...and a few other areas too. Still if the dorbell sold very well I wouldn't complain at this stage...
Robotics: Robotics can benefit from neuromorphic computing by having more efficient processing, real-time responsiveness, and dynamic adaptability. Neuromorphic systems are designed to operate in real-time and use minimal power, making them ideal for robotic applications where power and processing resources are limited. They can process sensory data as it arrives, allowing robots to respond in real-time to changing environments. Neuromorphic systems can also learn and adapt, which can improve a robot's ability to perform tasks in complex and unpredictable environments.
Neuromorphic robots: on the left a tracker chip mounted on a pan-tilt unit, on the right the iCub humanoid platform featuring event-driven vision sensors. (Image: Chiara Bartolozzi, Istituto Italiano di Tecnologia)
Self-driving cars: It has become clear to engineers that simply making incremental improvements to current technologies will not be enough to achieve the goal of truly autonomous vehicles. Neuromorphic engineering, with its unique approach to perception, computation, and cognition, offers the necessary breakthroughs to make truly autonomous vehicles a reality. Self-driving cars can benefit from neuromorphic computing by having more efficient processing, real-time responsiveness, and improved decision-making capabilities. Neuromorphic systems can process large amounts of sensory data in real-time and make decisions based on that data in a more efficient manner. Additionally, neuromorphic systems have the ability to learn and adapt, which can enable self-driving cars to continuously improve their decision-making abilities over time.
Olfaction and chemosensation: Machine olfaction, or the use of technology to detect and identify odors, has been a pioneer in using neuromorphic techniques to process and analyze sensory data. This is partly due to the in-depth study of the olfactory system in both experimental and computational neuroscience. To further improve and advance machine olfaction, there is a need for larger sensor arrays and advancements in neuromorphic circuitry to better process the data collected by these sensors. Additionally, the study of the various computational techniques used in these systems may help answer important questions about how and when to adjust the system's learning capabilities and the impact of local learning rules on the performance of these systems.
Event vision sensors: Event-based vision sensors are inspired by the workings of the human retina and attempt to recreate its processes for acquiring and processing visual information. These sensors can greatly benefit from neuromorphic engineering by allowing for more efficient processing of visual information, improved accuracy, and greater adaptability. Neuromorphic systems can process large amounts of sensory data related to visual events in real-time, making decisions based on that data more efficiently. This can lead to improved accuracy in detecting and recognizing visual information, as well as faster response times. Additionally, neuromorphic systems have the ability to learn and adapt, which can enable event-based vision sensors to continually improve their performance over time. This can result in a more effective and reliable system for detecting and recognizing visual information.
Neuromorphic audition: Neuromorphic hearing technology is influenced by the incredible power of human hearing. The aim of this technology is to match human hearing abilities through the creation of algorithms, hardware, and applications for artificial hearing devices. By incorporating principles of neuromorphic engineering into the design of artificial hearing devices, researchers aim to create systems that can process auditory information in a manner that is similar to the human brain. This can result in more advanced and sophisticated hearing devices that can better understand speech and sounds in a variety of challenging auditory environments. Additionally, the development of neuromorphic hardware and software can allow for more energy-efficient processing of auditory information, making it possible for these devices to be miniaturized and integrated into various applications. Progress in neuromorphic audition critically depends on advances in both silicon technology and algorithmic development.
Biohybrid systems for brain repair: Artificial devices have been successfully incorporated within neural spheroids and even inside individual living cells. However, these devices have simple designs, compared to the complexity of a neuromorphic system. To create functional biohybrids, it will require collaboration from different fields to tackle the numerous challenges involved. The rapid advancements in fabrication and miniaturization, energy harvesting, learning algorithms, wireless technology, and biodegradable bioelectronics, suggest that it may soon be possible to develop advanced biohybrid neurotechnology to safely and effectively regenerate the brain.
Concept of functional biohybrids for brain regeneration. Functional biohybrids merge concepts from regenerative medicine (rebuild of brain matter) and neuromorphic neuroprosthetics (adaptive control of brain function). The symbiotic interaction between the biological and artificial counterparts in the biohybrid graft is expected to achieve a controlled brain regeneration process. (Image: **riella Panuccio and Mufti Mahmud, Istituto Italiano di Tecnologia)
By incorporating elements of neural networks and artificial intelligence into these systems, it is possible to improve the ability of these systems to interact and interface with the brain in a more natural and intuitive way. This could lead to more effective and efficient rehabilitation strategies for individuals suffering from neurological injuries or disorders.
Embedded devices for neuromorphic time-series: The analysis of time-series data related to humans involves many tasks like understanding speech, detecting keywords, monitoring health, and recognizing human activity. This requires the creation of special devices to help with these tasks. However, there are challenges in processing this type of data for use in these devices, such as cleaning up the raw signals, removing noise, and figuring out the long and short relationships within the data. Already we are seeing commercial off-the-shelf device implementations in the form of fitness monitoring devices, sleep tracking gadgets, and EEG-based brain trauma marker identifying devices. Neuromorphic engineering can be used to develop algorithms that are optimized for analyzing and processing time-variant data. This can lead to improved performance in terms of processing speed and accuracy, as well as reduced power consumption, which is particularly important for wearable devices.
Collaborative autonomous systems: Collaborative Autonomous Systems (CAS) refers to systems that can cooperate among themselves and with humans with variable levels of human intervention (depending on the level of autonomy) in performing complex tasks in unknown environments. An example of CAS would be a fleet of drones working together to survey a large area, or a group of robots performing a task in a factory setting. In these scenarios, the individual systems must be able to communicate with each other and coordinate their actions in order to achieve their goal effectively and efficiently. Other examples of CAS include autonomous vehicles working together in a traffic network, or a group of robots collaborating in a disaster response scenario. The success of CAS requires advanced technologies such as machine learning, artificial intelligence, and networked communication systems.
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