Modelling and Verifying Dynamic Properties of Neuronal Networks in Coq

Abstract

Since the mid-1990s, formal verification has become increasingly important because it can provide guarantees that a software system is free of bugs and working correctly based on a provided model. Verification of biological and medical systems is a promising application of formal verification. Human neural networks have recently been emulated and studied as a biological system. Some recent research has been done on modelling some crucial neuronal circuits and using model checking techniques to verify their temporal properties. In large case studies, model checkers often cannot prove the given property at the desired level of generality. In this thesis, we provide a model using the Coq proof assistant and prove some properties concerning the dynamic behavior of some basic neuronal structures. Understanding the behavior of these modules is crucial because they constitute the elementary building blocks of bigger neuronal circuits. By using a proof assistant, we guarantee that the properties are true in the general case, that is, true for any input values, any length of input, and any amount of time. In this thesis, we define a model of human neural networks. We verify some properties of this model starting with properties of neurons. Neurons are the smallest unit in a human neuronal network. In the next step, we prove properties about functional structures of human neural networks which are called archetypes. Archetypes consist of two or more neurons connected in a suitable way. They are known for displaying some particular classes of behaviours, and their compositions govern several important functions such as walking, breathing, etc. The next step is verifying properties about structures that couple different archetypes to perform more complicated actions. We prove a property about one of these kinds of compositions. With such a model, there is the potential to detect inactive regions of the human brain and to treat mental disorders. Furthermore, our approach can be generalized to the verification of other kinds of networks, such as regulatory, metabolic, or environmental networks.