no code implementations • 28 Oct 2022 • Gagan Acharya, Sebastian F. Ruf, Erfan Nozari
Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as Parkinson's Disease, and depression.
no code implementations • 14 Dec 2021 • Tim Hahn, Hamidreza Jamalabadi, Erfan Nozari, Nils R. Winter, Jan Ernsting, Marius Gruber, Marco J. Mauritz, Pascal Grumbach, Lukas Fisch, Ramona Leenings, Kelvin Sarink, Julian Blanke, Leon Kleine Vennekate, Daniel Emden, Nils Opel, Dominik Grotegerd, Verena Enneking, Susanne Meinert, Tiana Borgers, Melissa Klug, Elisabeth J. Leehr, Katharina Dohm, Walter Heindel, Joachim Gross, Udo Dannlowski, Ronny Redlich, Jonathan Repple
We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses.
no code implementations • 27 Feb 2021 • Erfan Nozari, Robert Planas, Jorge Cortes
Exploiting the switched-affine nature of this dynamics, we obtain various necessary and/or sufficient conditions on the network structure and its external input for the existence of oscillations in (i) two-dimensional excitatory-inhibitory networks (E-I pairs), (ii) networks with one inhibitory but arbitrary number of excitatory nodes, (iii) purely inhibitory networks with an arbitrary number of nodes, and (iv) networks of E-I pairs.
1 code implementation • 22 Dec 2020 • Erfan Nozari, Maxwell A. Bertolero, Jennifer Stiso, Lorenzo Caciagli, Eli J. Cornblath, Xiaosong He, Arun S. Mahadevan, George J. Pappas, Dani Smith Bassett
Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics, eliminating the trade-off between accuracy and simplicity.
no code implementations • 3 May 2020 • Jason Z. Kim, Zhixin Lu, Erfan Nozari, George J. Pappas, Danielle S. Bassett
Here we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory.
no code implementations • 5 Sep 2018 • Erfan Nozari, Jorge Cortés
Goal-driven selective attention (GDSA) is a remarkable function that allows the complex dynamical networks of the brain to support coherent perception and cognition.
no code implementations • 5 Sep 2018 • Erfan Nozari, Jorge Cortés
Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions.