How does the brain work and how can we understand it? I want to make it a habit to report some of the thoughts about the brain that marked me most during the past twelve month at the end of each year – with the hope to advance and structure the progress in the part of my understanding of the brain that is not immediately reflected in journal publications. Enjoy the read! And check out previous year-end write-ups: 2018, 2019, 2020, 2021, 2022.
During the last year, I have continued to work on the ideas described during previous year-end write-ups, resulting in a project proposal that is currently under evaluation. I will use this year’s write-up to talk about something different, although related, a recent book by Peter Robin Hiesinger: The Self-Assembling Brain.
Hiesinger, based in Berlin, is working in the field of developmental neurobiology. However, this book is rather a cross-over between multiple disciplines, ranging from developmental neurobiology, circuit neuroscience, artificial intelligence, robotics, and many side-branches of the mentioned disciplines. Hiesinger masterfully assembles the perspectives of the different fields around his own points of interest. For example, his introductory discussion about the emergence of the field of artificial intelligence in the 1950s is one of the most insightful account that I have read about this period. He tells the stories how key figures like von Neumann, Minsky, Rosenblatt or McCarthy and their relationships and personalities influenced the further development of the field.
The main hypothesis of Hiesinger’s book is that the genetic code does not encode the endpoint of the system (e.g., the map of brain areas, the default state network, thalamocortical loops, interneuron connectivity, etc.). According to him, and I think that most neuroscientists would agree, the neuronal circuits of the brain are not directly encoded in the genetic code. Instead, the simple genetic code needs to unfold in time in order to generate the complex brain. More importantly, it is, according to Hiesinger, necessary to actually run the code in order to find out what the endpoint of the system is. Let’s pick two analogies brought up in the book to illustrate this unfolding idea.
First, in the preface Hiesinger describes how an alien not familiar with life on earth finds an apple seed. Upon analysis of the apple seed, the alien realizes that there are complex and intricate genetic codes in the apple seed, and it starts to see beauty and meaning in these patterns. However, the analysis based on its structural content would not enable the alien to predict the purpose of the apple seed. This is only possible by development (unfolding) of the seed into an apple tree. Unfolding therefore is the addition of both time and energy to the seed.
Second, Hiesinger connects the unfolding idea with the field of cellular automata, and in particular with the early work of Stephen Wolfram, a very influential but also controversial personality of complexity research, and his cellular automaton named rule 110. The 110 automaton is a very simple rule (the rule is described in this wikipedia article) that is applied to a row of 1’s and 0’s and results in another binary row. The resulting row is again subject to rule 110, etc., leading to a two-dimensional pattern as computed here in Matlab:
The pattern is surprisingly complex, despite the simplicity of the rule. For example, how can one explain the large solitary black triangle in the middle right? How the vertical line of equally sized triangles in the center that ends so abruptly? The answers are not obvious. These examples show that a very simple rule can lead to very complex patterns. From Hiesinger’s point of view, it is important to state that the endpoint of the system, let’s say line 267, cannot be derived from the rule – unless it is developed (unfolded) for exactly 267 iterations. Hiesinger believes that this analogy can be transferred to the relationship between the genetic code and the architecture of the brain.
The rest of Hiesinger’s book discusses the implications of this concept. As a side-effect, Hiesinger illustrates how complex the genome is in comparison with the simple 101 automaton. Not only is the code richer and more complex, but it is also, due to transcription factor cascades that include feedback loops, a system of rules where rules, unlike rule 110, change over time with development. Therefore, according to Hiesinger, the classical research in developmental biology that tries to match single genes (or a few genes) onto a specific function is ill-guided. He convincingly argues that the examples for such relationships that have been found as “classical” examples for the field (e.g., genes coding for cell adhesion molecules involved in establishing synaptic specificity) are probably the exception rather than the rule.
