# Emergent organization of receptive fields in networks of excitatory and inhibitory neurons

arXiv: https://arxiv.org/abs/2205.13614 · HTML: https://mlsanity.com/paper/2205.13614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 13 of 13)

- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Direct Feedback Alignment Provides Learning in Deep Neural Networks](https://mlsanity.com/paper/1609.01596/) (2016)
- [Deep Learning without Weight Transport](https://mlsanity.com/paper/1904.05391/) (2019)
- [Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures](https://mlsanity.com/paper/2006.12878/) (2020)
- [Sparse Overcomplete Word Vector Representations](https://mlsanity.com/paper/1506.02004/) (2015)
- [Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets](https://mlsanity.com/paper/1901.09049/) (2019)
- [Convergence and Alignment of Gradient Descent with Random Backpropagation Weights](https://mlsanity.com/paper/2106.06044/) (2021)
- [From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction](https://mlsanity.com/paper/1912.06207/) (2019)
- [Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity](https://mlsanity.com/paper/1705.01502/) (2017)
- [Learning Word Representations with Hierarchical Sparse Coding](https://mlsanity.com/paper/1406.2035/) (2014)
- [Word Equations: Inherently Interpretable Sparse Word Embeddingsthrough Sparse Coding](https://mlsanity.com/paper/2004.13847/) (2020)
- [Conditions for wave trains in spiking neural networks](https://mlsanity.com/paper/1801.06046/) (2018)

## 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.