A Comprehensive Framework for Computational Neuroscience: Exploring Descriptive, Mechanistic, and Interpretive Models with Analysis

Abstract

This paper introduces a groundbreaking framework for computational neuroscience, uniting Descriptive, Mechanistic, and Interpretive models. Tailored to unravel the complexities of the brain, our framework categorizes and establishes a dynamic platform for real-time comparative analysis, offering insights into individual model strengths and weaknesses. Descriptive models (Neural Firing Rate, Population Rate, Neural Field) quantitatively capture neural phenomena without an explicit focus on underlying mechanisms. Mechanistic models (Hodgkin-Huxley, Synaptic, Biophysical) delve into intricate biological processes, simulating neural activity with detailed mechanisms. Interpretive models (Integrate-and-Fire, Generalized Linear) prioritize conceptual understanding, offering insights into the principles governing neural processes.