Blake Richards explores the neurobiology of learning and memory, with the ultimate goal of understanding how experience alters the synaptic connections in the brain.
Research in the Richards lab seeks a unification between experiment and theory by testing hypotheses from computational neuroscience and machine learning. One major project in the lab is to identify potential neurophysiological mechanisms for mediating deep learning in the neocortex. This work uses computational models, electrophysiology and in vivo 2-photon imaging to explore how signals between brain regions can co-ordinate learning across multiple layers of information processing.
Another major project is to explore the interaction between episodic memories, schemata and reinforcement learning. Richards has previously shown that, in line with computational models of memory consolidation, animals switch over time from using episodic memories to more general or schematic memories, when engaged in reinforcement learning. His lab is further exploring the computational utility of this switch, as well as the neurobiological mechanisms in the prefrontal cortex and hippocampus that underpin it.
Google Research Faculty Award, 2016
Human Frontiers Young Investigator Grant, 2015
NSERC Discovery Grant, 2014
Banting Postdoctoral Fellowship, 2011
Santoro, A., P.W. Frankland, and B.A. Richards. "Memory Transformation Enhances Reinforcement Learning in Dynamic Environments." Journal of Neuroscience 36, no. 48 (2016): 12228–12242.
van Rheede, J.J., B.A. Richards, and C.J. Akerman. "Sensory-evoked spiking behavior emerges via an experience-dependent plasticity mechanism." Neuron 87, no. 5 (2015): 1050–1062.
Richards, B.A. et al. "Patterns across multiple memories are identified over time." Nature neuroscience 17, no. 7 (2014): 981–86.
Muldal, A.M. et al. "Clonal relationships impact neuronal tuning within a phylogenetically ancient vertebrate brain structure." Current Biology 24, no. 16 (2014): 1929–1933.
Yiu, A.P. et al. "Neurons are recruited to a memory trace based on relative neuronal excitability immediately before training." Neuron 83, no. 3 (2014): 722–35.