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Peter Robin Hiesinger
Goodreads Author
Born
Germany
Website
Genre
Influences
Douglas Adams
Member Since
May 2021
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The Self-Assembling Brain: How Neural Networks Grow Smarter
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The Self-assembling Brain: How Neural Networks Grow Smarter, Library Edition
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Peter’s Recent Updates
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Peter Hiesinger
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44 other people
liked
Brian Clegg's review
of
Why Information Grows: The Evolution of Order, from Atoms to Economies:
"Something that is absolutely essential to understand this book, subtitled 'The evolution of order from atoms to economies', on the fascinating topic of the nature of information in the world, and its relationship with the economy, is that the author "
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Peter Hiesinger
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1 other person
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Arttu's review
of
The Self-Assembling Brain: How Neural Networks Grow Smarter:
"Despite finishing the book in the first days of 2025, I can guarantee that the topics covered are very relevant, even without any reference to Large Language Models or the underlying breakthrough technologies such as transformers and their incredible"
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Peter Hiesinger
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1 other person
liked
Yuxi Liu's review
of
The Ontogeny of Information: Developmental Systems and Evolution (Science and Cultural Theory):
"The aim of the book is to propose a replacement to the nature-nurture dichotomy, by something more "dialectical". The concrete proposals are two:
* On the biology side, Oyama proposes developmental systems theory to replace the central dogma of molec" Read more of this review » |
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Peter Hiesinger
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2 other people
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Ferdi Ridvan Kiral's review
of
The Self-Assembling Brain: How Neural Networks Grow Smarter:
"I am one of the lucky people on earth who had the chance to get to know and work with Robin in person. As an accomplished neuroscientist, his approach to scientific questions/problems is strikingly different than those who find joy to follow the main"
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“The code is a metaphor that works well for the genetic code or the rules of a cellular automaton. The code is bad metaphor for the continuously changing states of neurons as they run through their algorithmic programs. Imagine looking for a code in space and time for rule 110, iteration 1,234, position 820-870. To study the mechanism that led to that state, we look at the point before, the relation of how it happened, in all available detail. Is that useful? In the case of the rule 110 automaton, the same rule applies everywhere, so the instance reveals something general about the whole. This is the hope of the biological experiment as well. But what happens if the rule changes with every iteration, as discussed for transcription factor cascades? To describe changing rules of algorithmic growth for every instance in time and space and in different systems is not only a rather large endeavor, it also suffers from the same danger of undefined depth. It is a description of the system, a series of bits of endpoint information, not a description of a code sufficient to create the system. The code is the 'extremely small amount of information to be specified genetically,' as Willshaw and von der Malsburg put it, that is sufficient to encode the unfolding of information under the influence of time and energy. The self-assembling brain.”
― The Self-Assembling Brain: How Neural Networks Grow Smarter
― The Self-Assembling Brain: How Neural Networks Grow Smarter
