Neural systems engineering

Reviews of Modern Physics, 1999

Journal of Computer Science and Technology/Journal of computer science and technology, 2024

Lecture Notes in Computer Science, 2014

The inner workings of the brain as a biological information processing system remain largely a mystery to science. Yet there is a growing interest in applying what is known about the brain to the design of novel computing systems, in part to explore hypotheses of brain function, but also to see if brain-inspired approaches can point to novel computational systems capable of circumventing the limitations of conventional approaches, particularly in the light of the slowing of the historical exponential progress resulting from Moore's Law. Although there are, as yet, few compelling demonstrations of the advantages of such approaches in engineered systems, a number of large-scale platforms have been developed recently that promise to accelerate progress both in understanding the biology and in supporting engineering applications. SpiNNaker (Spiking Neural Network Architecture) is one such large-scale example, and much has been learnt in the design, development and commissioning of this machine that will inform future developments in this area. † a discussion of the major challenges impeding progress in computer technology (Section 2); † an introduction to the brain from a computer engineer's perspective (Section 3); † metrics for comparing computers with brains (Section 4); † the major challenges in building brain-inspired machines (Section 5) and an overview of current large-scale projects building brain-inspired machines (Section 6);

Procedia Computer Science 145 , 153–157, 2018

Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence (AI) design implementations. However, it was soon became obvious that they are unable to fulfil their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures. Abstract Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence (AI) design implementations. However, it was soon became obvious that they are unable to fulfil their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures.

In this research project, the features of biological and artificial neural networks were studied by reviewing the existing works of authorities in print and electronics on biological and artificial neural networks. The features were then assessed and evaluated and comparative analysis of the two networks was carried out. The metrics such as structures, layers, size and functional capabilities of neurons, learning capabilities, style of computation, processing elements, processing speed, connections, strength, information storage, information transmission, communication media selection, signal transduction and fault tolerance were used as basis for comparison. A major finding in the research showed that artificial neural networks served as the platform for neuro-computing technology and as such a major driver of the development of neuron-like computing system. It was also discovered that Information processing of the future computer systems will greatly be influenced by the adoption of artificial neural network model.

Entropy

When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first artificial neural networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unexpected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate fro...

Like a computer, the human brain inputs, processes, stores and outputs information. Yet the brain evolved along different design principles from those of the Von Neumann architecture that lies behind most computers in operation today. A comparison of human and computer information processing styles suggests basic differences in: 1. Control (Central vs. Distributed), 2. Input (Sequential vs. Parallel), 3. Output (Exclusive vs. Overlaid), 4. Storage (by Address vs. by Content), 5. Initiation (Input vs. Process driven) and 6. Self Processing (Low vs. High). The conclusion is that the brain is a different type of information processor, not an inferior one. This suggests replacing technological utopianism with socio-technical progress, where computers plus people form more powerful systems than either alone. For this to occur, the computer must change its role from clever actor to simple assistant.

2018

Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence design implementations. However, it was soon became obvious that they are unable to fulfill their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures.

2002 Annual Conference Proceedings

Academic and commercial research teams are currently developing a new generation of devices that will interact with, incorporate, and/or emulate living nervous systems. Neural prostheses to restore hearing, mobility or sight will offer a wider range of function; robotic devices will become more effective with "neuromorphic" control systems; fundamentally new methods for processing information will be motivated by biological systems. Neural Engineering is the intellectual force behind these developments, supported by recent advances in cellular neurobiology, microfabrication and neural modeling. Based on decades of quantitative approaches to increase our understanding of neural systems, bioengineers are now beginning to design neural systems and neural interfaces. Neural engineers have new tools to control aspects of these systems such as guided axon growth and multielectrode arrays for stimulation and recording. In addition to potential applications attracting the attention of biotech and defense industries, these efforts in turn increase our understanding of natural neural systems.

In this research project, the features of biological and artificial neural networks were studied by reviewing the existing works of authorities in print and electronics on biological and artificial neural networks. The features were then assessed and evaluated and comparative analysis of the two networks was carried out. The metrics such as structures, layers, size and functional capabilities of neurons, learning capabilities, style of computation, processing elements, processing speed, connections, strength, information storage, information transmission, communication media selection, signal transduction and fault tolerance were used as basis for comparison. A major finding in the research showed that artificial neural networks served as the platform for neuro-computing technology and as such a major driver of the development of neuron-like computing system. It was also discovered that Information processing of the future computer systems will greatly be influenced by the adoption of artificial neural network model.

In our book “Neural Engineering: Representation, Transformations and Dynamics”, MIT Press 2003, Chris Eliasmith and I present a unified framework that describes the function of neurobiological systems through the application of the quantitative tools of systems engineering. Our approach is not revolutionary, but more evolutionary in nature, building on many current and generally disparate approaches to neuronal modeling. The basic premise is that the principles of information processing apply to neurobiological systems.

The human brain is still a 21 st century mystery, an organ of impassable complexity. It has being compared to various inventions, it was sometimes compared to a telephone switchboard and also compared to mathematical logic by renown scientist Gorge Boole but it is now seen as a sort of biological computer, with mushy hardware and software evolving from life experiences. This paper elucidates and also makes a critical review on the analytical study of the computer chip with the human brain exploring their similarities, differences, the latest trends, prospects and the challenges ahead. It however concludes that due to increasing parallel processing of computer chip, the prediction that the processing power of the hardware will match the human brain might be realistic.

Studies In History and Philosophy of Science Part A, 2010

Anower Hossain

The process of transferring data from a biological brain to an artificial brain is a difficult and intricate one. Scientists are exploring different methods to achieve this, such as mapping the neural connections of the biological brain and replicating them in the artificial brain, or using advanced algorithms to translate biological brain information into a language that can be understood by the artificial brain. Although there is still much research to be done in this field, the potential benefits of this technology could be immense. These benefits include restoring lost or damaged brain function and enhancing cognitive abilities beyond what is currently possible for humans.

… architectures based on …, 2001

This book is the result of a series of International Workshops organised by the EmerNet project on Emergent Neural Computational Architectures based on Neuroscience sponsored by the Engineering and Physical Sciences Research Council (EPSRC). The overall aim of the book is to present a broad spectrum of current research into biologically inspired computational systems and hence encourage the emergence of new computational approaches based on neuroscience. It is generally understood that the present approaches for computing do not have the performance, flexibility and reliability of biological information processing systems. Although there is a massive body of knowledge regarding how processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far.