Staff member


Xerxes Arsiwalla

Postdoctoral Researcher
Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS)
xarsiwalla@ibecbarcelona.eu

Staff member publications

Arsiwalla, Xerxes D., Verschure, Paul, (2018). Measuring the complexity of consciousness Frontiers in Neuroscience 12, (424), Article 424

The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.

Keywords: Consciousness in the Clinic, Computational neuroscience, Complexity measures, Clinical Neuroscience, Measures of consciousness


Arsiwalla, Xerxes, Signorelli, Camilo M., Puigbo, Jordi-Ysard, Freire, Ismael, Verschure, P., (2018). Are brains computers, emulators or simulators? Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018 (Lecture Notes in Computer Science) , Springer International Publishing (Paris, France) 10928, 11-15

There has been intense debate on the question of whether the brain is a computer. If so, that challenge is to show that all cognitive processes can be described by algorithms running on a universal Turing machine. By extension that implies consciousness is a computational process. Both Penrose and Searle have vehemently argued against this view, proposing that consciousness is a fundamentally non-computational process. Even proponents of the brain as a computer metaphor such a Dennett agree that the organizational architecture of the brain is unlike any computing system ever conceived, possibly alluding to non-classical computational processes. The latter class of processes veer away from any program that can be encoded by Church’s lambda calculus. In fact, such a program would have to be based on non-classical logic (either semi-classical or quantum). But quantum logic or machines that might implement them typically are not meant for solving the same type of problems that a classical computer solves (nor are they necessarily faster for any given problem). We will argue that machines implementing non-classical logic might be better suited for simulation rather than computation (a la Turing). It is thus reasonable to pit simulation as an alternative to computation and ask whether the brain, rather than computing, is simulating a model of the world in order to make predictions and guide behavior. If so, this suggests a hardware supporting dynamics more akin to a quantum many-body field theory.


Freire, Ismael, Puigbo, J., Arsiwalla, Xerxes, Verschure, Paul, (2018). Modeling the opponent’s action using control-based reinforcement learning Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018 (Lecture Notes in Computer Science) , Springer International Publishing (Paris, France) 10928, 179-186

In this paper, we propose an alternative to model-free reinforcement learning approaches that recently have demonstrated Theory-of-Mind like behaviors. We propose a game theoretic approach to the problem in which pure RL has demonstrated to perform below the standards of human-human interaction. In this context, we propose alternative learning architectures that complement basic RL models with the ability to predict the other’s actions. This architecture is tested in different scenarios where agents equipped with similar or varying capabilities compete in a social game. Our different interaction scenarios suggest that our model-based approaches are especially effective when competing against models of equivalent complexity, in contrast to our previous results with more basic predictive architectures. We conclude that the evolution of mechanisms that allow for the control of other agents provide different kinds of advantages that can become significant when interacting with different kinds of agents. We argue that no single proposed addition to the learning architecture is sufficient to optimize performance in these scenarios, but a combination of the different mechanisms suggested is required to achieve near-optimal performance in any case.


Hindriks, Rikkert, Schmiedt, Joscha, Arsiwalla, Xerxes D., Peter, Alina, Verschure, Paul F. M. J., Fries, Pascal, Schmid, Michael C., Deco, Gustavo, (2017). Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays PLoS ONE 12, (12), e0187490

Planar intra-cortical electrode (Utah) arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD) underlying such recordings, however, requires “inverting” Poisson’s equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs). Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to “invert” a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG) and magnetoencephalographic (MEG) inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task.


Moulin-Frier, C., Puigbò, J.-Y., Arsiwalla, Xerxes D., Martì Sanchez-Fibla, M., Verschure, Paul F. M. J., (2017). Embodied artificial intelligence through distributed adaptive control: An integrated framework 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob 2017) , IEEE (Lisbon, Portugal) , 1-8

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.

Keywords: Cognitive Architectures, Embodied Artificial Intelligence, Evolutionary Arms Race, Unified Theories of Cognition