Emergent organization of receptive fields in networks of excitatory and inhibitory neurons
This work addresses the organization of neuronal tunings in computational neuroscience, offering insights into neural coding with potential applications in AI, but it is incremental as it builds on existing sparse coding and neural wave models.
The paper tackled the problem of how networks of excitatory and inhibitory neurons can organize receptive fields through computational mechanisms, resulting in emergent topographic maps such as 'pinwheel' patterns for images and 2D representations of word semantics for text.
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.