Tetrode Recording in the Xenopus laevis Visual System Using Multichannel Glass Electrodes

Identification of movie encoding neurons enables movie recognition AI

Natural visual scenes are dominated by spatiotemporal image dynamics, but how the visual system integrates "movie" information over time is unclear. We characterized optic tectal neuronal receptive fields using sparse noise stimuli and reverse correlation analysis. Neurons recognized movies of ~200-600 ms durations with defined start and stop stimuli. Movie durations from start to stop responses were tuned by sensory experience though a hierarchical algorithm. Neurons encoded families of image sequences following trigonometric functions. Spike sequence and information flow suggest that repetitive circuit motifs underlie movie detection. Principles of frog topographic retinotectal plasticity and cortical simple cells are employed in machine learning networks for static image recognition, suggesting that discoveries of principles of movie encoding in the brain, such as how image sequences and duration are encoded, may benefit movie recognition technology. We built and trained a machine learning network that mimicked neural principles of visual system movie encoders. The network, named MovieNet, outperformed current machine learning image recognition networks in classifying natural movie scenes, while reducing data size and steps to complete the classification task. This study reveals how movie sequences and time are encoded in the brain and demonstrates that brain-based movie processing principles enable efficient machine learning.

Visual neurons recognize complex image transformations

Natural visual scenes are dominated by sequences of transforming images. Spatial visual information is thought to be processed by detection of elemental stimulus features which are recomposed into scenes. How image information is integrated over time is unclear. We explored visual information encoding in the optic tectum. Unbiased stimulus presentation shows that the majority of tectal neurons recognize image sequences. This is achieved by temporally dynamic response properties, which encode complex image transitions over several hundred milliseconds. Calcium imaging reveals that neurons that encode spatiotemporal image sequences fire in spike sequences that predict a logical diagram of spatiotemporal information processing. Furthermore, the temporal scale of visual information is tuned by experience. This study indicates how neurons recognize dynamic visual scenes that transform over time.

Neurophysiological and Behavioral Analysis in Xenopus

Because of its resilience to hypoxia and trauma, the frog has long been a favored preparation of neurophysiologists. Its use has led to the discovery of many fundamental properties of neurons and neural circuits. Neurophysiologists were originally attracted to Xenopus embryos, tadpoles, and frogs because of their ready availability, their external development, and the anatomical accessibility and relatively simple neural circuitry of the Xenopus visual, locomotory, and vocalization systems. Nowadays, the sequencing of Xenopus genomes and the panoply of tools for manipulating gene expression have created new opportunities for neurophysiologists to address the molecular underpinnings of how neurons generate behaviors in a vertebrate. Here, we introduce protocols for harnessing the power of Xenopus for performing electrophysiological studies of neural circuitry in the developing optic tectum and spinal cord, as well as in vocalization, and for studying the ontogeny of locomotory behavior.