We are a computational and theoretical neuroscience research group at the University of Maryland College Park, focused on understanding how the neural systems perform the complex computations underlying natural intelligence. We focus on the sensory cortex: studying both the computations underlying hierarchical processing of sensory information, and how this is modulated by top-down influences related to behavioral context and active sensing. We combine cutting-edge approaches in statistical computation and machine learning combined with state-of-the-art experimental neurophysiology recorded in collaboration with local and international collaborators.
OPPORTUNITIES FOR GRADUATE AND POSTDOCS. We are looking for postdocs and graduate students to work on funded projects described here. Experience in neuroscience background in quantitative disciplines is required. Please contact Dan Butts if you are interested.
OPPORTUNITIES FOR UNDERGRADUATE INVOLVEMENT. There are several opportunities for undergraduates to be involved in research projects in the lab, although requires a sufficient background in computer programming and applications of mathematics coursework up through linear algebra. Please contact Dan Butts if you are interested, and please be sure to send relevant information about your background.
- [11.10.21] We had two presentations at the [virtual] Society for Neuroscience Conference 2021 about the mechanisms and purpose of binocular integration in the primary visual cortex (presentations here):
P479.06: Amplification of disparity selectivity by spatial convolutions in the primary visual cortex presented by graduate student Felix Bartsch and in collaboration with Bruce Cumming (NIH) and Jenny Read (Newcastle University).
P479.05: Binocular integration as nonlinear mixing: how binocular neurons in primary visual cortex preserve eye-specific information for downstream visual processing presented by undergraduate student Ethan Chang.
- [11.08.21] Jake Yates posted a new preprint on bioRxiv: Beyond Fixation: detailed characterization of neural selectivity in free-viewing primates describing new Methods for studying neural processing of vision in free-viewing contexts, in collaboration with the Mitchell and Rucci labs at University of Rochester.
- [10.28.21] Craig Taswell from the Averback lab (NIH, co-advised by Dan Butts) successfully defended his Ph.D. dissertation: The role of ventral striatum and amygdala in reinforcement learning
- [10.07.21] Jake Yates accepted a faculty position at UC Berkeley School of Optometry starting in July 2022 (he will be recruiting -- stay tuned).
- [10.01.21] The National Science Foundation funded our NCS Foundations research proposal with the Briggs and Haefner Labs (University of Rochester): Active vision during natural behavior: More than meets the eye?
- [09.15.21] Felix Bartsch posted our new preprint on bioRxiv: Model-based characterization of the selectivity of neurons in primary visual cortex
- [09.01.21] The National Science Foundation funded our CRCNS research proposal with the Conway Lab (NIH): Computations for spatial-chromatic interactions and their physiological implementation in primary visual cortex
- [07.22.21] A joint study with the Nienborg lab (NIH) was published in Nature Communications: Decision-related feedback in visual cortex lacks spatial selectivity.
- [01.01.21] The NIH funded Jake Yates's K99/R00 career proposal: Neural mechanisms of active vision in the fovea.
- [01.09.20] A new lab preprint with Bruno Averbeck: A latent variable approach to decoding neural population activity is now up on bioRxiv.org.
- [09.17.19] Our statistical modeling review is out in Annual Review of Vision Science: Data-driven approaches to understanding visual neuron activity.
- [08.26.19] Our review on latent variable models and population activity The quest for interpretable models of neural population activity was published in Current Opinion in Neurobiology. [PDF]
- [06.18.19] Paper published with the Singer lab (UMD): Functional characterization of retinal ganglion cells using tailored nonlinear modeling has been published in Scientific Reports. [PDF]
- [04.27.19] Lab paper: Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models is now published in NBDT. [PDF]