The chapter introduces conn2res, an open-source Python toolbox designed to implement biological neural networks as artificial neural networks using reservoir computing. Reservoir computing is a versatile paradigm that leverages high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. conn2res is modular, allowing researchers to input connectomes reconstructed using various techniques and impose multiple dynamical systems. The toolbox supports a wide range of network architectures and dynamics, making it suitable for studying the link between structure and function in brain networks. It offers a comprehensive set of experimental configurations, including cognitive tasks, local dynamics, and linear optimization algorithms. The chapter also provides a detailed overview of the conn2res workflow, including the fetch task dataset, set connectivity matrix, simulate reservoir dynamics, learn, and assess performance stages. Additionally, it presents three applied examples to illustrate the toolbox's capabilities, such as quantifying the effect of different network dynamics on task performance, comparing memory capacity in empirical and rewired connectomes, and comparing memory capacity across different species. The discussion highlights the versatility of conn2res in addressing various neuroscience questions and its potential for physical reservoir computing.The chapter introduces conn2res, an open-source Python toolbox designed to implement biological neural networks as artificial neural networks using reservoir computing. Reservoir computing is a versatile paradigm that leverages high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. conn2res is modular, allowing researchers to input connectomes reconstructed using various techniques and impose multiple dynamical systems. The toolbox supports a wide range of network architectures and dynamics, making it suitable for studying the link between structure and function in brain networks. It offers a comprehensive set of experimental configurations, including cognitive tasks, local dynamics, and linear optimization algorithms. The chapter also provides a detailed overview of the conn2res workflow, including the fetch task dataset, set connectivity matrix, simulate reservoir dynamics, learn, and assess performance stages. Additionally, it presents three applied examples to illustrate the toolbox's capabilities, such as quantifying the effect of different network dynamics on task performance, comparing memory capacity in empirical and rewired connectomes, and comparing memory capacity across different species. The discussion highlights the versatility of conn2res in addressing various neuroscience questions and its potential for physical reservoir computing.