Associate Professor, Department of Biology
Ph.D. Stanford University
B.S. Stanford Univeristy
Research Interests: Function and development of neural circuits for visual processing
Overview: How do we make sense of the visual world around us? Our brain takes a pattern of photons hitting the retina and continually creates a coherent representation of what we see – detecting objects and landmarks rather than just perceiving an array of pixels. This image processing allows us to perform a range of visual tasks, such as recognizing a friend’s face, finding your way to the grocery store, and catching a frisbee. However, how these computational feats are achieved by the neural circuitry of the visual system is largely unknown. Furthermore, this circuitry is wired up by a range of cellular processes, such as arbor growth, synapse formation, and activity-dependent plasticity, and thus these developmental mechanisms effectively determine how we see the world.
Our research is focused on understanding how neural circuits perform the image processing that allows us to perform complex visual behaviors, and how these circuits are assembled during development. We use in vivo recording techniques, including high-density extracellular recording and two-photon imaging, along with molecular genetic tools to dissect neural circuits, such as cell-type specific markers, optogenetic activation and inactivation, tracing of neural pathways, and in vivo imaging of dendritic and synaptic structure. We have also implemented behavioral tasks for mice so we can perform quantitative pyschophysics to measure the animal’s perception, and we use theoretical models to understand general computational principles being instantiated by a neural circuit.
Differential Involvement of Three Brain Regions During Mouse Skill Learning.
eNeuro. 2019 Aug 01;:
Authors: Weible AP, Posner MI, Niell CM
Human skill learning is marked by a gradual decrease in reaction time and errors as the skill is acquired. To better understand the influence of brain areas thought to be involved in skill learning, we trained mice to associate visual-spatial cues with specific motor behaviors for a water reward. Task acquisition occurred over weeks and performance approximated a power function as often found with human skill learning. Using optogenetics we suppressed the visual cortex, anterior cingulate cortex, or dorsal hippocampus on 20% of trials at different stages of learning. Intermittent suppression of the visual cortex greatly reduced task performance on suppressed trials across multiple stages but did not change the overall rate of learning. In accord with some recent models of skill learning, anterior cingulate cortex suppression produced higher error rates on suppressed trials throughout learning the skill, with effects intensifying in the later stages. This would suggest that cognitive influences mediated by the anterior cingulate continue throughout learning. Suppression of the hippocampus only modestly affected performance, with largely similar effects seen across stages. These results indicate different degrees of visual cortex, anterior cingulate cortex, and dorsal hippocampus involvement in acquisition and performance of this visual-spatial task, and that the structures operate in parallel, and not in series, across learning stages.SIGNIFICANCE STATEMENT Mice resemble humans with improvements in accuracy and speed during skill learning. Through optogenetics, we can suppress different regions of the mouse brain at different stages of training to better understand when each region contributes to learning. Here we found that visual cortex suppression reduced accuracy across all training stages. Suppressing anterior cingulate cortex, a region thought to be important for attention early in training, also reduced accuracy throughout learning. Suppressing the hippocampus, a structure critically involved in associative learning, affected performance more modestly. These findings reveal parallel, rather than serial, involvement of these three structures in a mouse model of skill learning.
PMID: 31371454 [PubMed - as supplied by publisher]