Dendert

Dendert is a coined term used to describe a family of dendrite-inspired, tree-structured computation models and, in some research settings, corresponding neuromorphic hardware prototypes. The concept draws on biological neurons' dendritic trees, which process inputs locally before signals converge at the soma. In dendert models, computation is distributed along branching compartments, enabling complex nonlinear transformations with localized plasticity.

Each unit in a dendert network acts like a dendritic compartment with its own nonlinear activation, typically integrating synaptic-like inputs and producing outputs that influence parent and sibling compartments. The topology forms a tree or network of trees, allowing information to be routed and reconfigured dynamically. Learning rules emphasize local, compartment-level plasticity, often combined with a global regulator to achieve coordinated adaptation.

Researchers explore dendert-inspired architectures for energy-efficient, robust computation in domains such as sensory processing, pattern recognition, and robotic control. Simulated studies highlight advantages in fault tolerance, sparse connectivity, and scalable hierarchical processing. Early hardware demonstrations use memory elements arranged in branching layouts, aiming to mimic dendritic processing and reduce interconnect complexity, but such implementations remain experimental and exploratory.

As a term, dendert lacks formal standardization and is primarily found in niche synthetic-neuroscience and neuromorphic discussions. It serves as a conceptual umbrella for dendritic computation themes rather than a single agreed-upon model. More broadly, the ideas underpinning dendert align with ongoing work on dendritic computation and tree-structured neural networks.

See also: dendrite, dendritic computation, neuromorphic engineering.