Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning

With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to DNNs with vanilla artificial neurons, the biologically plausible Spiking Neural Network (SNN) contains a diverse population of spiking neurons, making it naturally powerful on state representation with spatial and temporal information. Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding. For input coding, we apply population coding with dynamically receptive fields to directly encode each input state component. For neuronal coding, we propose different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN is trained in conjunction with deep critic networks using the Twin Delayed Deep Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental results show that our TD3-PDSAN model achieves better performance than state-of-the-art models on four OpenAI gym benchmark tasks. It is an important attempt to improve RL with SNN towards the effective computation satisfying biological plausibility.Comment: 27 pages, 11 figures, accepted by Journal of Neural Network

Technology training program: addressing the distinct telehealth challenges of occupational therapy practitioners in mental health practice

As the delivery of occupational therapy (OT) services via telehealth has dramatically expanded after the COVID-19 pandemic in 2020 (AOTA, 2022), the effective utilization of Information Communication Technologies (ICTs) along with psychosocial skill proficiency is at the forefront of healthcare within a mounting mental health crisis (WHO, 2022). Despite the essential application of ICTs, occupational therapy practitioners (OTPs) apparently lack knowledge, skills and confidence utilizing telehealth effectively (Aboujaoudé et al., 2021; Campbell et al., 2019; Chike-Harris et al., 2021; Corey, 2019; Hermes et al., 2021; Hoel et al., 2020; Larsson-Lund & Nyman, 2019; McClellan et al., 2020; Miranda-Duro et al., 2021). Moreover, research suggests that OTPs further lack confidence incorporating psychosocial abilities and personal attributes, such as therapeutic use of self (Anderson & Halbakken, 2020; Birken et al., 2017; Taylor, 2020). These obstacles jeopardize the distinct value of OT in mental health, as well as the overall viability of the profession. The Technology Training Program (TTP) is a multi-module, curriculum-based ICT skills training intervention that promotes professional advancement in these areas of clinical practice. OTP proficiency in technology and psychosocial skills is a crucial investment that promotes overall competence and self-efficacy to successfully meet global challenges and healthcare demands within a rapidly evolving digital landscape.