Design of the Artificial: lessons from the biological roots of general intelligence

Our desire and fascination with intelligent machines dates back to the antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought (syllogism) and Heron of Alexandria's mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence

Considering Hilbert's Approach: Problems from a Cell's Perspective

Reducing the events in cellular systems and examining disconnected components has proven to be successful in uncovering molecular reactions and interactions. However, Ad-hoc and subsequent theory-less composition of the discrete bits has failed to create a system-scale map due to the missing links: unknown variables and the ensuing nonlinearities in the complex high-dimensional parameter space, not taking into account the natural noisiness and stochasticity of the cellular events, ignoring causal influence and disregarding the temporal dynamics. Composite static and phenomenological descriptions, as complicated as they may look, lack the essence of what makes the biological systems ``complex''. Formalization of system-level problems is key in the path towards constructing a metatheory of biology. As a template for such formalization, this wotk aims to tease apart a few problems that cells need to resolve. This approach may serve as a model in the path towards axiomatizing biological investigations

Ensemble Inhibition and Excitation in the Human Cortex: an Ising Model Analysis with Uncertainties

The pairwise maximum entropy model, also known as the Ising model, has been widely used to analyze the collective activity of neurons. However, controversy persists in the literature about seemingly inconsistent findings, whose significance is unclear due to lack of reliable error estimates. We therefore develop a method for accurately estimating parameter uncertainty based on random walks in parameter space using adaptive Markov Chain Monte Carlo after the convergence of the main optimization algorithm. We apply our method to the spiking patterns of excitatory and inhibitory neurons recorded with multielectrode arrays in the human temporal cortex during the wake-sleep cycle. Our analysis shows that the Ising model captures neuronal collective behavior much better than the independent model during wakefulness, light sleep, and deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled; ignoring the inhibitory effects of I-neurons dramatically overestimates synchrony among E-neurons. Furthermore, information-theoretic measures reveal that the Ising model explains about 80%-95% of the correlations, depending on sleep state and neuron type. Thermodynamic measures show signatures of criticality, although we take this with a grain of salt as it may be merely a reflection of long-range neural correlations.Comment: 17 pages, 8 figure

Theoretical Principles of Multiscale Spatiotemporal Control of Neuronal Networks: A Complex Systems Perspective

Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules

Physical computation and compositionality

Developments in quantum computing and, more in general, non-standard computing systems, represent a clear indication that the very notion of what a physical computing device is and does should be recast in a rigorous and sound framework. Physical computing has opened a whole stream of new research aimed to understand and control how information is processed by several types of physical devices. Therefore, classical definitions and entire frameworks need to be adapted in order to fit a broader notion of what physical computing systems really are. Recent studies have proposed a formalism that can be used to carve out a more proper notion of physical computing. In this paper we present a framework which capture such results in a very natural way via some basic constructions in Category Theory. Furthermore, we show that, within our framework, the compositional nature of physical computing systems is naturally formalized, and that it can be organized in coherent structures by the means of their relational nature

Causal Unit of Rotors in a Cardiac System

The heart exhibits complex systems behaviors during atrial fibrillation (AF), where the macroscopic collective behavior of the heart causes the microscopic behavior. However, the relationship between the downward causation and scale is nonlinear. We describe rotors in multiple spatiotemporal scales by generating a renormalization group from a numerical model of cardiac excitation, and evaluate the causal architecture of the system by quantifying causal emergence. Causal emergence is an information-theoretic metric that quantifies emergence or reduction between microscopic and macroscopic behaviors of a system by evaluating effective information at each spatiotemporal scale. We find that there is a spatiotemporal scale at which effective information peaks in the cardiac system with rotors. There is a positive correlation between the number of rotors and causal emergence up to the scale of peak causation. In conclusion, one can coarse-grain the cardiac system with rotors to identify a macroscopic scale at which the causal power reaches the maximum. This scale of peak causation should correspond to that of the AF driver, where networks of cardiomyocytes serve as the causal units. Those causal units, if identified, can be reasonable therapeutic targets of clinical intervention to cure AF.Comment: 19 pages, 9 figures. arXiv admin note: text overlap with arXiv:1711.1012

Florid Cemento-Osseous Dysplasia at the Site of Previous Teeth Extraction: Report of a Case

Objective: Florid cemento-osseous dysplasia (FCOD) is a rare bone lesion that predominantly involves the women’s jaws in middle age. This condition is usually asymptomatic and has a benign course.Case: This paper presents a rare case of FCOD in a white middle aged woman, which had affected mandible bilaterally and was diagnosed after tooth extraction and treated conservatively.We believed tooth extraction was a contributing factor for outbreak of such a lesion in this susceptible patient.Conclusion: For the asymptomatic patients, the best management consists of regular recall examinations with prophylaxis and reinforcement of oral hygiene to prevent periodontal diseases and tooth loss, but with accession of clinical signs and symptoms, surgical intervention is inevitable

Avalanche analysis from multi-electrode ensemble recordings in cat, monkey and human cerebral cortex during wakefulness and sleep

Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been observed in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different in vivo preparations during wakefulness, slow-wave sleep and REM sleep, using high-density electrode arrays in cat motor cortex (96 electrodes), monkey motor cortex and premotor cortex and human temporal cortex (96 electrodes) in epileptic patients. In neuronal avalanches defined from units (up to 160 single units), the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs) and in particular LFP negative peaks (nLFPs) among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and pre-motor cortices). In this case, the avalanches defined from nLFPs displayed power-law scaling in double log representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP) peaks, which are less directly related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using more reliable cumulative distribution functions (CDF) and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. Multiple exponential fitting yielded optimal fits of the avalanche dynamics with bi-exponential distributions. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man.Comment: In press in: Frontiers in Physiology, 2012, special issue "Critical Brain Dynamics" (Edited by He BY, Daffertshofer A, Boonstra TW); 33 pages, 13 figures. 3 table