Event type: Seminar

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11
Oct

Anita Layton
(University of Waterloo)

His and Her Mathematical Models of Physiological Systems
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Imagine someone having a heart attack. Do you visualize the dramatic Hollywood portrayal of a heart attack, in which a man collapses, grabbing his chest in agony? Even though heart disease is the leading killer of women worldwide, the misconception that heart disease is a men’s disease has persisted. A dangerous misconceptions and risks women ignoring their own symptoms. Gender biases and false impressions are by no means limited to heart attack symptoms. Such prejudices exist throughout our healthcare system, from scientific research to disease diagnosis and treatment strategies. A goal of our research program is to address this gender equity, by identifying and disseminating insights into sex differences in health and disease, using computational modeling tools.
Oct 11, 2022 DRL 2C8 Seminar
Next Event
06
Sep

Marte Julie Sætra
(Simula Research Laboratory)

Computational modeling of ion concentration dynamics in brain tissue
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Over the past decades, computational neuroscientists have developed ever more sophisticated and morphologically complex neuron models. Most of these models assume that the intra- and extracellular ion concentrations remain constant over the simulated period and thus do not account for concentration-dependent effects on neuronal firing properties. Of the models that do incorporate ion concentration dynamics, few account for the electrodiffusive nature of intra- and extracellular ion transport. In this talk, I will present the first multicompartmental neuron model that accounts for ion concentration dynamics in a biophysically consistent manner [1]. I will also show how electrodiffusive modeling of neurons and glial cells can be used to explore the genesis of slow potentials in the brain [2].
Sep 6, 2022 DRL 2C8 Seminar
Next Event
22
Mar

Danielle Bassett
(University of Pennsylvania)

Mar 22, 2022 Online Seminar
Next Event
29
Mar

Georgi Medvedev
(Drexel University)

Mar 29, 2022 Online & DRL Room A1 Seminar
Next Event
05
Apr

Andrew Mugler
(University of Pittsburgh)

Apr 5, 2022 Online & DRL Room A1 Seminar
Next Event
12
Apr

Isaac Klapper
(Temple University)

Apr 12, 2022 Online & DRL Room A1 Seminar
Next Event
19
Apr

Katie Storey
(Lafayette University)

Apr 19, 2022 DRL Room A1 Seminar
Next Event
26
Apr

Alexandre Morozov
(Rutgers University)

Apr 26, 2022 Online & DRL Room A1 Seminar
Next Event
27
Apr

Cengiz Pehlevan
(Harvard University)

Inductive Bias of Neural Networks
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Predicting a previously unseen example from training examples is unsolvable without additional assumptions about the nature of the task at hand. A learner’s performance depends crucially on how its internal assumptions, or inductive biases, align with the task. I will present a theory that describes the inductive biases of neural networks using kernel methods and statistical mechanics. This theory elucidates an inductive bias to explain data with “simple functions, which are identified by solving a related kernel eigenfunction problem on the data distribution. This notion of simplicity allows us to characterize whether a network is compatible with a learning task, facilitating good generalization performance from a small number of training examples. I will present applications of this theory to artificial and biological neural systems, and real datasets.
Apr 27, 2021 Online Seminar
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25
Jan

Job talk

Jan 25, 2022 Online Seminar
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