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Role of homeostasis in learning sparse representations
1Institut de Neurosciences Cognitives de Méditerranée, CNRS/University of Provence, 13402 Marseille Cedex 20, France. Laurent.Perrinet@incm.cnrs-mrs.fr
Neural Computation
|March 19, 2010
Summary
Homeostasis optimizes neural representation by tuning competition in sparse coding algorithms. This cooperative mechanism ensures fair competition, leading to efficient learning of natural image features by visual cortex neurons.
Area of Science:
- Computational neuroscience
- Systems neuroscience
- Machine learning
Background:
- Neurons in the primate visual cortex develop edge-like receptive fields.
- Efficient neural coding of natural scenes is thought to involve sparse representations achieved through neuronal competition.
- Existing sparse coding models incorporate Hebbian learning and homeostasis, but homeostasis's role remains unclear.
Purpose of the Study:
- To investigate the specific role of homeostasis in learning sparse representations.
- To derive a cooperative homeostasis mechanism that optimizes neuronal competition within sparse coding.
- To quantitatively assess the efficiency of neural representation during learning.
Main Methods:
- Developed a cooperative homeostasis mechanism to tune neuronal competition in sparse coding.
- Applied the homeostasis mechanism to learn from natural image patches.
- Compared the efficiency of the proposed homeostasis with state-of-the-art sparse coding algorithms.
Main Results:
- Different sparse coding algorithms yield similar coding outcomes.
- The proposed homeostasis mechanism provides an optimal balance for representing natural images across neuronal populations.
- Fair competition among neurons optimizes sparse coding.
Conclusions:
- Homeostasis plays a crucial role in optimizing statistical competition among neurons.
- This optimized competition leads to a more efficient emergence of independent components.
- The derived cooperative homeostasis mechanism offers an improved solution for sparse coding of natural images.
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