The talk on Precisely timed theta oscillations for memory encoding was given by Dr. Ipshita Zutshi, New York University (NYU). It was held on 25th January, 10 AM IST

Abstract: Brain oscillations have been hypothesized to support cognitive function by coordinating spike timing within and across brain regions, yet it is often not known when timing is either critical for neural computations or an epiphenomenon. The entorhinal cortex and hippocampus are necessary for learning and memory and exhibit prominent theta oscillations (6–9 Hz), which are controlled by pacemaker cells in the medial septal area. Here we show that entorhinal and hippocampal neuronal activity patterns were strongly entrained by rhythmic optical stimulation of parvalbumin-positive medial septal area neurons in mice. Despite strong entrainment, memory impairments in a spatial working memory task were not observed with pacing frequencies at or below the endogenous theta frequency and only emerged at frequencies ≥10 Hz, and specifically when pacing was targeted to maze segments where encoding occurs. Neural computations during the encoding phase were therefore selectively disrupted by perturbations of the timing of neuronal firing patterns.

About the speaker: Dr. Ipshita Zutshi received her BE and MSc degrees in biology and computer science from Birla Institute of Technology and Science (BITS), Pilani, India, and completed her master’s thesis with Dr. Carmen Sandi at EPFL, Switzerland, working on the epigenetic transmission of behavioral phenotypes. She received her Ph.D. from the University  of California, San Diego (UCSD), where she worked in the laboratory of Dr. Stefan Leutgeb. Her thesis focused on understanding how networks within the medial entorhinal cortex and hippocampus support spatial navigation and memory. During her Ph.D., she was the recipient of the Howard Hughes Medical Institute International Student Research Fellowship, and her thesis was awarded the UCSD Biology Founding Faculty Award for Graduate Excellence. She is currently a postdoctoral fellow at New York University (NYU) in the laboratory of Dr. György Buzsáki. Her postdoctoral work was supported by a Leon Levy Fellowship in Neuroscience. She is combining in vivo electrophysiology and optogenetics to pursue her interest in understanding how connections between neurons within the hippocampus give rise to sharp-wave ripples and theta oscillations, both complex physiological phenomena that support memory. In the future, Dr. Zutshi aims to focus on how multiple brain regions act in coordination to select and filter relevant sensory stimuli that subsequently feed into the temporal lobe to form coherent memories.

Recording of the talk:

The talk on computational and neural basis of visual metacognition was given by Dobromir Rahnev, Associate Professor of Psychology, Georgia Institute of Technology. It was held on 26 Aug 2021, 4 PM IST

Abstract: Metacognition is the ability to judge the accuracy of our own decisions. Metacognitive ability is known to be imperfect but the nature of this imperfection is still not understood. I will present a new model of metacognitive imperfection that assumes two separate noise sources: sensory noise that affects both the perceptual decision and the confidence ratings, and metacognitive noise that only affects the confidence ratings. The model makes the counterintuitive prediction that higher sensory noise should lead to better metacognitive efficiency. I will present a series of studies that confirm this prediction and shed light on the nature of this metacognitive noise. Finally, I will link different components of our confidence model to the function of different areas of the prefrontal cortex. Together, these results build the foundation for a mechanistic understanding of visual metacognition.

About the speaker: Dr. Rahnev received his Ph.D. in Psychology from Columbia University in 2012. After completing a 3-year post-doctoral fellowship at UC Berkeley, he joined Georgia Tech in 2015. His research focuses on perceptual decision making – the process of internally representing the available sensory information and making decisions on it. Dr. Rahnev uses a wide variety of methods such as functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), simultaneous TMS-fMRI, psychophysics, and computational modeling. Dr. Rahnev’s work appears in high-impact journals such as Behavioral and Brain Sciences, PNAS, Nature Communications, and Nature Human Behavior. He has received over 3 million dollars in funding, including PI grants from NIH, NSF, and the Office of Naval Research.

Recording of the talk: