2014
DOI: 10.1523/jneurosci.0260-14.2014
|View full text |Cite
|
Sign up to set email alerts
|

Online Stimulus Optimization Rapidly Reveals Multidimensional Selectivity in Auditory Cortical Neurons

Abstract: Neurons in sensory brain regions shape our perception of the surrounding environment through two parallel operations: decomposition and integration. For example, auditory neurons decompose sounds by separately encoding their frequency, temporal modulation, intensity, and spatial location. Neurons also integrate across these various features to support a unified perceptual gestalt of an auditory object. At higher levels of a sensory pathway, neurons may select for a restricted region of feature space defined by… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
27
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
3
2
2

Relationship

0
7

Authors

Journals

citations
Cited by 32 publications
(28 citation statements)
references
References 58 publications
1
27
0
Order By: Relevance
“…The complexity of contemporary behavioral experiments, however, presents a stiff methodological challenge. For example, researchers might wish to measure pupil dilation [28,28], respiration [25], and running speed [24], while tracking the positions of body parts in 3 dimensions [23] and recording the activity of large ensembles of neurons [17], as subjects perform tasks with custom input devices such as a steering wheel [4] while immersed in virtual reality environments using stimuli synthesized in real time [34,7]. Coordinating the array of necessary hardware into a coherent experimental design-with the millisecond precision required to study the brain-can be daunting.…”
Section: Contentsmentioning
confidence: 99%
“…The complexity of contemporary behavioral experiments, however, presents a stiff methodological challenge. For example, researchers might wish to measure pupil dilation [28,28], respiration [25], and running speed [24], while tracking the positions of body parts in 3 dimensions [23] and recording the activity of large ensembles of neurons [17], as subjects perform tasks with custom input devices such as a steering wheel [4] while immersed in virtual reality environments using stimuli synthesized in real time [34,7]. Coordinating the array of necessary hardware into a coherent experimental design-with the millisecond precision required to study the brain-can be daunting.…”
Section: Contentsmentioning
confidence: 99%
“…As a next step, we generated a set of spectrotemporally modulated noise bursts, that varied in center frequency (4-64 kHz, 0.1 octave steps), spectral bandwidth (0-1.5 octaves, 0.1 160 octave steps), level (0-60 dB SPL, 10 dB SPL steps), and sinusoidal amplitude modulation rate 8 (0-70 Hz, 10 Hz steps) ( Figure 3E). We then used a standard measure of sparsity to quantify the 162 shape of each cell's response distribution ( Figure 3F) ( Chambers et al 2014). This lifetime sparseness index is bounded between 0 and 1, with 164 values close to 1 reflecting selectivity for a sparse set of stimuli ( Figure 3F: left) and values close to 0 reflecting a broad response distribution ( Figure 3F: right).…”
Section: Sensory Characterization Of Ct Projectionsmentioning
confidence: 99%
“…We used a common measure of sparseness (Chambers et al 2014;Vinje & Gallant 2000;478 Rolls & Tovee 1995), defined as…”
Section: Sparsenessmentioning
confidence: 99%
“…Using high intensity tones as the standard stimulus did not impede the response to lower intensity oddball tones of the same frequency, when the intensity of the two tones differed by only 16 dB [25]. In other words, AI circuits can treat sound intensity as a separate dimension of acoustic space, varying independently of other dimensions such as frequency [34,35,36••]. …”
Section: Neuronal Adaptationmentioning
confidence: 99%
“…However, multiunit recordings in anesthetized rat [50] and single-unit responses to optimized stimuli in awake mouse and primate [36••,51] show that AI neurons can follow stimulation trains at much higher rates (beyond 10 Hz). How is it that neuronal output could adapt at a slower rate than the input?…”
Section: Short-term Plasticitymentioning
confidence: 99%