Abstract:SummaryBrains are optimized for processing ethologically relevant sensory signals. However, few studies have characterized the neural coding mechanisms that underlie the transformation from natural sensory information to behavior. Here, we focus on acoustic communication in Drosophila melanogaster, and use computational modeling to link natural courtship song, neuronal codes, and female behavioral responses to song. We show that melanogaster females are sensitive to long timescale song structure (on the order … Show more
“…1f, Supplementary 1e), suggesting the recorded CAP 7 is dominated by activity from type AB JONs and that adaptation is a property of this subpopulation. This 8 result is consistent with previous reports that show that the activity of type AB JONs, but not of type CE 9 JONs, is visible in the CAP for the frequency and intensity range of stimuli used in our study 11,21,24 . 10 Second, extracellular signals, such as cortical LFPs, can be sensitive to the synchrony among neurons in 11 the population 27 .…”
supporting
confidence: 93%
“…2a, top). On a 8 linear intensity scale, the slope of the tuning curves decreases with background intensity, indicating that 9 variance adaptation is indeed divisive. A logarithmic intensity scale, which transforms this division to a 10 rightward shift of the tuning curves, reveals a) that tuning curve shape is invariant to intensity (Fig.…”
mentioning
confidence: 99%
“…1a, bottom) and can saturate the responses to sound since mechanotransduction 8 works only over a limited displacement range 18 . 9 Thus, the Drosophila auditory system requires a neuronal mechanism for adaptation to both fluctuations 10 in the baseline of antennal displacements and the intensity of the acoustic signal. While a subtractive 11 mean adaptation mechanism corrects for the displacement baseline 18,19 , no studies have yet examined 12 variance adaptation in Drosophila.…”
To faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus 2 properties that corrupt pattern recognition. Here, we investigate sound intensity adaptation in the 3 Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory 4 neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to 5 wind or gravity, which affect the antenna's mean position. Song pattern recognition therefore requires 6 adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We 7 discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the 8 behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how 9 they interact. A computational model explains our results using a sequence of subtractive and divisive 10 adaptation modules, interleaved by rectification. These results lay the foundation for identifying the 11 molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.
“…1f, Supplementary 1e), suggesting the recorded CAP 7 is dominated by activity from type AB JONs and that adaptation is a property of this subpopulation. This 8 result is consistent with previous reports that show that the activity of type AB JONs, but not of type CE 9 JONs, is visible in the CAP for the frequency and intensity range of stimuli used in our study 11,21,24 . 10 Second, extracellular signals, such as cortical LFPs, can be sensitive to the synchrony among neurons in 11 the population 27 .…”
supporting
confidence: 93%
“…2a, top). On a 8 linear intensity scale, the slope of the tuning curves decreases with background intensity, indicating that 9 variance adaptation is indeed divisive. A logarithmic intensity scale, which transforms this division to a 10 rightward shift of the tuning curves, reveals a) that tuning curve shape is invariant to intensity (Fig.…”
mentioning
confidence: 99%
“…1a, bottom) and can saturate the responses to sound since mechanotransduction 8 works only over a limited displacement range 18 . 9 Thus, the Drosophila auditory system requires a neuronal mechanism for adaptation to both fluctuations 10 in the baseline of antennal displacements and the intensity of the acoustic signal. While a subtractive 11 mean adaptation mechanism corrects for the displacement baseline 18,19 , no studies have yet examined 12 variance adaptation in Drosophila.…”
To faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus 2 properties that corrupt pattern recognition. Here, we investigate sound intensity adaptation in the 3 Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory 4 neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to 5 wind or gravity, which affect the antenna's mean position. Song pattern recognition therefore requires 6 adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We 7 discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the 8 behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how 9 they interact. A computational model explains our results using a sequence of subtractive and divisive 10 adaptation modules, interleaved by rectification. These results lay the foundation for identifying the 11 molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.
“…Indeed, B1 cells are the only cells in the Drosophila brain which are known to be bandpass-tuned to antennal vibrations. All other cell types seems to be lowpass-tuned (Clemens et al, 2015; Tootoonian et al, 2012). …”
Section: Discussionmentioning
confidence: 99%
“…Recently, new genetic tools have made it possible to target identified mechanosensory neurons of the Drosophila central nervous system for in vivo intracellular recording (Chang et al, 2016; Clemens et al, 2015; Lehnert et al, 2013; Tootoonian et al, 2012; Tuthill and Wilson, 2016). This approach provides the opportunity to connect neural computations in mechanosensory systems with the cellular mechanisms that implement those computations.…”
Summary
To better understand biophysical mechanisms of mechanosensory processing, we investigated two cell types in the Drosophila brain (A2 and B1 cells) that are postsynaptic to antennal vibration receptors. A2 cells receive excitatory synaptic currents in response to both directions of movement – thus twice per vibration cycle. The membrane acts as a lowpass-filter, so that voltage and spiking mainly track the vibration envelope rather than individual cycles. By contrast, B1 cells are excited by only forward or backward movement, meaning they are sensitive to vibration phase. They receive oscillatory synaptic currents at the stimulus frequency, and they bandpass-filter these inputs to favor specific frequencies. Different cells prefer different frequencies, due to differences in their voltage-gated conductances. Both Na+ and K+ conductances suppress low-frequency synaptic inputs, so cells with larger voltage-gated conductances prefer higher frequencies. These results illustrate how membrane properties and voltage-gated conductances can extract distinct stimulus features into parallel channels.
Thoracic ganglia of many hearing insects house the first level of auditory processing. In bush-crickets, the largest population of local auditory neurons in the prothoracic processing centre are dorsal unpaired median (DUM) neurons. It has been suggested that DUM neurons are inhibitory using γ-aminobutyric acid (GABA) as transmitter. Immunohistochemistry reveals a population of about 35-50 GABA-positive somata in the posterior medial cluster of the prothoracic ganglion. Only very few small somata in this cluster remain unstained. At least 10 neurites from 10 neurons can be identified. Intracellularly stained auditory DUM neurons have their soma in the cluster of median GABA positive cells and most of them exhibit GABAimmunoreactivity. Responses of certain DUM neurons show obvious signs of inhibition. Application of picrotoxin (PTX), a chloride-channel blocker in insects, changes the responses of many DUM neurons. They become broader in frequency tuning and broader or narrower in temporal pattern tuning. Furthermore, inhibitory postsynaptic potentials (IPSPs) may be replaced by excitatory postsynaptic potentials. Loss of an IPSP in the rising graded potential after PTX-application leads to a significant reduction of first-spike latency. Therefore, auditory DUM neurons receive effective inhibition and are the best candidates for inhibition in DUM neurons and other auditory interneurons.
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