Efficient computation via sparse coding in electrosensory neural networks

The electric sense combines spatial aspects of vision and touch with temporal features of audition. Its accessible neural architecture shares similarities with mammalian sensory systems and allows for recordings from successive brain areas to test hypotheses about neural coding. Further, electrosensory stimuli encountered during prey capture, navigation, and communication, can be readily synthesized in the laboratory. These features enable analyses of the neural circuitry that reveal general principles of encoding and decoding, such as segregation of information into separate streams and neural response sparsification. A systems level understanding arises via linkage between cellular differentiation and network architecture, revealed by in vitro and in vivo analyses, while computational modeling reveals how single cell dynamics and connectivity shape the sparsification process.

► The electric sense encodes a wide range of stimuli including navigational cues, communication signals and prey signals. ► Electroreceptors on the body respond linearly to this range, except at high frequencies where population synchrony occurs. ► The electrosensory hindbrain structure (ELL), with limited cell types, has been studied to reveal how a sparse code emerges. ► This code relies on linked cellular and architectural properties including segregated maps and cell types. ► Computational studies ground this code in neural dynamics, and support studies of deeper sparsification in the midbrain.