Precise design principles exist for artificial connectomes and neural networks
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Peer-reviewed literature demonstrates that biological connectomes are governed by specific design principles and that these principles can be successfully translated into artificial neural networks and machine learning architectures.
The map of synaptic connectivity among neurons in the brain shapes the computations that neural circuits may perform. Inferring the design principles of neural connectomes is, therefore, fundamental for understanding brain development and architecture, neural computations, learning, and behavior. Here, we learn probabilistic generative models for the connectomes of the olfactory bulb of zebrafish, part of the mouse visual cortex, and of
C. elegans
. We show that, in all cases, models that rely on a surprisingly small number of simple biological and physical features are highly accurate in replicating a wide range of properties of the measured circuits. Specifically, they accurately predict the existence of individual synapses and their strength, distributions of synaptic indegree and outdegree of the neurons, frequency of sub-network motifs, and more. Furthermore, we simulate synthetic circuits generated by our model for the olfactory bulb of zebrafish and show that they replicate the computation that the real circuit performs in response to olfactory cues. Finally, we show that specific failures of our models reflect missing design features that we uncover by adding latent features to the model. Thus, our results reflect surprisingly simple design principles of real connectomes in three different systems and species, and offer a novel general computational framework for analyzing connectomes and linking structure and function in neural circuits.
A
bstract
Grand efforts in neuroscience are working toward mapping the connectomes of many new species, including the near completion of the
Drosophila melanogaster
. It is important to ask whether these models could benefit artificial intelligence. In this work we ask two fundamental questions: (1) where and when biological connectomes can provide use in machine learning, (2) which design principles are necessary for extracting a good representation of the connectome. Toward this end, we translate the motor circuit of the
C. Elegans
nematode into artificial neu-ral networks at varying levels of biophysical realism and evaluate the outcome of training these networks on motor and non-motor behavioral tasks. We demonstrate that biophysical realism need not be upheld to attain the advantages of using biological circuits. We also establish that, even if the exact wiring diagram is not retained, the architectural statistics provide a valuable prior. Finally, we show that while the
C. Elegans
locomotion circuit provides a powerful inductive bias on locomotion problems, its structure may hinder performance on tasks unrelated to locomotion such as visual classification problems.
The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the functional connections between different brain regions, has provided valuable insights through various advanced analysis techniques developed over the years. Similarly, neural networks, inspired by the brain's architecture, have achieved notable success in diverse applications but are often noted for their lack of interpretability. In this paper, we propose a novel approach that bridges neural networks and human brain functions by leveraging brain-inspired techniques. Our approach, grounded in the insights from the functional connectome, offers scalable ways to characterize topology of large neural networks using stable statistical and machine learning techniques. Our empirical analysis demonstrates its capability to enhance the interpretability of neural networks, providing a deeper understanding of their underlying mechanisms.
Some scholars believe thot Cognitive Science is the attempt to achieve in artificial systems what has already been achieved in the brain. Others, by contrast, argue that the study of ideal adaptive mechanisms could go on without reference to the brain. The author points out that the brain may not be the ideal cognitive device because of biological limitotions on its capacity. Although it may not be ideal, it is still of major interest to cognitive scientists because of the great interest in the human mind, and, in addition, because at this moment the brain is the most important single reservoir of odoptive mechanisms. The paper discusses several areas of neuroscience which are likely to shed light on mechanisms of adaptation. The study of simple nervous systems is likely to reveal important design principles of cognitive devices. The study of complex nervous systems will, of course, exert a major influence. The study of such systems leads to certain general principles concerning the neural circuits involved in complex odoptive behaviors: (1) There exist innate specialized systems for the learning of many specific behaviors that at first might appeor to be purely cultural. (2) There is no evidence for the existence of any all‐purpose computer in the brain. (3) There are many surprising dissociations manifested by the specialized systems in the brain, e.g., a special system for recognition of faces as against other visual patterns. (4) The study of the nervous system enables on
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