2013 Cognitive Neuroscience Society Annual Meeting Highlights

William Newsome Keynote Address

William Newsome does ground-breaking single-unit recordings of awake, behaving primates, the grist for my computational modeling mill. He shared new recoding data and his modeling of the data. The subject matter is engaging, gating of sensory information for decision-making, but the modeling is a game changer. It is the work of a post David Sussillo. The modeling is a premier example of dynamical neuroscience. First, he identified the relevant components to the signal. Second, he tracked how the relevant contribution of each of the components change over time. That might appear to be the next logical step but it is an additional dimension to the data, creating a wealth of information to explore. It was disappointing there was no question and answer period after the talk. A well-moderated Q&A would have added to both the science and the entertainment of already exceptional talk.

Analyzing Patterns Of Brain Activity To Understand Human Vision And Cognition Mini-Symposium

Frank Tong and John Serences were the warm-up for the main show of Jack Gallant. He researches the encoding and decoding of movies during fMRI. His work is much more than a “mind reading” pallor trick. He is able to identify the distributed semantic meaning network in the brain for percepts. In addition to what he does, I am also inspired by how he does it. He leverages cutting-edge web technology (i.e., HTML5) to facilitate the open exploration of his data. You can confirm (or disconfirm) his conclusions and explore your hypotheses All of this and more can be found on his lab website.

Posters

There were many interesting posters, here are my favorite two posters:

Lately I have been learning Python to tackle “big data” challenges. It was nice to see an application of this idea in Building Connectomes From The Cocomac Database Using Cocotool. The researchers data mined a macaque anatomical database with CoCoTools for Python. I have been playing with the code, it is simple, clean, and fast. I look forward to applying similar methods to human anatomical data.

Sebastein Hélie, a postdoctoral fellow while I was a graduate student in the Laboratory for Computational Cognitive Neuroscience, presented A Computational Cognitive Neuroscience Model Of Criterion Learning In Rule-guided Behavior. It included a new model for hebbian learning at inhibitory/GABA synapses which captures the properties of criterion selection during rule learning. The next challenge will be to translate the model to dopamine dependent learning.

General

This conference (and San Francisco) were a welcome working vacation. I have been doing mostly the “head’s down” work of research and teaching. It was nice to switch to the “head’s up” work of watching lectures, interacting with poster presenters, and having spirited conversations in the halls. I am reenergized to do the challenging work of cutting-edge science.

It was also a pleasure to catch up with old friends and colleagues and start new friendships and collaborations.