The paper "In-memory and in-sensor reservoir computing with memristive devices" by Ning Lin et al. explores the advancements in reservoir computing (RC) for brain-like computing, focusing on in-memory and in-sensor implementations. RC leverages emerging electronic and optoelectronic devices to process data directly where it is stored or sensed, reducing energy consumption and computational latency. The authors survey recent developments in RC models, including echo state networks (ESNs), liquid state machines (LSMs), and single dynamic nodes with delayed feedback, and discuss their hardware and software aspects. They also review the materials and devices used in these systems, such as redox resistive switches, ferroelectric tunneling junctions, perovskite resistive switches, and organic electrochemical transistors. The paper highlights the energy efficiency and real-time learning capabilities of RC systems, particularly in classification and regression problems involving various data modalities like images, audio, and graphs. Finally, the authors provide an outlook on future directions, including hardware improvements, architectural innovations, and expanded application areas, such as multimodal data fusion and deep in-sensor RC.The paper "In-memory and in-sensor reservoir computing with memristive devices" by Ning Lin et al. explores the advancements in reservoir computing (RC) for brain-like computing, focusing on in-memory and in-sensor implementations. RC leverages emerging electronic and optoelectronic devices to process data directly where it is stored or sensed, reducing energy consumption and computational latency. The authors survey recent developments in RC models, including echo state networks (ESNs), liquid state machines (LSMs), and single dynamic nodes with delayed feedback, and discuss their hardware and software aspects. They also review the materials and devices used in these systems, such as redox resistive switches, ferroelectric tunneling junctions, perovskite resistive switches, and organic electrochemical transistors. The paper highlights the energy efficiency and real-time learning capabilities of RC systems, particularly in classification and regression problems involving various data modalities like images, audio, and graphs. Finally, the authors provide an outlook on future directions, including hardware improvements, architectural innovations, and expanded application areas, such as multimodal data fusion and deep in-sensor RC.