The effects of feedback and lateral connections on perceptual processing: A study using oscillatory networks

Rao, A. Ravishankar; Cecchi, Guillermo A.

doi:10.1109/ijcnn.2011.6033357

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…At least certain forms of backpropagation appear compatible with a number of biological observations, e.g., spike timing-dependent plasticity (Scellier and Bengio, 2016 ). Deep neural networks that use spikes are becoming commonplace (Yu et al, 2013 ; Cao et al, 2015 ; Diehl et al, 2015 ; Hunsberger and Eliasmith, 2016 ; Kheradpisheh et al, 2016a ; Lee et al, 2016 ; Zambrano and Bohte, 2016 ), and attempts have also been made to introduce oscillatory components in deep networks (Rao and Cecchi, 2011 , 2013 ; Reichert and Serre, 2013 ). Finally, new DNN architectures are emerging with long-range feedforward (Huang et al, 2016a , b ) and feedback connectivity (Pascanu et al, 2013 ; Zilly et al, 2016 ).…”

Section: What Dreams May Comementioning

confidence: 99%

Perception Science in the Age of Deep Neural Networks

VanRullen

2017

Front. Psychol.

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Section: What Dreams May Comementioning

confidence: 99%

Perception Science in the Age of Deep Neural Networks

VanRullen

2017

Front. Psychol.

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A theory of relation learning and cross-domain generalization.

Doumas¹,

Puebla²,

Martin³

et al. 2022

Psychological Review

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People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations. The model is an extension of the Learning and Inference with Schemas and Analogy (LISA; Hummel & Holyoak, 1997, 2003) and Discovery of Relations by Analogy (DORA; Doumas et al., 2008) models of relational inference and learning. The resulting model learns both the content and format (i.e., structure) of relational representations from nonrelational inputs without supervision, when augmented with the capacity for reinforcement learning it leverages these representations to learn about individual domains, and then generalizes to new domains on the first exposure (i.e., zero-shot learning) via analogical inference. We demonstrate the capacity of the model to learn structured relational representations from a variety of simple visual stimuli, and to perform cross-domain generalization between video games (Breakout and Pong) and between several psychological tasks. We demonstrate that the model’s trajectory closely mirrors the trajectory of children as they learn about relations, accounting for phenomena from the literature on the development of children’s reasoning and analogy making. The model’s ability to generalize between domains demonstrates the flexibility afforded by representing domains in terms of their underlying relational structure, rather than simply in terms of the statistical relations between their inputs and outputs.

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The effects of feedback and lateral connections on perceptual processing: A study using oscillatory networks

Cited by 2 publications

References 9 publications

Perception Science in the Age of Deep Neural Networks

Perception Science in the Age of Deep Neural Networks

A theory of relation learning and cross-domain generalization.

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