What if we could simulate your brain?
By: Aarush Gupta (aarushgupta.com)
Paper: bioRxiv
Demo: playground.svbrain.xyz
Email: [email protected]
We are living through a revolution in computational neuroscience: the past decades have witnessed tremendous advances in our understanding of brain dynamics and neural networks. But despite these advances, bridging the gap between biologically plausible neural simulations and robust connectivity inference remains a significant challenge. To build systems that can accurately model and understand brain dynamics, we need a new approach — we need to integrate detailed neuronal models with state-of-the-art network inference techniques.
Over the past months, we've developed a comprehensive framework called Cerebrum that combines biologically plausible neural networks with rigorous mathematical modeling. We believe this represents an important step toward our long-term goal of understanding brain dynamics and connectivity, allowing researchers to simulate any neural network configuration and infer its underlying structure. Like traditional neural simulators, our framework supports detailed biophysical models. Unlike existing approaches, it spans multiple levels of biological detail and integrates modern machine learning techniques for connectivity inference.
Today's neural simulations face a fundamental dilemma: they must either sacrifice biological realism for computational efficiency or limit their scope to maintain biological detail. Simple artificial neural networks, while computationally efficient, ignore crucial biological mechanisms that shape brain function. On the other hand, detailed biophysical simulations capture these mechanisms but can only model small networks. Even implementing basic biological features requires extensive engineering, and capturing the full complexity of brain dynamics remains infeasible.
AI could change that landscape dramatically. By combining multiple levels of biological detail with modern machine learning techniques, we can create frameworks that adapt to different research needs while maintaining biological relevance. This flexibility is crucial because understanding the brain requires studying it at multiple scales - from individual ion channels to large-scale network dynamics.
To achieve this vision, we needed to solve major technical challenges. Our solution is Cerebrum, a prototype framework that integrates multiple neuronal models with a novel Graph Attention Network architecture. While we believe this is only an early step toward developing truly general-purpose brain modeling frameworks, it represents an exciting advance that provides a glimpse of what's possible.
Cerebrum's architecture is built around the principle of biological realism without sacrificing computational efficiency. At its core, the framework implements three distinct neuronal models, each serving different research needs. The Hodgkin-Huxley model provides the highest level of biological detail, capturing intricate ion channel dynamics. The Izhikevich model offers a compelling balance between biological realism and computational efficiency. The Adaptive Exponential Integrate-and-Fire model rounds out our toolkit, providing a simplified but mathematically rigorous approach to neuronal modeling.