Understanding the molecular mechanisms that drive cell-type-specific gene expression in the brain is crucial for advancing circuit neuroscience. The wealth of genome-wide information on gene regulation available today puts us in the position to decipherContinue reading
Tag: deep learning
Paper: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
We’re excited to see this published. A new take on spatial credit assignment in cortical circuits based on Richard Naud’s idea on burst multiplexing. A truly collaborative effort lead by Alexandre Payeur and Jordan GuerguievContinue reading
Perspective: A deep learning framework for neuroscience
Our perspective paper on how systems neuroscience can benefit from deep learning was published today. In work led by Blake Richards, Tim Lillicrap, and Konrad Kording, we argue that focusing on the three core elements usedContinue reading
Talk at “Neuroscience meets Deep Learning” Symposium at EPF Lausanne
I am stoked for the EPFL Neurosymposium “Neuroscience meets Deep Learning” next week. July 8th to July 9th 2019 in Lausanne, Switzerland https://neurosymposiumsummer2019.epfl.ch/ I will talk about: “Credit assignment in space and time — TrainingContinue reading