Bio-inspired Real Time Sensory Map Realignment in a Robotic Barn Owl
The visual and auditory map alignment in the Superior Colliculus (SC) of barn owl is important for its accurate localization for prey behavior. Prism learning or Blindness may interfere this alignment and cause loss of the capability of accurate prey. However, juvenile barn owl could recover its sensory map alignment by shifting its auditory map. The adaptation of this map alignment is believed based on activity dependent axon developing in Inferior Colliculus (IC). A model is built to explore this mechanism. In this model, axon growing process is instructed by an inhibitory network in SC while the strength of the inhibition adjusted by Spike Timing Dependent Plasticity (STDP). We test and analyze this mechanism by application of the neural structures involved in spatial localization in a robotic system.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Cross-Modal Visuo-Tactile Object Perception
Estimating physical properties is critical for safe and efficient autonomous robotic manipulation, particularly during contact-rich interactions. In such settings, vision and tactile sensing provide complementary informa…
Bayesian InferenceControl for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica
Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source…
Human-Inspired Soft Anthropomorphic Hand System for Neuromorphic Object and Pose Recognition Using Multimodal Signals
The human somatosensory system integrates multimodal sensory feedback, including tactile, proprioceptive, and thermal signals, to enable comprehensive perception and effective interaction with the environment. Inspired b…
Object RecognitionExploring the Relationship between Alignment and Cross-lingual Transfer in Multilingual Transformers
Without any explicit cross-lingual training data, multilingual language models can achieve cross-lingual transfer. One common way to improve this transfer is to perform realignment steps before fine-tuning, i.e., to trai…
Cross-Lingual TransferPOSPOS TaggingXLM-RRethinking what Matters: Effective and Robust Multilingual Realignment for Low-Resource Languages
Realignment is a promising strategy to improve cross-lingual transfer in multilingual language models. However, empirical results are mixed and often unreliable, particularly for typologically distant or low-resource lan…
Cross-Lingual Transfer