A stochastic framework to model axon interactions within growing neuronal populations

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.

Chemical and mechanical control of axon fasciculation and defasciculation

Neural networks are constructed through the development of robust axonal projections from individual neurons, which ultimately establish connections with their targets. In most animals, developing axons assemble in bundles to navigate collectively across various areas within the central nervous system or the periphery, before they separate from these bundles in order to find their specific targets. These processes, called fasciculation and defasciculation respectively, were thought for many years to be controlled chemically: while guidance cues may attract or repulse axonal growth cones, adhesion molecules expressed at the surface of axons mediate their fasciculation. Recently, an additional non-chemical parameter, the mechanical longitudinal tension of axons, turned out to play a role in axon fasciculation and defasciculation, through zippering and unzippering of axon shafts. In this review, we present an integrated view of the currently known chemical and mechanical control of axon:axon dynamic interactions. We highlight the facts that the decision to cross or not to cross another axon depends on a combination of chemical, mechanical and geometrical parameters, and that the decision to fasciculate/defasciculate through zippering/unzippering relies on the balance between axon:axon adhesion and their mechanical tension. Finally, we speculate about possible functional implications of zippering-dependent axon shaft fasciculation, in the collective migration of axons, and in the sorting of subpopulations of axons.

In silico framework to inform the design of repair constructs for peripheral nerve injury repair

Peripheral nerve injuries affect millions of people per year and cause loss of sensation and muscle control alongside chronic pain. The most severe injuries are treated through a nerve autograft; however, donor site morbidity and poor outcomes mean alternatives are required. One option is to engineer nerve replacement tissues to provide a supportive microenvironment to encourage nerve regeneration as an alternative to nerve grafts. Currently, progress is hampered due to a lack of consensus on how to arrange materials and cells in space to maximize rate of regeneration. This is compounded by a reliance on experimental testing, which precludes extensive investigations of multiple parameters due to time and cost limitations. Here, a computational framework is proposed to simulate the growth of repairing neurites, captured using a random walk approach and parameterized against literature data. The framework is applied to a specific scenario where the engineered tissue comprises a collagen hydrogel with embedded biomaterial fibres. The size and number of fibres are optimized to maximize neurite regrowth, and the robustness of model predictions is tested through sensitivity analyses. The approach provides an in silico tool to inform the design of engineered replacement tissues, with the opportunity for further development to multi-cue environments.

BCNNM: A Framework for in silico Neural Tissue Development Modeling

Cerebral (“brain”) organoids are high-fidelity in vitro cellular models of the developing brain, which makes them one of the go-to methods to study isolated processes of tissue organization and its electrophysiological properties, allowing to collect invaluable data for in silico modeling neurodevelopmental processes. Complex computer models of biological systems supplement in vivo and in vitro experimentation and allow researchers to look at things that no laboratory study has access to, due to either technological or ethical limitations. In this paper, we present the Biological Cellular Neural Network Modeling (BCNNM) framework designed for building dynamic spatial models of neural tissue organization and basic stimulus dynamics. The BCNNM uses a convenient predicate description of sequences of biochemical reactions and can be used to run complex models of multi-layer neural network formation from a single initial stem cell. It involves processes such as proliferation of precursor cells and their differentiation into mature cell types, cell migration, axon and dendritic tree formation, axon pathfinding and synaptogenesis. The experiment described in this article demonstrates a creation of an in silico cerebral organoid-like structure, constituted of up to 1 million cells, which differentiate and self-organize into an interconnected system with four layers, where the spatial arrangement of layers and cells are consistent with the values of analogous parameters obtained from research on living tissues. Our in silico organoid contains axons and millions of synapses within and between the layers, and it comprises neurons with high density of connections (more than 10). In sum, the BCNNM is an easy-to-use and powerful framework for simulations of neural tissue development that provides a convenient way to design a variety of tractable in silico experiments.

Cofilin regulates axon growth and branching of Drosophila γ-neurons

The mechanisms that control intrinsic axon growth potential, and thus axon regeneration following injury, are not well understood. Developmental axon regrowth of Drosophila mushroom body γ-neurons during neuronal remodeling offers a unique opportunity to study the molecular mechanisms controlling intrinsic growth potential. Motivated by the recently uncovered developmental expression atlas of γ-neurons, we here focus on the role of the actin-severing protein cofilin during axon regrowth. We show that Twinstar (Tsr), the fly cofilin, is a crucial regulator of both axon growth and branching during developmental remodeling of γ-neurons. tsr mutant axons demonstrate growth defects both in vivo and in vitro, and also exhibit actin-rich filopodial-like structures at failed branch points in vivo Our data is inconsistent with Tsr being important for increasing G-actin availability. Furthermore, analysis of microtubule localization suggests that Tsr is required for microtubule infiltration into the axon tips and branch points. Taken together, we show that Tsr promotes axon growth and branching, likely by clearing F-actin to facilitate protrusion of microtubules.

The prion-like domain of Drosophila Imp promotes axonal transport of RNP granules in vivo

Prion-like domains (PLDs), defined by their low sequence complexity and intrinsic disorder, are present in hundreds of human proteins. Although gain-of-function mutations in the PLDs of neuronal RNA-binding proteins have been linked to neurodegenerative disease progression, the physiological role of PLDs and their range of molecular functions are still largely unknown. Here, we show that the PLD of Drosophila Imp, a conserved component of neuronal ribonucleoprotein (RNP) granules, is essential for the developmentally-controlled localization of Imp RNP granules to axons and regulates in vivo axonal remodeling. Furthermore, we demonstrate that Imp PLD restricts, rather than promotes, granule assembly, revealing a novel modulatory function for PLDs in RNP granule homeostasis. Swapping the position of Imp PLD compromises RNP granule dynamic assembly but not transport, suggesting that these two functions are uncoupled. Together, our study uncovers a physiological function for PLDs in the spatio-temporal control of neuronal RNP assemblies.

The physiological role of prion-like domains (PLDs) within RNA-binding proteins is not well understood. Here, authors show in Drosophila that the PLD in the protein Imp is required for localization of ribonucleoprotein granules to axons and axonal remodelling.