Abstract
In the field of songbird neuroscience, researchers have used playback of aversive noise bursts to drive changes in song behavior for specific syllables within a bird's song. Typically, a short (~5-10 msec) slice of the syllable is selected for targeting and the average spectrum of the slice is used as a template. Sounds that are sufficiently close to the template are considered a match. If other syllables have portions that are spectrally similar to the target, false positive errors will weaken the operant contingency. We present a gradient descent method for template optimization that increases the separation in distance between target and distractors slices, greatly improving targeting accuracy. Applied to songs from five adult Bengalese finches, the fractional reduction in errors for sub-syllabic slices was 51.54±22.92%. At the level of song syllables, we use an error metric that controls for the vastly greater number of distractors vs. target syllables. Setting 5% average error (misses + false positives) as a minimal performance criterion, the number of targetable syllables increased from 3 to 16 out of 61 syllables. At 10% error, targetable syllables increased from 11 to 26. By using simple and robust linear discriminant methods, the algorithm reaches near asymptotic performance when using 10 songs as training data, and the error increases by <2.3% on average when using only a single song for training. Targeting is temporally precise, with average jitter of 3.33 msec for the 16 accurately targeted syllables. Because the algorithm is concerned only with the problem of template selection, it can be used as a simple and robust front end for existing hardware and software implementations for triggered feedback.
Highlights
For over a century, operant conditioning has had great success in making complex learning questions experimentally tractable
We evaluated the performance of the averaged and optimized templates by determining whether the distance to the template for target and distractor slices fell below the slice-optimal threshold determined from the smoothed distribution of distances for each template
We have presented a method that improves detection performance for template-based operant conditioning experiments, using syllable detection in songbirds as an example
Summary
Operant conditioning has had great success in making complex learning questions experimentally tractable. It has been the main experimental tool for understanding the neural underpinnings of learning processes [1, 2]. Applying operant conditioning to discrete behaviors such as lever presses and nose pokes is relatively straightforward. To successfully shape a complex behavior, a conditioning stimulus must be delivered with high specificity and with a short temporal delay [3]. Short-delay operant feedback techniques have been applied fruitfully in the field of birdsong neuroscience. An individual bird’s song comprises a sequence of distinct
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