Topic: voltage-gated ion channels

Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. However, it is unclear how qualitative aspects of a dendritic tree, such as its branched morphology, its repetition of presynaptic inputs, voltage-gated ion channels, electrical properties and complex synapses, determine neural computation beyond this apparent nonlinearity. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong,

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)