Dissecting the neural processes supporting perceptual learning
The brain and its inherent functions can be modified by various forms of learning. Learning-induced changes are seen even in basic perceptual functions. In particular, repeated training in a perceptual task can lead to a significant improvement in the trained task—a phenomenon known as perceptual learning. There has been a long-standing debate about the mechanisms of perceptual learning. In this talk, I will present results from our series of electrophysiological studies. These studies have consistently shown that perceptual learning is mediated by concerted changes in both perceptual and cognitive processes, resulting in improved sensory representation, enhanced top-down influences, and refined readout process.
NMC4 Keynote: An all-natural deep recurrent neural network architecture for flexible navigation
A wide variety of animals and some artificial agents can adapt their behavior to changing cues, contexts, and goals. But what neural network architectures support such behavioral flexibility? Agents with loosely structured network architectures and random connections can be trained over millions of trials to display flexibility in specific tasks, but many animals must adapt and learn with much less experience just to survive. Further, it has been challenging to understand how the structure of trained deep neural networks relates to their functional properties, an important objective for neuroscience. In my talk, I will use a combination of behavioral, physiological and connectomic evidence from the fly to make the case that the built-in modularity and structure of its networks incorporate key aspects of the animal’s ecological niche, enabling rapid flexibility by constraining learning to operate on a restricted parameter set. It is not unlikely that this is also a feature of many biological neural networks across other animals, large and small, and with and without vertebrae.
Understanding the role of neural heterogeneity in learning
The brain has a hugely diverse and heterogeneous nature. The exact role of heterogeneity has been relatively little explored as most neural models tend to be largely homogeneous. We trained spiking neural networks with varying degrees of heterogeneity on complex real-world tasks and found that heterogeneity resulted in more stable and robust training and improved training performance, especially for tasks with a higher temporal structure. Moreover, the optimal distribution of parameters found by training was found to be similar to experimental observations. These findings suggest that heterogeneity is not simply a result of noisy biological processes, but it may play a crucial role for learning in complex, changing environments.