Network Structure Governs Drosophila Brain Functionality

TL;DR

This study investigates how network topology shapes brain function by applying a large-scale network communication model to the adult Drosophila connectome. Across multiple activation mechanisms, activation patterns resemble real brain activity, and these patterns depend more on network structure than on neuron-level dynamics. The authors demonstrate strong visual-olfactory system separation and show that network distance, not spatial distance, governs activation, with even tiny perturbations capable of disrupting functional patterns. These insights underscore the pivotal role of connectivity in neural processing and have implications for both neuroscience and the design of brain-inspired AI.

Abstract

How intelligence emerges from living beings has been a fundamental question in neuroscience. However, it remains largely unanswered due to the complex neuronal dynamics and intricate connections between neurons in real neural systems. To address this challenge, we leveraged the largest available adult Drosophila connectome data set, and constructed a comprehensive computational framework based on simplified neuronal activation mechanisms to simulate the observed activation behavior within the connectome. The results revealed that even with rudimentary neuronal activation mechanisms, models grounded in real neural network structures can generate activation patterns strikingly similar to those observed in the actual brain. A significant discovery was the consistency of activation patterns across various neuronal dynamic models. This consistency, achieved with the same network structure, underscores the pivotal role of network topology in neural information processing. These results challenge the prevailing view that solely relies on neuron count or complex individual neuron dynamics. Further analysis demonstrated a near-complete separation of the visual and olfactory systems at the network level. Moreover, we found that the network distance, rather than spatial distance, is the primary determinant of activation patterns. Additionally, our experiments revealed that a reconnect rate of at least 0.1% was sufficient to disrupt the previously observed activation patterns. We also observed synergistic effects between the brain hemispheres: Even with unilateral input stimuli, visual-related neurons in both hemispheres were activated, highlighting the importance of interhemispheric communication. These findings emphasize the crucial role of network structure in neural activation and offer novel insights into the fundamental principles governing brain functionality.

Figures (12)

• Figure 1: Visualization and statistic properties of Drosophila connectome data set. (a) is a visualization of Drosophila connectome. The different colors represent different functional regions. (b) shows the degree distribution of the network. (c), (d) and (e) plot the distribution of degree, clustering coefficient, and eigenvector centrality in each hemisphere, respectively. (f) represents the correlation between degree distribution and neuron characteristics. We use nodes to represent neurons' cell bodies and use edges to represent the synapses when calculated statistic properties.
• Figure 2: Comparative test of intrinsic neurons' activation. For disturbing model, we conduct 5 repeated experiments and take the average and standard deviation. The dotted lines represent average activate rate in brain. In disturbing model, we set reconnect rate $p = 20\%$ and $\sigma$ = 0.8.
• Figure 3: Heatmap and schematic diagram illustrating the connections between areas related to vision and olfaction. (a) presents the heatmap, while (b) shows the schematic diagram of the connection relationships.
• Figure S1: An example of complex connection
• Figure S2: An example of original graph and dual graph