neural-receptive-fields-hyperbolic-geometry

What problem does it solve?

This skill provides a physio-grounded framework showing how neural receptive fields emerge naturally from the hyperbolic geometry of scale-free networks, removing the need for synaptic fine-tuning.

Core Features & Use Cases

• Hyperbolic embedding of scale-free networks to model neural organization
• Stimulus-space boundary mapping to produce localized activity patterns
• Predicts degree-dependent receptive field sizes and supports multiple modalities (orientation, place fields)
• Compatible with rate-based and spiking neuron dynamics
• Use Case: computational neuroscience research on orientation selectivity and hippocampal place fields

Quick Start

Initialize a scale-free network, apply hyperbolic embedding, map the stimulus space to the boundary, then run rate-based or spiking simulations to observe receptive-field formation.