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Journal of Artificial General Intelligence

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Causal Mathematical Logic as a guiding framework for the prediction of “Intelligence Signals” in brain simulations

Felix Lanzalaco / Sergio Pissanetzky
Published Online: 2014-04-25 | DOI: https://doi.org/10.2478/jagi-2013-0006


A recent theory of physical information based on the fundamental principles of causality and thermodynamics has proposed that a large number of observable life and intelligence signals can be described in terms of the Causal Mathematical Logic (CML), which is proposed to encode the natural principles of intelligence across any physical domain and substrate. We attempt to expound the current definition of CML, the “Action functional” as a theory in terms of its ability to possess a superior explanatory power for the current neuroscientific data we use to measure the mammalian brains “intelligence” processes at its most general biophysical level. Brain simulation projects define their success partly in terms of the emergence of “non-explicitly programmed” complex biophysical signals such as self-oscillation and spreading cortical waves. Here we propose to extend the causal theory to predict and guide the understanding of these more complex emergent “intelligence Signals”. To achieve this we review whether causal logic is consistent with, can explain and predict the function of complete perceptual processes associated with intelligence. Primarily those are defined as the range of Event Related Potentials (ERP) which include their primary subcomponents; Event Related Desynchronization (ERD) and Event Related Synchronization (ERS). This approach is aiming for a universal and predictive logic for neurosimulation and AGi. The result of this investigation has produced a general “Information Engine” model from translation of the ERD and ERS. The CML algorithm run in terms of action cost predicts ERP signal contents and is consistent with the fundamental laws of thermodynamics. A working substrate independent natural information logic would be a major asset. An information theory consistent with fundamental physics can be an AGi. It can also operate within genetic information space and provides a roadmap to understand the live biophysical operation of the phenotype

Keywords: Causal Mathematical logic; Whole brain emulation; Brain simulation; Artificial General Intelligence; biological replication


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  • Glossary of terms: Words in italics refer to other glossary terms Alpha: The 8-13 hz EEG oscillations primarily associated with the thalamus activity Alpha-beta. The range of oscillations in the alpha to beta range, 8-30 hz. Used to assist with the classification of Mu (8-30hz) in terms of mu-alpha (8-13hz) and mu-beta (12-30hz).Google Scholar

  • AMPA/KAINATE: One of the two primary classes of cortical neurons, these are the excitatory neurons. Astrocytes: Cortical Glial cells (support cells that outnumber neurons) that are primarily the most dense at the uppermost cortical layers. Previously considered just support cells, newer research suggest a role in computation function. Maybe gain capacitors for cortical columns.Google Scholar

  • Autobiographical processing: The brain processes which string together episodes into a sequence, that may for example tell a story.Google Scholar

  • Basal ganglia: A subcortical structure of various linked parts associated with a variety of functions, including voluntary motor control, procedural learning relating to routine behaviors or re-enforcement "habits" cognitive, emotional functions and action selection.Google Scholar

  • Beta: The 12-30hz EEG oscillations found in the striatum to cortex activity.Google Scholar

  • Central pacemaker: Areas in the centre of the brain which are thought to be the source clock for a wider range of neural oscillations.Google Scholar

  • Cingulate: Is the part of the cortex which wrap around the limbic system and is so highly integrated with it, and separate from the corpus callosum some propose it is part of the limbic system. Its function seems to be to integrate and resolve conflictions between cortex and limbic system.Google Scholar

  • Cortical surface gyration: The folds of the cortex. As neuron to glia ratio increases the gyrations increase. Increased gyration is thought to be linked to intelligence.Google Scholar

  • Corticolimbic: A reference to the cortex and limbic system in terms of its integrated function.Google Scholar

  • Delta: The 0-4hz EEG oscillation. Primarily subcortical, but also found in the cortex to thalamus cycle.Google Scholar

  • Dynamic attention allocation : Frontal cortex, executive control can suppress various signals in other areas of the brain to allow focus on other stimulus, which could originate as Spreading waves arising from lateral inhibition.Google Scholar

