Events

Bodian Seminars are scheduled for every Monday at 4 p.m. If there is not a specific date listed below, then that date is open. Please contact us to schedule a seminar for an open date.

Oct
22
Mon
Bodian Seminar: Peter Tse Ph.D.
Oct 22 @ 4:00 pm – 5:00 pm

Bodian Seminar:

Peter Tse, Ph.D.

Professor

Psychological & Brain Sciences

Dartmouth College

“The neural bases of human imagination and creativity”

The artifact record suggests that the minds of earlier species in our genus were singularly unimaginative. For example, the Acheulian handaxes of Homo Erectus hardly changed over more than a million years. Even the more sophisticated Mousterian technology of the Neanderthals was stable over many tens of thousands of years. With the advent of our species, however, innovation in art and technology became explosive. What changed in our brains to permit our species to become so innovative and creative? I will begin with some ideas in this regard, before getting into the details of fMRI data from three experiments on mental operations over imagined objects. In particular, I will argue that changes in the circuitry commonly thought to underlie volitional manipulation of operands in working memory was central to the development of our species’ creativity.

Nov
12
Mon
Bodian Seminar: Vikram Chib PhD.
Nov 12 @ 4:00 pm – 5:00 pm

Bodian Seminar:

Vikram Chib PhD.

Assistant Professor

Department of Biomedical Engineering

Johns Hopkins University

“Subjective Valuation of Effort”

Our decisions are shaped not only by the rewards at stake, but also the effort required to obtain them. Moreover, the subjectivity of effort plays an integral role in driving choice – if a task feels very effortful one may not be willing to perform the work required to obtain reward, whereas if a task feels less effortful one may be more likely to persevere. Despite the ubiquity of such effort-based judgments in our daily lives, subjective valuation of effort has received limited investigation. In this talk I will present functional brain imaging data that was obtained while human subjects made decisions under uncertainty about prospective effort. Participants exhibited subjectivity in their decision-making, displaying increased sensitivity to changes in subjective effort as objective effort levels increased. Analysis of blood-oxygenation level dependent (BOLD) activity revealed that signals in the prefrontal cortex encoded the subjective valuation of prospective effort, and that fatigue induced changes in an individual’s physical state served to alter these representations. Furthermore, we found that functional connectivity between premotor cortex and prefrontal cortex was decreased in individuals that found effort to be especially costly, indicative of a mechanism by which motor cortical state served to modulate brain regions involved in effort-based choice. Taken together, these results provide new insights into the behavioral and neural mechanisms responsible for decision-making regarding effort.

Nov
16
Fri
The 2018 Kenneth O. Johnson & Steven S. Hsiao Memorial Lecture
Nov 16 @ 4:00 pm – 5:00 pm

The 2018 Kenneth O. Johnson & Steven S. Hsiao Memorial Lecture:

JACK GALLANT, Ph.D.

Chancellor’s Professor of Psychology and Class of 1940 Chair Department of Psychology Affiliate, Electrical Engineering and Computer Science Programs in Neuroscience, Bioengineering, Vision Science & Biophysics University of California at Berkeley

A deep convolutional energy model of ventral stream areas V1, V2 and V4

The ventral stream areas V1, V2 and V4 are crucial for visual object recognition. Good computational models of V1 neurons already exist, but current models of V2 and V4 neurons are poor. To build better models we recorded from neurons while awake animals viewed clips of large, full color natural movies. Because neurons could be recorded for several days, we collected responses to hundreds of thousands (up to over 1 million) distinct movie frames, for hundreds of different V1, V2 and V4 neurons. We fit these data using a new deep convolutional energy model. A two-stage version of the model is used to model V1 and V2, and a three-stage version is used for V4.  Deep convolutional energy models fit to V1 and V2 neurons approach the noise-ceiling of prediction performance. Predictions of V4 neuron responses are somewhat lower, but they are as good as the classical model fit to V1 neurons. Furthermore, the model predicts V4 responses to various types of synthetic curvature stimuli in previous studies of V4. Finally, these models can be used to visualize and help interpret the response properties of each neuron. The deep convolutional energy model thus presents a unified framework for modeling and understanding neurons in the early and intermediate ventral stream.

Dec
10
Mon
Bodian Seminar: Isabel Muzzio Ph.D.
Dec 10 @ 4:00 pm – 5:00 pm

Bodian Seminar: Isabel Muzzio Ph.D.

University of Texas at San Antonio

TBA

Bodian Seminar: Isabel Muzzio, Ph.D.
Dec 10 @ 4:00 pm – 5:00 pm

Bodian Seminar:

Isabel Muzzio, Ph.D.

Associate Professor

Biotechnology, Sciences, and Engineering

University of Texas

San Antonio

TBA

Feb
4
Mon
Bodian Seminar: Nicholas J. Priebe
Feb 4 @ 4:00 pm – 5:00 pm

Bodian Seminar: Nicholas J. Priebe

University of Texas Austin

TBA

Bodian Seminar: Nicholas J. Priebe, Ph.D.
Feb 4 @ 4:00 pm – 5:00 pm

Bodian Seminar:

Nicholas J. Priebe, Ph.D.

Associate Professor

Department of Neuroscience

Center for Learning and Memory

College of Natural Sciences

University of Texas Austin

TBA

Feb
25
Mon
Bodian Seminar: Sridevi V. Sarma, Ph.D.
Feb 25 @ 4:00 pm – 5:00 pm

Bodian Seminar

Sridevi V. Sarma, Ph.D.

Associate Professor

Institute of Computational Medicine

Johns Hopkins University

TBA

Apr
17
Wed
Bodian Seminar: Uta Noppeney, Ph.D.
Apr 17 @ 4:00 pm – 5:00 pm

Bodian Seminar:

Uta Noppeney, Ph.D.

Professor

Computational Neuroscience

Cognitive Robotics Centre

University of Birmingham

United Kingdom

TBA

May
20
Mon
Bodian Seminar: Soohyun Lee, Ph.D.
May 20 @ 4:00 pm – 5:00 pm

Bodian Seminar:

Soohyun Lee, Ph.D.

Investigator

Unit on Functional Neural Circuits

NIMH

TBA