Premium
Receptive field organization across multiple electrosensory maps. II. Computational analysis of the effects of receptive field size on prey localization
Author(s) -
Maler Leonard
Publication year - 2009
Publication title -
journal of comparative neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.855
H-Index - 209
eISSN - 1096-9861
pISSN - 0021-9967
DOI - 10.1002/cne.22120
Subject(s) - electroreception , electric fish , receptive field , biology , predation , population , lobe , prey detection , fish <actinopterygii> , neuroscience , biological system , sensory system , anatomy , ecology , fishery , demography , sociology
The electric fish Apteronotus leptorhynchus emits a high‐frequency electric organ discharge (EOD) sensed by specialized electroreceptors (P‐units) distributed across the fish's skin. Objects such as prey increase the amplitude of the EOD over the underlying skin and thus cause an increase in P‐unit discharge. The resulting localized intensity increase is called the electric image and is detected by its effect on the P‐unit population; the electric image peak value and the extent to its spreads are cues utilized by these fish to estimate the location and size of its prey. P‐units project topographically to three topographic maps in the electrosensory lateral line lobe (ELL): centromedial (CMS), centrolateral (CLS), and lateral (LS) segments. In a companion paper I have calculated the receptive fields (RFs) in these maps: RFs were small in CMS and very large in LS, with intermediate values in CLS. Here I use physiological data to create a simple model of the RF structure within the three ELL maps and to compute the response of these model maps to simulated prey. The Fisher information (FI) method was used to compute the optimal estimates possible for prey localization across the three maps. The FI predictions were compared with behavioral studies on prey detection. These comparisons were used to frame alternative hypotheses on the functions of the three maps and on the constraints that RF size and synaptic strength impose on weak signal detection and estimation. J. Comp. Neurol. 516:394–422, 2009. © 2009 Wiley‐Liss, Inc.