Large-scale biophysically detailed models of microcircuits and mesocircuits have provided unprecedented insights into cellular diversity, synaptic connectivity and network activity. However, their computational demands limit their use for studying emergent properties at the network and behavioural levels. Conversely, simplified models, such as mean-field, population-based, or rate-based representations, enable efficient large-scale simulations but often sacrifice biological realism. Bridging these approaches is crucial to building models which are both mechanistically grounded and computationally scalable.

This workshop will explore the integration of biophysically and morphologically detailed models with simplified representations to advance our understanding of brain dynamics. By bringing together leading experts in

computational neuroscience, this workshop will discuss state-of-the-art methodologies, trade-offs between different modelling approaches, and how these models can be leveraged to gain mechanistic insights into learning, memory, and cognition.