This work is dedicated to the review and perspective of the new direction that we call "Neuropunk revolution" resembling the cultural phenomenon of cyberpunk. This new phenomenon has its foundations in advances in neuromorphic technologies including memristive and bio-plausible simulations, BCI, and neurointerfaces as well as unconventional approaches to AI and computing in general. We present the review of the current state-of-the-art and our vision of near future development of scientific approaches and future technologies. We call the "Neuropunk revolution" the set of trends that in our view provide the necessary background for the new generation of approaches technologies to integrate the cybernetic objects with biological tissues in close loop system as well as robotic systems inspired by the biological processes again integrated with biological objects. We see bio-plausible simulations implemented by digital computers or spiking networks memristive hardware as promising bridge or middleware between digital and (neuro)biological domains.

The generalized synchronization of differentorder chaotic systems with completely unknown parameters is studied and found to possess considerable parallels with two-neuron inhibitory loops. A compound set of adaptive controller and parameter-update law is designed that enforces achieving the generalized synchronization of chaotic or hyper-chaotic systems with different order dynamics via the Lyapunov stability theory. The proposed design does not need either reduced order or increased order dynamics for the drive system. This systematic technique is tested by means of computer simulations, the representative results of which are given to demonstrate both the effectiveness and feasibility of the proposed scheme.

Autonomous vehicles have the potential to improve automotive safety, largely by removing human error as a possible cause of collisions. However, it cannot be guaranteed that autonomous vehicles will be able to eliminate all collisions. Therefore, automotive safety will continue to be a necessity for automotive design. This paper proposes a decision making system which selects the least severe collision for an autonomous vehicle to take, when facing multiple imminent and unavoidable collisions on a motorway. The novel decision making system developed combines simulation results and multi-attribute decision making (MADM) methods. The simulator includes models of vehicle dynamics and the manoeuvre trajectory path. MADM methods are used to decide which vehicle(s) the autonomous vehicle should collide with, based on the severity of collisions. Severity of collisions is calculated in the simulator using the following variables: impact velocity between autonomous vehicle and vehicle ahead, impact velocity between vehicle behind and autonomous vehicle, manoeuvre acceleration and time-to-collision. Various MADM methods are investigated and three methods are selected including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytical Hierarchy Process (AHP), and the Analytical Network Process (ANP). Various collision scenarios are defined and tested in order to understand the impact that small changes in parameters of the autonomous vehicle and vehicles ahead and behind have on the decision made. The analysed decision making results are promising and lead to the conclusion that MADM methods can be successfully applied in autonomous vehicles.

There is mounting evidence acknowledging that embodiment is foundational to cognition. In HCI, this understanding has been incorporated in concepts like embodied interaction, bodily play, and natural user-interfaces. However, while embodied cognition suggests a strong connection between motor activity and memory, we find the design of technological systems that target this connection to be largely overlooked. Considering this, we are provided with an opportunity to extend human capabilities through augmenting motor memory. Augmentation of motor memory is now possible with the advent of new and emerging technologies including neuromodulation, electric stimulation, brain-computer interfaces, and adaptive intelligent systems. This workshop aims to explore the possibility of augmenting motor memory using these and other technologies. In doing so, we stand to benefit not only from new technologies and interactions, but also a means to further study cognition.

In this article, a practical look is taken at some of the possible enhancements for humans through the use of implants, particularly into the brain or nervous system. Some cognitive enhancements may not turn out to be practically useful, whereas others may turn out to be mere steps on the way to the construction of superhumans. The emphasis here is the focus on enhancements that take such recipients beyond the human norm rather than any implantations employed merely for therapy. This is divided into what we know has already been tried and tested and what remains at this time as more speculative. Five examples from the author’s own experimentation are described. Each case is looked at in detail, from the inside, to give a unique personal experience. The premise is that humans are essentially their brains and that bodies serve as interfaces between brains and the environment. The possibility of building an Interplanetary Creature, having an intelligence and possibly a consciousness of its own, is also considered.

I have conducted two specific implant experiments to investigate the merger between humans and technology, thereby creating a cyborg. What I have attempted to do in this chapter is to give some indication of the experiments along with the reasoning behind the arrangement and my feelings which resulted.

Parkinson’s Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using ε-recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that ε-recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system.

Science of artificial neural networks and relevant computing mechanisms since McCullock-Pitts artificial neuron (1943) up via neurons and networks of Anderson (1972), Barto et al (1983), Grosberg (1967, 1976), Hopfield (1982, 1984), Kohonen (1972) to Kasabov's evolving connectionist systems with spiking-neurons (2003) have undergone developments beyond any conceivable predictions. The computational efficiency and functionality of all kinds of neural network implies stable operating steady-state equilibrium is fast established and guaranteed. In parallel, Neurophysiology has yielded many insights Gayton-Hall (2006) converging to paradigm of systems biology. It appeared, on the crossroad of these findings with Hilbert's Thirteen problem and Kolmogorov's Superposition Representations in conjunction with Lyapunov foundations of stability and LaSalle invariance principle certain delicate subtle issues emerged Siljak (2008) and Sprecher (2017). This re-thinking the foundations of neural networks via the quest for parallels between artificial and living neurons is believed to open a new horizon. This belief follows obtained results on cultured-neuron controllers and recurrent neural networks with time-varying delays. A closer look into how animal and/or human brain cells can be cultivated as a controlling brain for a mobile robot (physical body) such that can move around and interact with the world. In turn, a new kind of artificial intelligence may be created, which is emulated by stabilized complex highly non-linear complex neural network system.

