Finding Needles in Genomic Haystacks
The ability to read the DNA sequences of different organisms has transformed biology in much the same way that the telescope transformed astronomy. And yet, much of the sequence found in these genomes is as enigmatic as the Rosetta Stone was to early Egyptologists. With the aim of making steps to crack the genomic Rosetta Stone, I will describe unexpected ways of using the physics of information transfer first developed at Bell Labs for thinking about telephone communications to try to decipher the meaning of the regulatory features of genomes. Specifically, I will show how we have been able to explore genes for which we know nothing about how they are regulated by using a combination of mutagenesis, deep sequencing and the physics of information, with the result that we now have falsifiable hypotheses about how those genes work. With those results in hand, I will show how simple tools from statistical physics can be used to predict the level of expression of different genes, followed by a description of precision measurements used to test those predictions. Bringing the two threads of the talk together, I will think about next steps in reading and writing genomes at will.
Deep reinforcement learning and its neuroscientific implications
The last few years have seen some dramatic developments in artificial intelligence research. What implications might these have for neuroscience? Investigations of this question have, to date, focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. However, there is another area of recent AI work which has so far received less attention from neuroscientists, but which may have more profound neuroscientific implications: Deep reinforcement learning. Deep RL offers a rich framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. I’ll provide a high level introduction to deep RL, discuss some recent neuroscience-oriented investigations from my group at DeepMind, and survey some wider implications for research on brain and behavior.