The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest recently to identify noise structures that can be integrated linearly in a way that preserves reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a complementary de-noising approach that is mediated by focal dendritic spikes. Dendritic spikes might seem to be unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation increases neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of conditions that can be processed by neuronal populations.
Model Type: Synapse; Dendrite; Neuron or other electrically excitable cell
Region(s) or Organism(s): Neocortex
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Neocortex primary motor area pyramidal layer 5 corticospinal cell
Currents: I Na,t; I Potassium
Model Concept(s): Synaptic Integration; Active Dendrites; Information transfer
Simulation Environment: NEURON
Implementer(s): Polsky, Alon [alonpol at tx.technion.ac.il]
References:
Poleg-Polsky A. (2019). Dendritic spikes expand the range of well-tolerated population noise structures. The Journal of neuroscience : the official journal of the Society for Neuroscience. 39 [PubMed]