1.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Columbia UniversityUS11 papers
- Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Parma2 papers
- Universidad de GranadaES2 papers
- University of California, Los AngelesUS2 papers
- University of ParmaIT2 papers
- Center for NeurosciencesUS1 paper
- Icahn School of Medicine at Mount SinaiUS1 paper
- Institut des Sciences Cognitives Marc JeannerodFR1 paper
- Institute for Complex SystemsIT1 paper
- Istituto Nazionale di Fisica NucleareIT1 paper
- Massachusetts Institute of TechnologyUS1 paper
- MIND Research InstituteUS1 paper
11 papers
Dimension of activity in random neural networks
David G. Clark, L. F. Abbott, Ashok Litwin-Kumar
Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and…
Improving Across-Dataset Brain Tissue Segmentation Using Transformer
Vishwanatha M. Rao, Zihan Wan, Soroush Arabshahi +6
Brain tissue segmentation has demonstrated great utility in quantifying MRI data through Voxel-Based Morphometry and highlighting subtle structural changes associated with various…
Feedback mechanisms for self-organization to the edge of a phase transition
Victor Buendía, Serena di Santo, Juan A. Bonachela +1
Scale-free outbursts of activity are commonly observed in physical, geological, and biological systems. The idea of self-organized criticality (SOC), introduced back in 1987 by Bak…
Optimal Learning with Excitatory and Inhibitory synapses
Alessandro Ingrosso
Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits. In this work, I…
Going in circles is the way forward: the role of recurrence in visual inference
Ruben S. van Bergen, Nikolaus Kriegeskorte
Biological visual systems exhibit abundant recurrent connectivity. State-of-the-art neural network models for visual recognition, by contrast, rely heavily or exclusively on feedfo…
Self-organized bistability and its possible relevance for brain dynamics
Victor Buendía, Serena di Santo, Pablo Villegas +2
Self-organized bistability (SOB) is the counterpart of 'self-organized criticality' (SOC), for systems tuning themselves to the edge of bistability of a discontinuous phase transit…
Training dynamically balanced excitatory-inhibitory networks
Alessandro Ingrosso, L. F. Abbott
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networ…
Interpreting Encoding and Decoding Models
Nikolaus Kriegeskorte, Pamela K. Douglas
Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their result…
Cognitive computational neuroscience
Nikolaus Kriegeskorte, Pamela K. Douglas
To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments.…
Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs
Tristan Bepler, Andrew Morin, Julia Brasch +3
Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination. However, identifying a sufficient number of particles for analysis (often >…
full-FORCE: A Target-Based Method for Training Recurrent Networks
Brian DePasquale, Christopher J. Cueva, Kanaka Rajan +2
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent n…