TY - JOUR T1 - Dendrites enable a robust mechanism for neuronal stimulus selectivity JF - bioRxiv DO - 10.1101/023200 SP - 023200 AU - Romain D. Cazé AU - Sarah Jarvis AU - Amanda J. Foust AU - Simon R. Schultz Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/07/18/023200.abstract N2 - 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. Non-linear 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 non-preferred stimuli. Using a multi-subunit non-linear model, we demonstrate that selectivity can arise from the spatial distribution of synapses. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Contrary to an equivalent linear model, our model can maintain stimulus selectivity even when 50% of synapses fail or when more than 50% of dendrites fail. We then use a Layer 2/3 biophysical neuron model to show that our implementation is consistent with recent experimental observations, of a mixture of selectivities in dendrites, that can differ from the somatic selectivity, and of hyperpolarization broadening somatic tuning without affecting dendritic tuning. Our model predicts that an initially non-selective 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.Author summary From the stripes of your shirt to the sound of your grandmother’s name, your neurons are capable of selecting among stimuli. How do they perform this selection? The standard model assumes that a neuron receives the strongest inputs from neurons encoding its preferred stimulus. While this can explain many observations, it neglects dendrites, the neuron’s ”antennae” for receiving inputs. Here we propose an alternate non-linear model for stimulus selectivity which incorporates dendrites fitting with the latest experimental observations and more robust than a linear model all other things being equal. This additional robustness enabled by dendrites offer new possibilities for neuromorphic chip design. ER -