From stimulus estimation to combination sensitivity: encoding and processing of amplitude and timing information in parallel, convergent sensory pathways

Abstract

Information theoretical approaches to sensory processing in electric fish have focused on the encoding of amplitude modulations in a single sensory pathway in the South American gymnotiforms. To assess the generality of these studies, we investigated the encoding of amplitude and phase modulations in the distantly related African fish Gymnarchus. In both the amplitude-and time-coding pathways, primary afferents accurately estimated the time course of random modulations whereas hindbrain neurons extracted information about specific stimulus features. Despite exhibiting a clear preference for encoding amplitude or phase, afferents and hindbrain neurons could encode significant amounts of modulation of their nonpreferred attribute. Although no increase in feature extraction performance occurred where the two pathways converge in the midbrain, neurons there were increasingly sensitive to simultaneous modulation of both attributes. A shift from accurate stimulus estimation in the periphery to increasingly sparse representations of specific features appears to be a general strategy in electrosensory processing.

Electrosensory processing inApteronotus albifrons: implications for general and specific neural coding strategies across wave-type weakly electric fish species

“…While ELL neurons in G. niloticus respond to sinusoidal and noisy AM stimuli (Carlson and Kawasaki 2008;Kawasaki and Guo 1996), their tuning has not been systematically characterized to date using the measures presented in the current study. Nevertheless, an indirect comparison of other measures (e.g., the STAs) shows striking similarities between ELL neurons responding to AMs in G. niloticus and E. virescens (Carlson and Kawasaki 2008;Metzner et al 1998), thereby suggesting that neurons in both species have similar tuning curves.…”

Decoding stimulus duration from neural responses in the auditory midbrain

“…Traditionally, BD has been defined as the stimulus duration evoking the maximum spike count (Casseday et al 1994). In this study, BD was defined as the average of the shortest and longest stimulus durations with evoked spike counts Ն90% of the peak spike count (Fremouw et al 2005).…”

“…Studies characterizing DTNs measure the BD stimulus and the shape (short-pass, band-pass, or long-pass) and bandwidth of the duration tuning curve at an arbitrary fraction of the peak response (e.g., the 50 or 75% temporal response bandwidth; Casseday et al 1994;Faure et al 2003;Fremouw et al 2005;Jen and Wu 2006). In this study we used the midpoint of the 90% temporal bandwidth to define the BD of DTNs (see Single Neuron Analysis).…”

“…We measured spike counts and latencies from auditory DTNs in the mammalian IC to characterize the information content of duration-selective neural responses. Previous studies, including those by the authors, have characterized the spiking profile of DTNs by the shape of the duration tuning curve (i.e., short-pass, band-pass, or long-pass) and defined the neuronal best duration (BD) to be the stimulus duration that evoked the peak spike count (Casseday et al 1994(Casseday et al , 2000Ehrlich et al 1997;Faure et al 2003;Aubie et al 2009Aubie et al , 2012Sayegh et al 2011). To test this hypothesis, we measured information content of DTN spiking responses by calculating the stimulus-specific information (SSI) and estimated observed Fisher information (FI).…”

“…To date, the electrophysiological properties and underlying synaptic inputs responsible for creating the temporally selective responses of DTNs have been a major research focus, but a quantitative analysis of the information content and encoding efficiency of any neural system is important for linking neurophysiology to behavior and perception because it can suggest limits on the efficacy of information representation. Information theoretic measurements have previously been used to characterize the spiking responses of visual (Strong et al 1998;Tolhurst et al 2009), auditory (Hsu et al 2004;Montgomery and Wehr 2010;Kayser et al 2010), somatosensory (Arabzadeh 2004;Saal et al 2009;Panzeri and Diamond 2010), olfactory (Rolls et al 1996(Rolls et al , 2009, and electrosensory neurons (Carlson and Kawasaki 2008;Maler 2009;Vonderschen and Chacron 2014). In this study, we used an information theory-based approach to analyze the information available in the responses of auditory DTNs from the mammalian IC.…”