Topic: dendritic morphology

Most models of neural computation used in artificial intelligence and neuroscience use point neurons, attributing computational complexity to the network level. This approach overlooks the computational complexity of single biological neurons. Neural dendrites exhibit many different nonlinear dynamics, temporal-spatial filtering, and internal feedback and amplification mechanisms (London & Häusser 2005). Abstracting the entire dendritic arbor into a single compartment thus omits useful computational properties (Poirazi et al. 2003). Three main factors contribute to the integration process: (1)

Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental for exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks — a framework designed to illuminate how morpho-electric properties map to dendritic events, and how these dendritic events shape neuronal output. Through its graphical interface, users can access the