Idiopathic Parkinsons disease (PD) is currently the second most important neurodegenerative disease in incidence. Deep brain stimulation (DBS) constitutes a successful and necessary therapy; however, the continuous stimulation it provides can be associated with multiple side effects. DBS uses an implanted pulse generator that delivers, through a set of electrodes, electrical stimulation to the target area, normally the Sub Thalamic Nucleus. Recently, Closed-loop DBS has emerged as a promising new strategy, where the device stimulates only when necessary, thereby reducing any adverse effects. Here, we present a Closed-loop DBS system for PD, which is able to recognize, with 100% accuracy, when the patient is going to enter into the tremor phase, thus allowing the device to stimulate only in such cases. The expert system has been designed and implemented within the data stream mining paradigm, suitable for our scenario since it can cope with continuous data of a theoretical infinite length and with a certain variability, which uses the synchronization among the neural population within the Sub Thalamic Nucleus as the continuous data stream input to the system. (C) 2019 Elsevier Ltd. All rights reserved.

This article contains a directed overview of the field of neuroengineering and neuroprosthetics. The aim of the article is, however, not to go over introductory material covered elsewhere, but rather to look ahead at exciting areas for likely future development. The BrainGate implant is focussed on in terms of its use as an interface between the Internet and the human nervous system. Sensory prosthetics of different types and deep brain stimulation are considered. Different possibilities with deep brain stimulation are also discussed.

The introduction of autonomous vehicles (AVs) is expected to reduce the number of road traffic accidents; however, it is expected that this will not be completely without incidents. In the event of AV incidents, ethical decisions will need to be made, e.g. a decision between colliding into one of three different AVs ahead on a multiple lane highway. A possible solution to the ethical problem is known as the utilitarian approach; this involves the AV steering into the collision path of least loss of utility, e.g. lowest injury level or number of fatalities. Deciding on the collision outcome of such ethical problems involves the investigation of a model-to-decision (M2D) approach. Thus, using a mathematical model of the collision scenarios to then make decisions based on the severity of each outcome as a measure of utility loss. This research investigates the use of such an approach for AVs in convoy on a three-lane highway. The mathematical model of an AV convoy collision that has been developed is of a nonlinear (bilinear) lumped mass spring configuration. This model is used in conjunction with a pragmatic multi-criteria decision maker (MCDM). The results from the M2D approach highlight the effectiveness of the utilisation approach on-board future AVs to minimise utility loss in terms of the severity of road traffic injuries and/or number of fatalities.

Trust is an expected certainty in order to transact confidently. However, how accurate is our decision-making in human-machine interaction? In this chapter, the present evidence from experimental conditions in which human interrogators used their judgement of what constitutes a satisfactory response trusting a hidden interlocutor was human when it was actually a machine. A simultaneous comparison Turing test is presented with conversation between a human judge and two hidden entities during Turing100 at Bletchley Park, UK. Results of post-test conversational analysis by the audience at Turing Education Day show more than 30% made the same identification errors as the Turing test judge. Trust is found to be misplaced in subjective certainty that could lead to susceptibility to deception in cyberspace.

Autonomous vehicle safety has received much attention in recent years. Autonomous vehicles will improve road safety by eliminating human errors. However, not all automotive collisions can be avoided. A strategy needs to be developed in the event when an autonomous vehicle encounters an unavoidable collision. Furthermore, the vehicle will need to take responsibility for the safety of its occupants, as well as any other individuals, who may be affected by the vehicle’s behaviour. This paper proposes a control system to assist an autonomous vehicle to make a decision to reduce the risks to occupants potentially involved in highway motorway collisions. Before any decision can be made, the potential collisions need to be assessed for their effects. A quick and numerical method for evaluation of impact of potential collisions was developed. Assessing the Kinetic Energy of the vehicles before and after collisions is proposed as a method to assess the severity of collisions. A simulation model developed calculates the kinetic energy values and recommends an autonomous vehicle the motorway lane to drive into to cause the least severe collision impact. Different scenarios are defined and used to test the simulation model. The results obtained are promising and in line with the decision made by the subject expert.

In this chapter, the author describes his personal experience in experimenting as a cyborg (part biology/part technology) by having technology implanted in his body, which he lived with over a period. A look is also taken at the author's experiments into creating cyborgs by growing biological brains which are subsequently given a robot body. The experiments are dealt with in separate sections. In each case the nature of the experiment is briefly described along with the results obtained and this is followed by an indication of the experience, including personal feelings and emotions felt in and around the time of the experiments and subsequently as a result of the experiments. Although the subject can be treated scientifically from an external perspective, it is really through individual, personal experience that a true reflection can be gained on what might be possible in the future.

In this paper, we consider the use of permanent implanted magnets inserted into an individual’s fingers as a form of human computer interface, the magnets being excited by an external coil. Tests involving amplitude detection, amplitude discrimination, frequency discrimination, temporal discrimination, and temporal gap detection were performed on implanted subjects. As a comparison the same tests were performed on individuals who had identical magnets attached to the outside of their skin. Results indicated that much smaller stimulation currents were required to achieve a sensitivity response in the implanted subjects. It is apparent that different corpuscles are affected by complex signals at different frequencies and this has a considerable effect on the results obtained and hence on the type of stimulation that can best be applied.