Orientation detection for self-calibrating neuromorphic systems

Bioinspired sensory architectures help build adaptive touch systems with efficient hardware implementations. Orientation detection is a fundamental capability for manipulating objects in real-world scenarios, with diverse applications in robotic manipulation and manufacturing assessment processes. In this work, we built spiking neural networks that combine biologically inspired receptive fields with unsupervised, local, spike-driven learning rules and self-regulating network dynamics to achieve orientation detection. The system is fully compatible with current neuromorphic hardware that operates in online learning conditions. Receptive fields were constructed using predefined connectivity based on biological findings in the somatosensory system, and the system was evaluated on edge orientation discrimination tasks against standard approaches in artificial systems. The system produces sparse, compressed neural representations, supporting energy-efficient neuromorphic implementations. Results show that random and unstructured receptive fields, which spread their inputs across the skin, outperform the regular kernel receptive field on this spatial discrimination task. The advantage is largest at fine angular resolution and small stimulus offset. These findings demonstrate that receptive field design is a key determinant of performance and present a biologically inspired framework for building self-calibrating, low-power tactile perception systems.