Attractor and integrator networks in the brain
In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects …
describing how the brain maintains persistent activity states for working memory, corrects …
Neural tuning and representational geometry
N Kriegeskorte, XX Wei - Nature Reviews Neuroscience, 2021 - nature.com
A central goal of neuroscience is to understand the representations formed by brain activity
patterns and their connection to behaviour. The classic approach is to investigate how …
patterns and their connection to behaviour. The classic approach is to investigate how …
Toroidal topology of population activity in grid cells
The medial entorhinal cortex is part of a neural system for mapping the position of an
individual within a physical environment. Grid cells, a key component of this system, fire in a …
individual within a physical environment. Grid cells, a key component of this system, fire in a …
The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep
Neural circuits construct distributed representations of key variables—external stimuli or
internal constructs of quantities relevant for survival, such as an estimate of one's location in …
internal constructs of quantities relevant for survival, such as an estimate of one's location in …
Improving performance of robots using human-inspired approaches: a survey
H Qiao, S Zhong, Z Chen, H Wang - Science China Information Sciences, 2022 - Springer
Realizing high performance of ordinary robots is one of the core problems in robotic
research. Improving the performance of ordinary robots usually relies on the collaborative …
research. Improving the performance of ordinary robots usually relies on the collaborative …
Topological data analysis
L Wasserman - Annual Review of Statistics and Its Application, 2018 - annualreviews.org
Topological data analysis (TDA) can broadly be described as a collection of data analysis
methods that find structure in data. These methods include clustering, manifold estimation …
methods that find structure in data. These methods include clustering, manifold estimation …
Persistence images: A stable vector representation of persistent homology
Many data sets can be viewed as a noisy sampling of an underlying space, and tools from
topological data analysis can characterize this structure for the purpose of knowledge …
topological data analysis can characterize this structure for the purpose of knowledge …
The why, how, and when of representations for complex systems
Complex systems, composed at the most basic level of units and their interactions, describe
phenomena in a wide variety of domains, from neuroscience to computer science and …
phenomena in a wide variety of domains, from neuroscience to computer science and …
Two's company, three (or more) is a simplex: Algebraic-topological tools for understanding higher-order structure in neural data
The language of graph theory, or network science, has proven to be an exceptional tool for
addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a …
addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a …
Deep learning with topological signatures
C Hofer, R Kwitt, M Niethammer… - Advances in neural …, 2017 - proceedings.neurips.cc
Inferring topological and geometrical information from data can offer an alternative
perspective in machine learning problems. Methods from topological data analysis, eg …
perspective in machine learning problems. Methods from topological data analysis, eg …