The article introduces conn2res, an open-source Python toolbox that implements biological neural networks as artificial neural networks to perform cognitive tasks. It leverages connectomics and artificial intelligence to explore how network architecture shapes computational capacity in brain networks. Reservoir computing, a versatile paradigm using high-dimensional, nonlinear dynamical systems, is applied here. conn2res allows researchers to input connectomes reconstructed using various techniques and impose multiple dynamical systems, from spiking neurons to memristive dynamics. The toolbox supports a wide range of cognitive tasks and offers flexibility in network architecture, dynamics, and task paradigms. It is interoperable with other Python packages and provides a comprehensive set of experimental configurations. The article discusses the fundamentals of reservoir computing, its application to biological neural circuits, and the potential of conn2res to study structure-function relationships in brain networks. It also highlights the versatility of the toolbox in exploring how network structure and dynamics influence computational performance, and its potential applications in clinical settings and comparative studies across species. The toolbox is designed to facilitate hypothesis-driven experiments and offers a modular architecture for various neuroscience problems, including understanding the link between structure and function, studying individual differences in behavior, and exploring the functional consequences of network perturbations. The article also discusses the potential of physical reservoir computing, using physical systems as reservoirs, and highlights the diverse range of physical substrates that can be used for reservoir computing. Overall, the article emphasizes the importance of connecting neuroscience and artificial intelligence to gain a deeper understanding of brain function and computation.The article introduces conn2res, an open-source Python toolbox that implements biological neural networks as artificial neural networks to perform cognitive tasks. It leverages connectomics and artificial intelligence to explore how network architecture shapes computational capacity in brain networks. Reservoir computing, a versatile paradigm using high-dimensional, nonlinear dynamical systems, is applied here. conn2res allows researchers to input connectomes reconstructed using various techniques and impose multiple dynamical systems, from spiking neurons to memristive dynamics. The toolbox supports a wide range of cognitive tasks and offers flexibility in network architecture, dynamics, and task paradigms. It is interoperable with other Python packages and provides a comprehensive set of experimental configurations. The article discusses the fundamentals of reservoir computing, its application to biological neural circuits, and the potential of conn2res to study structure-function relationships in brain networks. It also highlights the versatility of the toolbox in exploring how network structure and dynamics influence computational performance, and its potential applications in clinical settings and comparative studies across species. The toolbox is designed to facilitate hypothesis-driven experiments and offers a modular architecture for various neuroscience problems, including understanding the link between structure and function, studying individual differences in behavior, and exploring the functional consequences of network perturbations. The article also discusses the potential of physical reservoir computing, using physical systems as reservoirs, and highlights the diverse range of physical substrates that can be used for reservoir computing. Overall, the article emphasizes the importance of connecting neuroscience and artificial intelligence to gain a deeper understanding of brain function and computation.