Brain connectivity meets reservoir computing

Abstract

The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? At a meso/macro-scale level of the connectivity, ANNs’ architectures are carefully engineered and such those design decisions have crucial importance in many recent performance improvements. On the other hand, BNNs exhibit complex emergent connectivity patterns at all scales. At the individual level, BNNs connectivity results from brain development and plasticity processes, while at the species level, adaptive reconfigurations during evolution also play a major role shaping connectivity. Ubiquitous features of brain connectivity have been identified in recent years, but their role in the brain’s ability to perform concrete computations remains poorly understood. Computational neuroscience studies reveal the influence of specific brain connectivity features only on abstract dynamical properties, although the implications of real brain networks topologies on machine learning or cognitive tasks have been barely explored. Here we present a cross-species study with a hybrid approach integrating real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks, allowing us to probe the potential computational implications of real brain connectivity patterns on task solving. We find results consistent across species and tasks, showing that biologically inspired networks perform as well as classical echo state networks, provided a minimum level of randomness and diversity of connections is allowed. We also present a framework, bio2art, to map and scale up real connectomes that can be integrated into recurrent ANNs. This approach also allows us to show the crucial importance of the diversity of interareal connectivity patterns, stressing the importance of stochastic processes determining neural networks connectivity in general.

Laminar RNNs: using biologically-inspired network topology on the cortical laminar level in memory tasks

“…In recent years, several open-source toolboxes have been created for building biologically-inspired RNNs and training and testing them on various tasks, including working memory tasks. These toolboxes include conn2res , which builds neuromorphic networks based on a derivation of RNNs called reservoir computing (Suárez et al 2021, 2024), as well as bio2art , which builds bio-instantiated networks based on classic RNNs such as the Elmann network (Goulas et al 2021, Damicelli et al 2022). Concurrently, a new neuroimaging framework has introduced a model-based subfield of connectomics, termed ‘laminar connectomics’.…”

“… (a) Constructing a laminar connectome using multimodal neuroimaging datasets and a data-driven model, resulting in a laminar-level characterization of the connectome (Shamir and Assaf 2021a). (b) Constructing a biologically-inspired network using a standard connectomics neuroimaging dataset and the bio2art framework for converting biological networks into artificial RNNs (Goulas et al 2021, Damicelli et al 2022). (c) Constructing a biologically-inspired laminar-level RNN, using the laminar connectome in the bio2art framework.…”

“…In this study we integrate two intricate models from the fields of multimodal neuroimaging (Shamir and Assaf 2023) and biologically-inspired DL memory architectures (Goulas et al 2021, Damicelli et al 2022). The ensuing hybrid model is based on a comprehensive network model of brain connectivity on the cortical layer level.…”

conn2res: A toolbox for connectome-based reservoir computing

“…Similarly, small-world topology [21][22][23] -prevalent across biological networks [6] contributes to the echo-state property of the reservoir [107], a measure of how flexibly network dynamics can untether from initial conditions, and promotes efficient signal propagation along the network [108]. Besides improving our understanding on how network architecture and dynamics affect the computational capacity of biological brain networks, implementing reservoirs informed by empirical connectomes can also be used for practical engineering purposes such as designing brainlike artificial neural network models or neuromorphic hardware [53,109,110].…”

An organized view of reservoir computing: a perspective on theory and technology development

“…56,57) Furthermore, RC seems to be a perfect tool to understand the relation between the connectome (the connectivity map between all neurons in the nervous system) and the cognitive abilities of the neural system. 58,59) Whereas the vision of a full understanding of the human brain is still far-fetched, the development of new tools will be helpful for a better understanding of ANNs and also physical neuromimetic systems. [60][61][62]…”