Electrosensory neural responses to natural electro-communication stimuli are distributed along a continuum

• Published in: PloS one Vol. 12; no. 4; p. e0175322
• Main Authors: Sproule, Michael K J, Chacron, Maurice J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.04.2017; Public Library of Science (PLoS)
• Subjects: Action Potentials; Animals; Biology and Life Sciences; Black ghost; Brain; Cell Communication; Classification; Clusters; Coding; Communication; Computer and Information Sciences; Electric fields; Electric Fish - physiology; Electric instruments; Electric Stimulation; Electrosensory system; Lateral line; Medicine and Health Sciences; Morphology; Neural coding; Neural networks; Neurons; Neurophysiology; Neurosciences; Physical Sciences; Physiology; Pyramidal cells; Pyramidal Cells - physiology; Research and Analysis Methods; Sensory receptors; Skin; Stimuli; Canada; Montreal Quebec Canada
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0175322

Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.