the Brain

by S. J. Ben Yo... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19724.pdf

Deeper Inquiries

How can the proposed brain-derived neuromorphic computing approach be extended to incorporate other sensory modalities beyond vision and audition to achieve more comprehensive human-like intelligence?

In order to incorporate other sensory modalities beyond vision and audition into the brain-derived neuromorphic computing approach, the system can be expanded to include modules that simulate the processing of touch, taste, and smell. This would involve integrating sensors that can detect tactile sensations, taste stimuli, and odors, and converting this sensory input into signals that can be processed by the neuromorphic system. By incorporating these additional sensory modalities, the artificial brain prototype can achieve a more comprehensive understanding of the environment, similar to how the human brain processes information from multiple senses simultaneously. This multi-modal integration would enable the artificial agents to have a more holistic perception of the world and make decisions based on a wider range of sensory inputs, leading to more human-like intelligence.

What are the potential ethical and societal implications of developing highly capable, brain-derived artificial agents that can work alongside humans on complex tasks?

The development of highly capable, brain-derived artificial agents that can work alongside humans on complex tasks raises several ethical and societal implications. One major concern is the potential displacement of human workers as these artificial agents become more proficient at performing tasks traditionally done by humans. This could lead to job loss and economic disruption in various industries. Additionally, there are concerns about the ethical implications of creating artificial agents that have the potential to outperform humans in certain tasks, raising questions about the ethical treatment of these agents and their impact on human society. Furthermore, there are concerns about privacy and data security when using brain-derived artificial agents that have the ability to process and analyze vast amounts of data. Issues related to data protection, algorithm bias, and transparency in decision-making processes need to be addressed to ensure that these artificial agents are used ethically and responsibly. Additionally, there may be societal implications related to the acceptance and integration of these artificial agents into various aspects of daily life, including potential changes in social dynamics and human-AI interactions.

Given the hierarchical and distributed nature of the human brain, how can the proposed 3D EPIC architecture be further optimized to better emulate the multi-scale information processing and communication observed in biological neural networks?

To better emulate the multi-scale information processing and communication observed in biological neural networks, the proposed 3D EPIC architecture can be further optimized in several ways. Firstly, the architecture can be expanded to include more layers and levels of hierarchy to mimic the complex structure of the human brain. This would involve creating interconnected neural networks at different scales, from individual neurons to larger brain regions, to enable information processing and communication across multiple levels of abstraction. Additionally, the 3D EPIC architecture can be enhanced with dynamic reconfigurability and adaptability to replicate the plasticity and flexibility of biological neural networks. By incorporating mechanisms for synaptic plasticity and network reorganization, the artificial brain prototype can learn and adapt to new information in a manner similar to the human brain. Furthermore, the optimization of the 3D EPIC architecture can involve the development of advanced learning algorithms that capture the distributed and parallel processing capabilities of biological neural networks. By implementing bio-plausible local learning algorithms that enable unsupervised and self-supervised learning mechanisms, the artificial brain prototype can better emulate the sophisticated information processing and communication observed in the human brain.