The implication of the unfolding hypothesis for research on artificial intelligence is, interestingly, very similar. That is, to stop treating intelligent systems like engineered systems, where the rules can be fully designed. Since the connection between the generative rules and the resulting endpoint system cannot be understood unless their unfolding in time is observed, Hiesinger is in favor of research that embraces this limitation. He suggests to build models based on a to-be-optimized (“genetic”) code and, letting go of the full control, make them unfold in time to generate an artificial intelligence. Of course, this idea reminds of the existing field of evolutionary algorithms. However, in classic evolutionary algorithms, evolving properties of the code are more or less directly mapped to properties of the network or the agent. If I understood the book right, it would be in Hiesinger’s spirit to make this mapping more indirect through developmental steps that allow for higher complexity, even though it would also obfuscate the mechanistic connection between rules and models.
Overall, I find Hiesinger’s approach interesting. He shows mastery of other fields as well, but it is pncing point that the idea of the unfolding code, the self-assembling brain, is reasonable, and he also brings up examples of research that goes into that direction. However, as a note of caution to myself, accepting the idea of self-assembly seemed a bit like giving in when faced with complexity. There is a long history of complexity research that agreed on the fact that things are too complex to be understood. Giving in resulted in giving vague names to the complex phenomena, which seemed to explain away the unknown but in reality only gave it a name. For example, the concepts of emergence, autopoiesis or the free energy principle are in my opinion relatively abstract and non-concrete concepts that contributed to the halting of effective research by preventing incremental progress on more comprehensible questions. I get similar vibes when Hiesinger states that the connections between the self-organizing rules and the resulting product are too complex to be understood and require unfolding in time. The conclusion of this statement is either that everything is solved, because the final explanation is unfolding in time of a code that cannot be understood; or it is that nothing can be solved because it is too complex. In both cases, there seems to be some sort of logical dead end. But this just as a note of caution to myself.
So, what is the use of the unfolding hypothesis about the organization and self-assembly of the brain? I think it is useful because it might help guide future efforts. I agree with Hiesinger that the field of “artificial intelligence” should shift its focus on self-organized and growing neuronal networks. In my opinion, work focusing on evolutionary algorithms, actor-based reinforcement learning (e.g., something called neuroevolution), neural architecture search or more generally AutoML go into the right direction. Right now it seems a long shot to say this, but my guess is that these forms of artificial neuronal networks will become dominant within 10 years, potentially replacing artificial neuronal networks based on backpropagation. – After finishing the write-up, I came across a blog post by Sebastian Risi that is a good starting point with up-to-date references on self-assembling algorithms from the perspective of computer science and machine learning – check it out if you want to know more.
For neurobiology, on the other hand, the unfolding hypothesis means that an understanding of the brain requires understanding of its self-assembly. Self-assembly can happen, as Hiesinger stresses, during development, but it can also happen in the developped circuit through neuronal plasticity (synaptic plasticity on short and long time scales, as well as intrinsic plasticity). I have written about this self-organizing aspect of neuronal circuits in my last year’s write-up. Beyond that, if we were to accept the unfolding hypothesis as central to the organization of the brain, we would also be pressured to drop some of the beautiful models of the brain that are based on engineering concepts like modularity. For example, the idea of the cortical column, the canonical microcircuit, or the concept of segregated neuronal cell types. All those concepts have been obviously very useful frameworks or hypotheses to advance our understanding of the brain, but if the unfolding of the brain is indeed the main concept of its assembly, these engineering concepts are unlikely (although not impossible) to turn out to be true.
It is possible that most of the ideas are already contained in the first few pages, and the rest of the book is less dense and feels often a bit redundant. But especially the historical perspective in the beginning and also some later discussions are very interesting. Language-wise, the book could have benefitted from a bit more inference by the editor to avoid unnaturally sounding sentences, especially during the first couple of pages. But this is only a minor drawback of an otherwise clear and nice presentation.
The book is structured into ten “seminars”, which are each of them a slightly confusing mix of book chapter and lecture style. Each of the “seminars” is accompanied by a staged discussion between four actors: a developmental biologist, an AI researcher, a circuit neuroscientist and a robotics engineer (see the photo above). Theoretically, this is a great idea. In practice, it works only half of the time, and the book loses a bit of its natural flow because the direction is a bit missing. However, these small drawbacks are acceptable because the main ideas are interesting and enthusiastically presented.
Altogether, Hiesinger’s book is worth the time to read it, and I can recommend it to anybody interested in the intersection of biological brains, artificial neuronal networks and self-organized systems.
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