This is a past event.
Oct 28, 2019 / 6:00 pm - 9:00 pm
Samberg MIT Conference Center (7th floor)
The Simons Collaboration on the Global Brain invites you to the Boston-area SCGB Postdoc Meeting. The purpose of these meetings is to bring together postdocs interested in neural coding and dynamics to discuss ideas and data. We will have two postdocs presentations, followed by dinner, drinks, and lively discussion.
The meeting is co-organized by Ishita Basu, a postdoc at Harvard, and Sourish Chakravarty, a postdoc at MIT, and will take place at the Samberg Conference Center (6th Floor, Dining Room 3).
The speakers are:
Postdoctoral Associate, MIT
Hippocampal Remapping as Hidden State Inference
Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a “context change” has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.
Assistant Professor of Computer Science, WPI (Formerly Postdoc at BU; Advisor: Uri T. Eden)
Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach
Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants (N=8) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.
6:00 guests arrive at 6th Floor Room 3, drinks and snacks available
6:30-7:10 Talk 1 and Q&A in Room 3
7:10-7:50 Talk 2 and Q&A in Room 3
8:00-9:00 Dinner, drinks, and discussion next door in Dining Room 2
Dinner and beverages will be served. Please forward this to colleagues that you think will be interested.
*You may be eligible for transportation reimbursement to this event. Please email firstname.lastname@example.org for more information.
We look forward to seeing you there!
Signup is required.
Oct 28, 2019 / Monday
6:00 pm - 9:00 pm
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