Yuchen Xiao

Yingchao Yu, Yaochu Jin, Kuangrong Hao et al.

Learning-to-learn (L2L), defined as progressively faster learning across similar tasks, is fundamental to both neuroscience and artificial intelligence. However, its neural basis remains elusive, as most studies emphasize neural population dynamics induced by synaptic plasticity while overlooking adaptations driven by intrinsic neuronal plasticity, which point-neuron models cannot capture. To address the above issue, we develop a recurrent spiking neural network with bi-level intrinsic plasticity (IP$^{2}$-RSNN). First, based on task demands, a slow meta-intrinsic plasticity determines which intrinsic neuronal properties are learnable, which is preserved throughout subsequent task learning once configured. Second, a fast intrinsic plasticity fine-tunes those learnable properties within each task. Our results indicate that the proposed bi-level intrinsic plasticity plays a critical role in enabling L2L in RSNNs and show that IP$^{2}$-RSNNs outperform point-neuron recurrent neural networks and self-attention models. Furthermore, our analysis of multi-scale neural dynamics reveals that the bi-level intrinsic plasticity is essential to task-type-specific adaptations at both the neuronal and network levels during L2L, while such adaptations cannot be captured by point-neuron models. Our results suggest that intrinsic plasticity provides significant computational advantages in L2L, shedding light on the design of brain-inspired deep learning models and algorithms.

Pedro Piacenza, Emily Hannigan, Clayton Baumgart et al.

Traditional methods for achieving high localization accuracy on tactile sensors usually involve a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt to non-planar geometries. We propose a method where sensing terminals are embedded in a volume of soft material. Mechanical strain in this material results in a measurable signal between any two given terminals. By having multiple terminals and pairing them against each other in all possible combinations, we obtain a rich signal set using few wires. We mine this data to learn the mapping between the signals we extract and the contact parameters of interest. Our approach is general enough that it can be applied with different transduction methods, and achieves high accuracy in identifying indentation location and depth. Moreover, this method lends itself to simple fabrication techniques and makes no assumption about the underlying geometry, potentially simplifying future integration in robot hands.

Sangwoo Park, Lauri Bishop, Tara Post et al.

Fully wearable hand rehabilitation and assistive devices could extend training and improve quality of life for patients affected by hand impairments. However, such devices must deliver meaningful manipulation capabilities in a small and lightweight package. In this context, this paper investigates the capability of single-actuator devices to assist whole-hand movement patterns through a network of exotendons. Our prototypes combine a single linear actuator (mounted on a forearm splint) with a network of exotendons (routed on the surface of a soft glove). We investigated two possible tendon network configurations: one that produces full finger extension (overcoming flexor spasticity), and one that combines proximal flexion with distal extension at each finger. In experiments with stroke survivors, we measured the force levels needed to overcome various levels of spasticity and open the hand for grasping using the first of these configurations, and qualitatively demonstrated the ability to execute fingertip grasps using the second. Our results support the feasibility of developing future wearable devices able to assist a range of manipulation tasks.