104 citations · 125 across the 15 of their papers we have counts for
19 papers
A neural network that maintains and retrieves memories based on context
Hayoung Song, JeongJun Park, Qihong Lu +4
Every day, people continuously infer situational context and adjust the way they understand and remember the world. Context, signaled by the prefrontal cortex, is known to modulate…
A neural-astrocyte architecture implements a hybrid automaton for evidence accumulation
Giacomo Vedovati, Ilya E. Monosov, Thomas J. Papouin +1
Astrocytes are non-neuronal glial cells that are receiving widespread attention due to their emerging role in neural computation. In this paper, we propose and study dynamical mech…
Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment
Ruiqi Chen, Giacomo Vedovati, Todd Braver +1
Dynamical systems models such as recurrent neural networks (RNNs) are increasingly popular in theoretical neuroscience for hypothesis-generation and data analysis. Evaluating the d…
Control of neural field equations with step-function inputs
Cyprien Tamekue, ShiNung Ching
Wilson-Cowan and Amari-type models capture nonlinear neural population dynamics, providing a fundamental framework for modeling how sensory and other exogenous inputs shape activit…
Episodically adapted network-based controllers
Sruti Mallik, ShiNung Ching
We consider the problem of distributing a control policy across a network of interconnected units. Distributing controllers in this way has a number of potential advantages, especi…
On the control of recurrent neural networks using constant inputs
Cyprien Tamekue, Ruiqi Chen, ShiNung Ching
This paper investigates the controllability of a broad class of recurrent neural networks widely used in theoretical neuroscience, including models of large-scale human brain dynam…
Synergistic pathways of modulation enable robust task packing within neural dynamics
Giacomo Vedovati, ShiNung Ching
Understanding how brain networks learn and manage multiple tasks simultaneously is of interest in both neuroscience and artificial intelligence. In this regard, a recent research t…
DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models
Ruiqi Chen, Giacomo Vedovati, Todd Braver +1
Dynamical system models such as Recurrent Neural Networks (RNNs) have become increasingly popular as hypothesis-generating tools in scientific research. Evaluating the dynamics in…
Strong anti-Hebbian plasticity alters the convexity of network attractor landscapes
Lulu Gong, Xudong Chen, ShiNung Ching
In this paper, we study recurrent neural networks in the presence of pairwise learning rules. We are specifically interested in how the attractor landscapes of such networks become…
Astrocytes as a mechanism for meta-plasticity and contextually-guided network function
Lulu Gong, Fabio Pasqualetti, Thomas Papouin +1
Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is…
Contextual guidance: An integrated theory for astrocytes function in brain circuits and behavior
Ciaran Murphy-Royal, ShiNung Ching, Thomas Papouin
The participation of astrocytes in brain computation was formally hypothesized in 1992, coinciding with the discovery that these glial cells display a complex form of Ca2+ excitabi…
Representation Learning for Context-Dependent Decision-Making
Yuzhen Qin, Tommaso Menara, Samet Oymak +2
Humans are capable of adjusting to changing environments flexibly and quickly. Empirical evidence has revealed that representation learning plays a crucial role in endowing humans…
Non-Stationary Representation Learning in Sequential Linear Bandits
Yuzhen Qin, Tommaso Menara, Samet Oymak +2
In this paper, we study representation learning for multi-task decision-making in non-stationary environments. We consider the framework of sequential linear bandits, where the age…
Efficient state and parameter estimation for high-dimensional nonlinear system identification with application to MEG brain network modeling
Matthew F. Singh, Chong Wang, Michael W. Cole +1
System identification poses a significant bottleneck to characterizing and controlling complex systems. This challenge is greatest when both the system states and parameters are no…
Slow manifolds in recurrent networks encode working memory efficiently and robustly
Elham Ghazizadeh, ShiNung Ching
Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent compu…
Input Novelty as a Control Metric for Time Varying Linear Systems
Gautam Kumar, Delsin Menolascino, ShiNung Ching
This paper introduces a framework for quantitative characterization of the controllability of time-varying linear systems (or networks) in terms of input novelty. The motivation fo…
Controlling Linear Networks with Minimally Novel Inputs
Gautam Kumar, Delsin Menolascino, MohammadMehdi Kafashan +1
In this paper, we propose a novelty-based metric for quantitative characterization of the controllability of complex networks. This inherently bounded metric describes the average…
Analysis of Recurrent Linear Networks for Enabling Compressed Sensing of Time-Varying Signals
MohammadMehdi Kafashan, Anirban Nandi, ShiNung Ching
Recent interest has developed around the problem of dynamic compressed sensing, or the recovery of time-varying, sparse signals from limited observations. In this paper, we study h…
On the output of nonlinear systems excited by discrete prolate spheroidal sequences
Kyle Q. Lepage, ShiNung Ching
The discrete prolate spheroidal sequences (DPSSs) - a set of optimally bandlimited sequences with unique properties - are important to applications in both science and engineering.…