RT Journal Article
SR Electronic
T1 Dendritic spikes expand the range of well-tolerated population noise structures
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 454215
DO 10.1101/454215
A1 Alon Poleg-Polsky
YR 2019
UL http://biorxiv.org/content/early/2019/04/11/454215.abstract
AB 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 performance for a broad category of computational tasks, including analog and binary discrimination, as well as for a range of correlated and uncorrelated noise structures, some of which cannot be adequately resolved with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of brain states and presynaptic population activity dynamics can be reliably de-noised by biologically-realistic neurons.Significance Statement Noise, or random variability, is a prominent feature of the neuronal code and poses a fundamental challenge for information processing. To reconcile the surprisingly accurate output of the brain with the inherent noisiness of biological systems, previous work examined signal integration in idealized neurons. The notion that emerged from this body of work is that accurate signal representation relies largely on input averaging in neuronal dendrites. In contrast to the prevailing view, I show that de-noising in simulated neurons with realistic morphology and biophysical properties follows a different strategy: dendritic spikes act as classifiers that assist in extracting information from a variety of noise structures that have been considered before to be particularly disruptive for reliable brain function.