Emergent organization of receptive fields in networks of excitatory and inhibitory neurons
Not on the map. Why?
Abstract (arXiv)
Local patterns of excitation and inhibition that can generate neural waves are studied as a computational mechanism underlying the organization of neuronal tunings. Sparse coding algorithms based on networks of excitatory and inhibitory neurons are proposed that exhibit topographic maps as the receptive fields are adapted to input stimuli. Motivated by a leaky integrate-and-fire model of neural waves, we propose an activation model that is more typical of artificial neural networks. Computational experiments with the activation model using both natural images and natural language text are presented. In the case of images, familiar "pinwheel" patterns of oriented edge detectors emerge; in the case of text, the resulting topographic maps exhibit a 2-dimensional representation of granular word semantics. Experiments with a synthetic model of somatosensory input are used to investigate how the network dynamics may affect plasticity of neuronal maps under changes to the inputs.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 60 references that are in the map. First posted on arXiv 26 May 2022.
Builds on
13 of 60 references.
References · 13
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
- 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
- 2016Direct Feedback Alignment Provides Learning in Deep Neural Networkscited by 240
- 2019Deep Learning without Weight Transportcited by 87
- 2020Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architecturescited by 56
- 2015Sparse Overcomplete Word Vector Representationscited by 43
- 2019Biologically inspired alternatives to backpropagation through time for learning in recurrent neural netscited by 20
- 2021Convergence and Alignment of Gradient Descent with Random Backpropagation Weightscited by 14
- 2019From deep learning to mechanistic understanding in neuroscience: the structure of retinal predictioncited by 11
- 2017Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivitycited by 7
- 2014Learning Word Representations with Hierarchical Sparse Codingcited by 4
- 2020Word Equations: Inherently Interpretable Sparse Word Embeddingsthrough Sparse Codingcited by 3
1 more
- 2018Conditions for wave trains in spiking neural networkscited by 1
Built on by
Not cited yet.