Systematic errors in connectivity inferred from activity in strongly recurrent networks
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been...
Main Authors: | Das, Abhranil, Fiete, Ila R |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
Format: | Article |
Language: | English |
Published: |
Springer Science and Business Media LLC
2021
|
Online Access: | https://hdl.handle.net/1721.1/135458 |
Similar Items
-
Attractor and integrator networks in the brain
by: Khona, Mikail, et al.
Published: (2023) -
A study of recurrent neural networks for grammatical inference
by: Tan, Poy Boon.
Published: (2009) -
Sources of path integration error in young and aging humans
by: Stangl, Matthias, et al.
Published: (2021) -
Strong Formulations for Network Design Problems with Connectivity Requirements
by: Magnanti, Thomas L., et al.
Published: (2004) -
Recurrent error-based ridge polynomial neural networks for time series forecasting
by: Hassan Saeed, Waddah Waheeb
Published: (2019)