The long-term objective of this research is to gain a better understanding of neural mechanisms and information processing principles involved in sensory acquisition in vertebrate sensory systems. The aspects of sensory processing under investigation are those in which the nervous system actively influences the quality and content of incoming sensory data to enhance signals of interest and to suppress unwanted background. Two related aspects of sensory acquisition are studied in the active electrosensory system of weakly electric fish: (1) the adaptive control of signal processing properties in sensory pathways, and (2) the motor control of peripheral sensory receptor structures. Certain fish have the ability to sense their surroundings by detecting extremely weak self-generated electric fields. This ability, referred to as electrolocation, enables them to hunt and navigate in the absence of visual cues. One thrust of this project is to understand how neural circuitry in the hindbrain electrosensory nucleus, the electrosensory lateral line lobe (ELL), helps weakly electric fish extract and enhance signals of behavioral relevance. In particular, this study seeks to characterize adaptive signal processing mechanisms and principles associated with descending pathways to the ELL. A second thrust involves the motor control aspects of electrosensory acquisition associated with the movement strategies used by weakly electric fish when performing a natural electrosensory task. These issues are addressed using a combination of approaches: 1) infrared video recording studies of prey capture behavior in weakly electric fish; 2) neurophysiological recordings from the ELL and associated neural circuitry; 3) biologically-based neural models and computer simulations of ELL signal processing; 4) application of adaptive signal processing and noise suppression concepts from engineering. This research is intended to lead to a better understanding of general principles of sensory acquisition that enable animals, including humans, to efficiently obtain and process information about their environment.
| Johnson, Erik C; Jones, Douglas L; Ratnam, Rama (2016) A minimum-error, energy-constrained neural code is an instantaneous-rate code. J Comput Neurosci 40:193-206 |
| Jones, Douglas L; Johnson, Erik C; Ratnam, Rama (2015) A stimulus-dependent spike threshold is an optimal neural coder. Front Comput Neurosci 9:61 |
| Nelson, M E; MacIver, M A (2006) Sensory acquisition in active sensing systems. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 192:573-86 |
| Chen, Ling; House, Jonathan L; Krahe, Rudiger et al. (2005) Modeling signal and background components of electrosensory scenes. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 191:331-45 |
| Goense, J B M; Ratnam, R (2003) Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 189:741-59 |
| Brandman, Relly; Nelson, Mark E (2002) A simple model of long-term spike train regularization. Neural Comput 14:1575-97 |
| Nelson, Mark E; MacIver, Malcolm A; Coombs, Sheryl (2002) Modeling electrosensory and mechanosensory images during the predatory behavior of weakly electric fish. Brain Behav Evol 59:199-210 |
| Nelson, Mark E (2002) Multiscale spike train variability in primary electrosensory afferents. J Physiol Paris 96:507-16 |
| MacIver, M A; Sharabash, N M; Nelson, M E (2001) Prey-capture behavior in gymnotid electric fish: motion analysis and effects of water conductivity. J Exp Biol 204:543-57 |
| MacIver, M A; Nelson, M E (2000) Body modeling and model-based tracking for neuroethology. J Neurosci Methods 95:133-43 |
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