22 citations · 48 across the 10 of their papers we have counts for
10 papers
Contextual fusion enhances robustness to image blurring
Shruti Joshi, Aiswarya Akumalla, Seth Haney +1
Mammalian brains handle complex reasoning by integrating information across brain regions specialized for particular sensory modalities. This enables improved robustness and genera…
Residual and Attentional Architectures for Vector-Symbols
Wilkie Olin-Ammentorp Maxim Bazhenov
Vector-symbolic architectures (VSAs) provide methods for computing which are highly flexible and carry unique advantages. Concepts in VSAs are represented by 'symbols,' long vector…
Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations
Wilkie Olin-Ammentorp, Maxim Bazhenov
Despite rapid progress, current deep learning methods face a number of critical challenges. These include high energy consumption, catastrophic forgetting, dependance on global los…
Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks
Wilkie Olin-Ammentorp, Maxim Bazhenov
In this work, we extend standard neural networks by building upon an assumption that neuronal activations correspond to the angle of a complex number lying on the unit circle, or '…
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov +3
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in bio…
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms
Wilkie Olin-Ammentorp, Yury Sokolov, Maxim Bazhenov
Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applic…
Biologically inspired sleep algorithm for artificial neural networks
Giri P Krishnan, Timothy Tadros, Ramyaa Ramyaa +1
Sleep plays an important role in incremental learning and consolidation of memories in biological systems. Motivated by the processes that are known to be involved in sleep generat…
Differential covariance: A new method to estimate functional connectivity in fMRI
Tiger w. Lin, Giri P. Krishnan, Maxim Bazhenov +1
Measuring functional connectivity from fMRI is important in understanding processing in cortical networks. However, because brain's connection pattern is complex, currently used me…
Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings
Tiger W. Lin, Anup Das, Giri P. Krishnan +2
With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship between mult…
Multi-pulse phase resetting curve
Giri P. Krishnan, Maxim Bazhenov, Arkady Pikovsky
In this paper, we introduce and systematically study, in terms of phase response curves (PRC), the effect of a dual pulse excitation on the dynamics of an autonomous oscillator. Sp…