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Related Experiment Video

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Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Bidirectional generative adversarial representation learning for natural stimulus synthesis.

Johnny Reilly¹, John D Goodwin¹, Sihao Lu¹

¹Department of Bioengineering, Imperial College London, London, United Kingdom.

Journal of Neurophysiology

|August 28, 2024

Summary

This summary is machine-generated.

A new AI model, BiWaveGAN, generates realistic animal vocalizations for research. These synthetic sounds effectively stimulate auditory neurons, matching the impact of natural vocalizations.

Keywords:

auditory cortex generative adversarial learning natural stimuli receptive fields ultrasonic vocalizations

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Published on: July 13, 2019

Area of Science:

Neuroscience
Bioacoustics
Artificial Intelligence

Background:

Animal vocalizations are complex signals crucial for communication.
Analyzing and synthesizing these signals is challenging due to their high dimensionality and potential for human bias.
Understanding how auditory neurons represent ethologically relevant stimuli is key in sensory neuroscience.

Purpose of the Study:

To develop a method for generating naturalistic animal vocalization waveforms for use as stimuli.
To create a bidirectional generative adversarial network (GAN) capable of learning latent representations of vocalizations.
To synthesize novel vocalizations and interpolate between existing ones for research purposes.

Main Methods:

Developed BiWaveGAN, a bidirectional generative adversarial network (GAN).
Trained BiWaveGAN on mouse ultrasonic vocalizations (USVs) to learn a latent representation.
Generated realistic USV waveforms and interpolated between them using the trained model.

Main Results:

BiWaveGAN successfully generated realistic mouse USV waveforms.
Synthesized vocalizations effectively stimulated mouse auditory cortical neurons, comparable to natural USVs.
Generated stimuli produced receptive fields with equivalent predictive power to natural vocalizations.

Conclusions:

BiWaveGAN provides a powerful tool for generating high-quality, naturalistic animal vocalizations.
The synthetic stimuli are as effective as natural vocalizations in probing neuronal responses.
This method can be applied to various species for studying auditory processing and categorical perception.