*** Already with several important authors confirmed, including Michael Levin, Alex Gomez-Marin, Matthew Cobb, Blake Richards, Bud Mishra, Stuart Kauffman & Ricard Solé ***
The title of this Research Topic paraphrases a well-known aphorism in statistics. There is an intentional and challenging mistake in it: Metaphor and Analogy are used as synonyms, yet they are not. In science, metaphors are not intended to provide a solution to a given problem and have no explanatory power. In contrast, Analogies do have this power and enable us to make connections to understand the functioning of a given system based on the knowledge acquired on another system. Much the same way as scientific hypotheses, Metaphors and Analogies are transitory, always adjusting to technological advances. The Brain-Computer is usually referred to as a metaphor, but it should be thought of as an analogy instead. This analogy has raised a harsh debate in the scientific community, with some taking it literally, whereas the very meaning of analogies implies only a partial overlap of properties.
The Brain is the product of about 600 million years of biological evolution, the Computer is a human-made artifact whose construction narrative started less than 200 years ago. In trying to understand what is usually regarded as the most complex structure in the Universe, metaphors and analogies might prove fruitful. For example, analogies and knowledge derived from Network Science may contribute to understand how (parts of) the brain(s) work, learn about its degree of flexibility, neural network structure, and the functional role of synaptic distribution and density. We may also ask if self-organization phenomena play a role in shaping Brain (any brain) architecture and evolution. Clearly, experimental research will be the checkpoint for all analogy-driven hypotheses. Indeed, single-neuron research suggests that certain neurons may function as complex, multi-unit processing systems, and dendrites may serve as nonlinear computing subunits (sort of "mini-brains").
The relationships between Brain and Computer encompass so vast a spectrum of topics from Natural Sciences, Mathematics, Psychology, and Philosophy that here we need to narrow our scope for this topic. By educated guess, a tentative list of the topics (and keywords) that will likely be discussed might be the following:
- Brain architecture, evolution, and functioning (adaptive response; self-organization; extended cognition)
- Neural Networks and Computational Neuroscience (Artificial neural networks)
- Network Science (network evolution; brain flexibility; self-organization)
- Computer Science (distributed-centralized architectures)
- Information theory (reliability-error checking; efficiency-vs-speed of information)
- Game theory (decentralized neural architecture; asymmetric information distribution)
- Quantum brain - quantum computer (Church-Turing thesis; Computational Complexity; Information Asymmetry and Costly Signaling)
- Neurobiology Experimental research (Single-neuron research; Loss-of-function experiments/CRISPR technology)
- Artificial Intelligence (Imitation games; Turing test for AI)
Many other equally important topics will not be included here, since they deserve a full Research Topic on their own: Consciousness/Mind, Cognition, Behavior, Intelligence, Language, and Culture.
We hope to stimulate a multi-, trans- and inter-disciplinary authorship of the articles in this Research Topic, though we are fully aware of the communication issues between scientists of such diverse scientific backgrounds.
In Matthew Cobb's words, "… the very fact that this debate is taking place suggests that we may indeed be approaching the end of the computational metaphor" (2020). Yet by the same token, one can reach the opposite conclusion, and hopefully so, for Metaphors - and Analogies to a lesser extent - are invaluable in enabling scientists to be more creative.
*** Already with several important authors confirmed, including Michael Levin, Alex Gomez-Marin, Matthew Cobb, Blake Richards, Bud Mishra, Stuart Kauffman & Ricard Solé ***
The title of this Research Topic paraphrases a well-known aphorism in statistics. There is an intentional and challenging mistake in it: Metaphor and Analogy are used as synonyms, yet they are not. In science, metaphors are not intended to provide a solution to a given problem and have no explanatory power. In contrast, Analogies do have this power and enable us to make connections to understand the functioning of a given system based on the knowledge acquired on another system. Much the same way as scientific hypotheses, Metaphors and Analogies are transitory, always adjusting to technological advances. The Brain-Computer is usually referred to as a metaphor, but it should be thought of as an analogy instead. This analogy has raised a harsh debate in the scientific community, with some taking it literally, whereas the very meaning of analogies implies only a partial overlap of properties.
The Brain is the product of about 600 million years of biological evolution, the Computer is a human-made artifact whose construction narrative started less than 200 years ago. In trying to understand what is usually regarded as the most complex structure in the Universe, metaphors and analogies might prove fruitful. For example, analogies and knowledge derived from Network Science may contribute to understand how (parts of) the brain(s) work, learn about its degree of flexibility, neural network structure, and the functional role of synaptic distribution and density. We may also ask if self-organization phenomena play a role in shaping Brain (any brain) architecture and evolution. Clearly, experimental research will be the checkpoint for all analogy-driven hypotheses. Indeed, single-neuron research suggests that certain neurons may function as complex, multi-unit processing systems, and dendrites may serve as nonlinear computing subunits (sort of "mini-brains").
The relationships between Brain and Computer encompass so vast a spectrum of topics from Natural Sciences, Mathematics, Psychology, and Philosophy that here we need to narrow our scope for this topic. By educated guess, a tentative list of the topics (and keywords) that will likely be discussed might be the following:
- Brain architecture, evolution, and functioning (adaptive response; self-organization; extended cognition)
- Neural Networks and Computational Neuroscience (Artificial neural networks)
- Network Science (network evolution; brain flexibility; self-organization)
- Computer Science (distributed-centralized architectures)
- Information theory (reliability-error checking; efficiency-vs-speed of information)
- Game theory (decentralized neural architecture; asymmetric information distribution)
- Quantum brain - quantum computer (Church-Turing thesis; Computational Complexity; Information Asymmetry and Costly Signaling)
- Neurobiology Experimental research (Single-neuron research; Loss-of-function experiments/CRISPR technology)
- Artificial Intelligence (Imitation games; Turing test for AI)
Many other equally important topics will not be included here, since they deserve a full Research Topic on their own: Consciousness/Mind, Cognition, Behavior, Intelligence, Language, and Culture.
We hope to stimulate a multi-, trans- and inter-disciplinary authorship of the articles in this Research Topic, though we are fully aware of the communication issues between scientists of such diverse scientific backgrounds.
In Matthew Cobb's words, "… the very fact that this debate is taking place suggests that we may indeed be approaching the end of the computational metaphor" (2020). Yet by the same token, one can reach the opposite conclusion, and hopefully so, for Metaphors - and Analogies to a lesser extent - are invaluable in enabling scientists to be more creative.