Dendrites enable a robust mechanism for neuronal stimulus selectivity

File Description SizeFormat 
Caze_NC2017_preprint.pdfAccepted version1.09 MBAdobe PDFView/Open
Title: Dendrites enable a robust mechanism for neuronal stimulus selectivity
Authors: Cazé, RD
Jarvis, S
Foust, AJ
Schultz, SR
Item Type: Journal Article
Abstract: Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.
Issue Date: 23-Aug-2017
Date of Acceptance: 23-Mar-2017
ISSN: 0899-7667
Publisher: Massachusetts Institute of Technology Press (MIT Press)
Start Page: 2511
End Page: 2527
Journal / Book Title: Neural Computation
Volume: 29
Copyright Statement: © 2017 Massachusetts Institute of Technology
Keywords: MD Multidisciplinary
Artificial Intelligence & Image Processing
Publication Status: Published online
Conference Place: United States
Appears in Collections:Faculty of Engineering

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx