Grid codes vs. multi-scale, multi-field place codes for space.

Introduction: Recent work on bats flying over long distances has revealed that single hippocampal cells have multiple place fields of different sizes. At the network level, a multi-scale, multi-field place cell code outperforms classical single-scale, single-field place codes, yet the performance boundaries of such a code remain an open question. In particular, it is unknown how general multi-field codes compare to a highly regular grid code, in which cells form distinct modules with different scales. Methods: In this work, we address the coding properties of theoretical spatial coding models with rigorous analyses of comprehensive simulations. Starting from a multi-scale, multi-field network, we performed evolutionary optimization. The resulting multi-field networks sometimes retained the multi-scale property at the single-cell level but most often converged to a single scale, with all place fields in a given cell having the same size. We compared the results against a single-scale single-field code and a one-dimensional grid code, focusing on two main characteristics: the performance of the code itself and the dynamics of the network generating it. Results: Our simulation experiments revealed that, under normal conditions, a regular grid code outperforms all other codes with respect to decoding accuracy, achieving a given precision with fewer neurons and fields. In contrast, multi-field codes are more robust against noise and lesions, such as random drop-out of neurons, given that the significantly higher number of fields provides redundancy. Contrary to our expectations, the network dynamics of all models, from the original multi-scale models before optimization to the multi-field models that resulted from optimization, did not maintain activity bumps at their original locations when a position-specific external input was removed. Discussion: Optimized multi-field codes appear to strike a compromise between a place code and a grid code that reflects a trade-off between accurate positional encoding and robustness. Surprisingly, the recurrent neural network models we implemented and optimized for either multi- or single-scale, multi-field codes did not intrinsically produce a persistent "memory" of attractor states. These models, therefore, were not continuous attractor networks.

Silky Liquid Metal Electrodes for On-Skin Health Monitoring.

Next generation on-skin electrodes will require soft, flexible, and gentle materials to provide both high-fidelity sensing and wearer comfort. However, many commercially available on-skin electrodes lack these key properties due to their use of rigid hardware, harsh adhesives, uncomfortable support structures, and poor breathability. To address these challenges, this work presents a new device paradigm by joining biocompatible electrospun spider silk with printable liquid metal to yield an incredibly soft and scalable on-skin electrode that is strain-tolerant, conformable, and gentle on-skin. These electrodes, termed silky liquid metal (SLiM) electrodes, are found to be over five times more breathable than commercial wet electrodes, while the silk's intrinsic adhesion mechanism allows SLiM electrodes to avoid the use of harsh artificial adhesives, potentially decreasing skin irritation and inflammation over long-term use. Finally, the SLiM electrodes provide comparable impedances to traditional wet and other liquid metal electrodes, offering a high-fidelity sensing alternative with increased wearer comfort. Human subject testing confirmed the SLiM electrodes ability to sense electrophysiological signals with high fidelity and minimal irritation to the skin. The unique properties of the reported SLiM electrodes offer a comfortable electrophysiological sensing solution especially for patients with pre-existing skin conditions or surface wounds.

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.