Abstract
The way in which neurons encode information remains a hotly debated topic in neuroscience. Lin and colleagues in a 2014 article in the journal Nature Neuroscience demonstrate how sparse coding in the olfactory learning and memory center of Drosophila can influence learning behavior. Coding sparsity is the idea that only a small number of neurons in a network represent any given stimulus. Using neurogenetics, computational neuroscience, and cognitive approaches, they outline the discovery of an inhibitory feedback circuit responsible for differentiating the neuronal response to different odors. Manipulating this feedback circuit, they demonstrate how the sparseness in neural networks (how easily neurons are activated) can correspond to the ability to learn a sensory discrimination more easily. From a research perspective, this paper was important as it was the first causal demonstration of the role of sparseness in learning. From a teaching point of view, this paper is valuable because it is a simple but effective introduction to artificial neural network theory, where both the abstract theory and the importance of its application is apparent to those without a mathematical or computational background.
