The long-term objective of this research is to understand neural mechanisms and information processing principles involved in sensory acquisition. Humans and other animals receive a constant barrage of sensory input from the environment. The nervous system must continuously process this massive amount of data to extract useful information and to suppress irrelevant background noise. This project investigates mechanisms of sensory acquisition using the electric sense of weakly electric fish as a model system. These animals are able to sense their surroundings by detecting modulations in a weak (millivolt-level) self-generated electric field. This ability, referred to as electrolocation, enables them to hunt and navigate in the dark. Electrosensory signals arising from small aquatic prey are much weaker than many of the background signals experienced by the animal. The research proposed here helps elucidate how the nervous system solves this challenging information processing problem. This study uses a combination of experimental and modeling approaches to investigate how the electrosensory system extracts behaviorally relevant signals from the background. Neurophysiological recordings from electrosensory afferent nerve fibers are used to guide the development of an accurate model of afferent response dynamics. Recordings of the physical stimulus (transdermal voltage) in free-swimming fish are used to characterize the amplitude and spectral properties of the background. Information processing principles are explored in an empirically constrained model of electrosensory processing in three topographic maps of the hindbrain electrosensory lateral line lobe (ELL). Each of the three maps is known to have unique spatial and temporal filtering properties. The modeling framework is used to quantitatively evaluate the contributions of afferent convergence and spatiotemporal filtering in the ELL maps to the enhancement of the signal and the suppression of the background.
| 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 |
Showing the most recent 10 out of 12 publications
