Towards the mind of a humanoid: Does a cognitive robot need a self? - Lessons from neuroscience

As we endow cognitive robots with ever more human-like capacities, these have begun to resemble constituent aspects of the 'self' in humans (e.g., putative psychological constructs such as a narrative self, social self, somatic self and experiential self). Robot's capacity for body-mapping and social learning in turn facilitate skill acquisition and development, extending cognitive architectures to include temporal horizon by using autobiographical memory (own experience) and inter-personal space by mapping the observations and predictions on the experience of others (biographic reconstruction). This 'self-projection' into the past and future as well as other's mind can facilitate scaffolded development, social interaction and planning in humanoid robots. This temporally extended horizon and social capacities newly and increasingly available to cognitive roboticists have analogues in the function of the Default Mode Network (DMN) known from human neuroscience, activity of which is associated with self-referencing, including discursive narrative processes about present moment experience, 'self-projection' into past memories or future intentions, as well as the minds of others. Hyperactivity and overconnectivity of the DMN, as well as its co-activation with the brain networks related to affective and bodily states have been observed in different psychopathologies. Mindfulness practice, which entails reduction in narrative self-referential processing, has been shown to result in an attenuation of the DMN activity and its decoupling from other brain networks, resulting in more efficient brain dynamics, and associated gains in cognitive function and well-being. This suggests that there is a vast space of possibilities for orchestrating self-related processes in humanoids together with other cognitive activity, some less desirable or efficient than others. Just as for humans, relying on emergence and self-organization in humanoid scaffolded cognitive development might not always lead to the 'healthiest' and most efficient modes of cognitive dynamics. Rather, transient activations of self-related processes and their interplay dependent on and appropriate to the functional context may be better suited for the structuring of adaptive robot cognition and behaviour.This work was supported in part by the European Commission under projects ITALK ("Integration and Transfer of Action and Language in Robots") and BIOMICS (contract numbers FP7-214668 and FP7-318202, respectively) to Prof Nehaniv, and by the King’s Together Fund award (“Towards Experiential Neuroscience Paradigm”) to Dr Antonova

Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences

We introduce new techniques to the analysis of neural spatiotemporal dynamics via applying $\epsilon$-machine reconstruction to electroencephalography (EEG) microstate sequences. Microstates are short duration quasi-stable states of the dynamically changing electrical field topographies recorded via an array of electrodes from the human scalp, and cluster into four canonical classes. The sequence of microstates observed under particular conditions can be considered an information source with unknown underlying structure. $\epsilon$-machines are discrete dynamical system automata with state-dependent probabilities on different future observations (in this case the next measured EEG microstate). They artificially reproduce underlying structure in an optimally predictive manner as generative models exhibiting dynamics emulating the behaviour of the source. Here we present experiments using both simulations and empirical data supporting the value of associating these discrete dynamical systems with mental states (e.g. mind-wandering, focused attention, etc.) and with clinical populations. The neurodynamics of mental states and clinical populations can then be further characterized by properties of these dynamical systems, including: i) statistical complexity (determined by the number of states of the corresponding $\epsilon$-automaton); ii) entropy rate; iii) characteristic sequence patterning (syntax, probabilistic grammars); iv) duration, persistence and stability of dynamical patterns; and v) algebraic measures such as Krohn-Rhodes complexity or holonomy length of the decompositions of these. The potential applications include the characterization of mental states in neurodynamic terms for mental health diagnostics, well-being interventions, human-machine interface, and others on both subject-specific and group/population-level

Eliminating the mystery from the concept of emergence

While some branches of complexity theory are advancing rapidly, the same cannot be said for our understanding of emergence. Despite a complete knowledge of the rules underlying the interactions between the parts of many systems, we are often baffled by their sudden transitions from simple to complex. Here I propose a solution to this conceptual problem. Given that emergence is often the result of many interactions occurring simultaneously in time and space, an ability to intuitively grasp it would require the ability to consciously think in parallel. A simple exercise is used to demonstrate that we do not possess this ability. Our surprise at the behaviour of cellular automata models, and the natural cases of pattern formation they mimic, is then explained from this perspective. This work suggests that the cognitive limitations of the mind can be as significant a barrier to scientific progress as the limitations of our senses

Co-designing the computational model and the computing substrate

Given a proposed unconventional computing substrate, we can ask: Does it actually compute? If so, how well does it compute? Can it be made to compute better? Given a proposed unconventional computational model we can ask: How powerful is the model? Can it be implemented in a substrate? How faithfully or efficiently can it be implemented? Given complete freedom in the choice of model and substrate, we can ask: Can we co-design a model and substrate to work well together? Here I propose an approach to posing and answering these questions, building on an existing definition of physical computing and framework for characterising the computing properties of given substrates

EEG Microstate Syntax Analysis: A Review of Methodological Challenges and Advances

Data availability: No data was used for the research described in the article.Electroencephalography (EEG) microstates are “quasi-stable” periods of electrical potential distribution in multichannel EEG derived from peaks in Global Field Power. Transitions between microstates form a temporal sequence that may reflect underlying neural dynamics. Mounting evidence indicates that EEG microstate sequences have long-range, non-Markovian dependencies, suggesting a complex underlying process that drives EEG microstate syntax (i.e., the transitional dynamics between microstates). Despite growing interest in EEG microstate syntax, the field remains fragmented, with inconsistent terminologies used between studies and a lack of defined methodological categories. To advance the understanding of functional significance of microstates and to facilitate methodological comparability and finding replicability across studies, we: i) derive categories of syntax analysis methods, reviewing how each may be utilised most readily; ii) define three “time-modes” for EEG microstate sequence construction; and iii) outline general issues concerning current microstate syntax analysis methods, suggesting that the microstate models derived using these methods are cross-referenced against models of continuous EEG. We advocate for these continuous approaches as they do not assume a winner-takes-all model inherent in the microstate derivation methods and contextualise the relationship between microstate models and EEG data. They may also allow for the development of more robust associative models between microstates and functional Magnetic Resonance Imaging data.US Air Force Office of Scientific Research awarded to CN (PI), EA (joint-PI), SK (joint-PI), and RL (co-I) (Award N: FA9550-19-1-7034) and the PhD studentship from the School of Physics, Engineering and Computer Science, University of Hertfordshire, UK awarded to SK, CN and EA. This work was also supported in part by the research grant from the National Institutes of Health (Award N: RO1DC017734-05)

Atom Tracking Using Cayley Graphs

While atom tracking with isotope-labeled compounds is an essential and sophisticated wet-lab tool in order to, e.g., illuminate reaction mechanisms, there exists only a limited amount of formal methods to approach the problem. Specifically when large (bio-)chemical networks are considered where reactions are stereo-specific, rigorous techniques are inevitable. We present an approach using the right Cayley graph of a monoid in order to track atoms concurrently through sequences of reactions and predict their potential location in product molecules. This can not only be used to systematically build hypothesis or reject reaction mechanisms (we will use the mechanism “Addition of the Nucleophile, Ring Opening, and Ring Closure” as an example), but also to infer naturally occurring subsystems of (bio-)chemical systems. We will exemplify the latter by analysing the carbon traces within the TCA cycle and infer subsystems based on projections of the right Cayley graph onto a set of relevant atoms.</p