Hardware design of LIF with Latency neuron model with memristive STDP synapses

In this paper, the hardware implementation of a neuromorphic system is presented. This system is composed of a Leaky Integrate-and-Fire with Latency (LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL neuron model allows to encode more information than the common Integrate-and-Fire models, typically considered for neuromorphic implementations. In our system LIFL neuron is implemented using CMOS circuits while memristor is used for the implementation of the STDP synapse. A description of the entire circuit is provided. Finally, the capabilities of the proposed architecture have been evaluated by simulating a motif composed of three neurons and two synapses. The simulation results confirm the validity of the proposed system and its suitability for the design of more complex spiking neural network

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Adaptive Closed-Loop Neuromorphic Controller for Use in Respiratory Pacing

Respiratory pacing can treat ventilatory insufficiency through electrical stimulation of the respiratory muscles, or the respective innervating nerves, to induce ventilation. It avoids some of the adverse effects associated with mechanical ventilation such as risk of diaphragm atrophy and lung damage. However, current respiratory pacing systems provide stimulation in an open-loop manner. This often requires users to undergo frequent tuning sessions with trained clinicians if the specified stimulation parameters are unable to induce sufficient ventilation in the presence of time-varying changes in muscle properties, chest biomechanics, and metabolic demand. Lack of adaptation to these changes may lead to complications arising from hyperventilation or hypoventilation. A novel adaptive closed-loop neuromorphic controller for respiratory pacing has been developed to address the need for closed-loop control respiratory pacing capable of responding to changes in metabolic production of CO2, diaphragm muscle health, and biomechanics. A 3-stage processes was utilized to develop the controller. First, an adaptive controller that could follow a preset within-breath volume profile was developed in silico and evaluated in vivo in anesthetized rats with an intact spinal cord or with diaphragm hemiparesis induced by spinal cord hemisection. Second, a neuromorphic computational model was developed to generate a desired trajectory that reflects changes in breath volume and respiratory rate in response to arterial CO2 levels. An enhanced controller capable of generating and matching this model-based desired trajectory was evaluated in silico and in vivo on rats with depressed ventilation and diaphragm hemiparesis. Finally, the enhanced adaptive controller was modified for human-related biomechanics and CO2 dynamics and evaluated in silico under changes of metabolic demand, presence of muscle fatigue, and after randomization of model parameters to reproduce expected between-subject differences. Results showed that the adaptive controller could adapt and modulate stimulation parameters and respiratory rate to follow a desired model-generated breath volume trajectory in response to dynamic arterial CO2 levels. In silico studies aimed at assessing potential for clinical translation showed that an enhanced controller modified for human use could successfully control ventilation to achieve and maintain normocapnic arterial CO2 levels. Overall, these results suggest that use of an adaptive closed-loop controller could lead to improved ventilatory outcomes and quality of life for users of adaptive respiratory pacing

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

The Largest Unethical Medical Experiment in Human History

This monograph describes the largest unethical medical experiment in human history: the implementation and operation of non-ionizing non-visible EMF radiation (hereafter called wireless radiation) infrastructure for communications, surveillance, weaponry, and other applications. It is unethical because it violates the key ethical medical experiment requirement for “informed consent” by the overwhelming majority of the participants. The monograph provides background on unethical medical research/experimentation, and frames the implementation of wireless radiation within that context. The monograph then identifies a wide spectrum of adverse effects of wireless radiation as reported in the premier biomedical literature for over seven decades. Even though many of these reported adverse effects are extremely severe, the true extent of their severity has been grossly underestimated. Most of the reported laboratory experiments that produced these effects are not reflective of the real-life environment in which wireless radiation operates. Many experiments do not include pulsing and modulation of the carrier signal, and most do not account for synergistic effects of other toxic stimuli acting in concert with the wireless radiation. These two additions greatly exacerbate the severity of the adverse effects from wireless radiation, and their neglect in current (and past) experimentation results in substantial under-estimation of the breadth and severity of adverse effects to be expected in a real-life situation. This lack of credible safety testing, combined with depriving the public of the opportunity to provide informed consent, contextualizes the wireless radiation infrastructure operation as an unethical medical experiment