NEURAL Simulator: A Computational Exploration Platform for Large-Scale Biologically Plausible Neural Networks

NEURAL Simulator is a computational platform for simulating large-scale biologically plausible neural networks, designed to provide multi-scale modeling capabilities from the molecular to the network level for neuroscience research. By simulating the physicochemical properties of biological nervous systems, it bridges biology and computational neuroscience, supporting applications in basic research, disease modeling, brain-inspired computing, and education. It is a key tool for exploring the mysteries of the brain and developing brain-inspired intelligence.

Significance of Biologically Plausible Neural Networks

The human brain has approximately 86 billion neurons and trillions of synapses, with dynamic properties involving multi-level mechanisms such as ion channels, neurotransmitters, dendritic computation, and plasticity. Unlike artificial neural networks, biologically plausible neural networks aim to simulate the characteristics of real nervous systems at the cellular and circuit levels. Although they have higher computational costs, they can capture the unique computational paradigms of the biological brain (e.g., sustained firing for working memory maintenance, stochastic resonance in decision-making), providing support for verifying theoretical hypotheses and guiding the design of brain-inspired chips.

Technical Architecture

• Model Level: Supports integration from spiking models to multi-compartment Hodgkin-Huxley models. Synapse models cover chemical/electrical synapses and various plasticity rules, allowing users to balance accuracy and efficiency.
• Network Level: Provides flexible connection patterns (regular grids, random small-world networks, structured connections from real anatomical data), with neuron population attributes and connection rules defined via code/configurations.
• Computational Architecture: Uses multi-threaded CPUs, GPU acceleration (CUDA/OpenCL), and distributed memory parallelism to support ultra-large-scale simulations of millions of neurons.

Multi-scale Modeling

• Micro Scale: Simulates the electrophysiological properties of individual neurons (ion channels, calcium dynamics).
• Meso Scale: Builds circuit models of specific brain regions (e.g., cortical columns, hippocampal place cell networks).
• Macro Scale: Connects multi-region models to enable whole-brain or large-scale subsystem simulations.

Application Scenarios

• Basic Research: Verify computational hypotheses, predict experimental results, and design new experimental protocols.
• Disease Research: Build disease models for epilepsy (ion channel disorders), Alzheimer's disease (synaptic degeneration), schizophrenia (connectome abnormalities), etc., and test treatment strategies.
• Brain-Inspired Computing: Guide the design of neuromorphic chips and migrate network architectures and algorithms to low-power platforms.
• Education: Serve as a teaching tool to visualize phenomena such as action potential propagation and synaptic transmission.

Current Challenges

1. Trade-off Between Scale and Detail: Whole-brain detailed simulations require enormous computational resources; key details need to be reasonably simplified.
2. Parameter Determination: Many model parameters are difficult to measure accurately; parameter optimization tools (genetic algorithms, Bayesian optimization) need to be integrated.
3. Standardization of Model Validation: A unified evaluation standard is lacking; a benchmark test suite needs to be established.

Future Directions

• Deeply integrate experimental data platforms and import data from neuroelectrophysiological databases.
• Use machine learning to assist model simplification and automatically eliminate non-critical details.
• Implement real-time interactive simulations to support dynamic parameter adjustment and response observation.

NEURAL Simulator represents an important advancement in computational neuroscience, providing a powerful tool for simulating large-scale biologically plausible neural networks. It connects molecular biology, cellular electrophysiology, and systems neuroscience, facilitating multi-scale exploration of the brain's mysteries. With the improvement of computational power and model accuracy, it will play a key role in understanding brain functions, treating brain diseases, and developing brain-inspired intelligence, making it a platform worth attention in the field of neural computing.