  • Dynamic Causal Modeling: The aim of dynamic causal modeling (DCM) is to infer the causal architecture of coupled or distributed dynamical neural systems. It is a Bayesian model comparison procedure that rests on comparing models of how data were generated Episodic encoding and recall: Episodes are memories which are a set of associations that cover many sensory modalities. e.g. The experience of being in a particular location with somebody.Google Scholar

  • Event Related Potential, ERP : is the brains EKG equivalent for the heart. It is where we look when we measure the brains perceptual operation at its highest and most integrated level.Google Scholar

  • Event related Desynchronization (ERD): This is the lowering of the brains ongoing stationary resting state energy. Mostly found as cortical lateral inhibition.Google Scholar

  • GABAA/GABAC : One of the two primary classes of cortical neurons, these are the inhibitory neurons. Gamma rhythm: The 30hz upwards oscillations generated in the cortex. Tend towards decoherence.Google Scholar

  • Glutamate: The primary excitatory neurotransmitter in the cortex.Google Scholar

  • Globus pallidus: The globus pallidus is a major component of the basal ganglia core along with the striatum “Greedy growth” principle: A mathematical term now used for neurons to describe each neurons dendrite growth in terms of a mathematical power law Grey to white matter ratios: (or the inverse). The ratio of grey to white matter either in individual brain areas, modules or the entire system.Google Scholar

  • Hippocampus: Primary roles is the consolidation of information from short-term memory to long-term memory, recall and spatial navigation.Google Scholar

  • Hippocampal theta: The 4-8hz EEG oscillations primarily associated with hippocampal activity.Google Scholar

  • Hopf bifurcation: A local bifurcation in which a fixed point of a dynamical system loses stability as a pair of complex conjugate eigenvalues of the linearization around the fixed point cross the imaginary axis of the complex plane.Google Scholar

  • Invariants: Both mathematical and computational neuroscience invariants have roughly the same general equivalence.Google Scholar

  • Lateral inhibition : an excited neuron or set of neurons such as cortical columns can reduce the activity of its neighbours. This can even extend to transcallosal inhibition across the two cortical hemispheres.Google Scholar

  • Limbic system. The set of subcortical structures which are separate from the brainstem and cerebellum.Google Scholar

  • Mu wave: EEG oscillation in the range 8-30 hz. It mirrors the alpha to beta range but has no known subcortical source, so maybe a cortical model of the alpha-beta range.Google Scholar

  • N1-P1 signal: An ERP composite signal associated with visual recognition in the time range of 100ms. There are also other types of N signal.. N just means the signal has a negative energy deviation to baseline, while P is positive. 1 is just an abbreviation so it could be called N100 on its own, just as P3 would also be P300Google Scholar

  • Non linear cortical activity: A reference to the manner in which most cortical activity is considered to be primarily non linear dynamical in nature.Google Scholar

  • Non explicit programming: For example programming all the neuronal components into a brain simulation wiring them together and seeing if ERS arises without programming that. But there are various levels of this. i.e. CML is first principle based, so if ERS/ERD arose from its program this would be considered a more pure example. P100-P300 or P600 etc: These are the various positive event related potentials with their onset time (see N1-P1 signal for more clarification) P300: Most well known ERP component occurring at 300ms as it is clearest in decision making and onset of conscious access. All ERP numbers describe the milliseconds of their onset after stimulus Phase coupling - coupled oscillation: Neural oscillations of various frequencies in the brain are found to lock together for a wide variety of neural sub processes, coding, recall and high level perceptual functions.Google Scholar

  • Phase-locked delta: the timing of spikes in the delta wave becomes phase coupled to the activity of other oscillations, such as alpha.Google Scholar

  • Phase reset: Neural oscillations may reset another set of neural activities, such as one set of oscillations, restarting the phase of another when coupled.Google Scholar

  • Re-enforcement learning: The brain processes associated with learning and repeating routines and habits.Google Scholar

  • Sensory routing: Allocation of inputs to outputs, mostly associated with thalamus function.Google Scholar

  • Septal areas: the region of the cerebral hemisphere, forming the medial wall of the lateral ventricle's frontal horn Spreading waves: There are many terms for this in neurodynamics, but basically it is mass attractor activity that is non linear dynamic in nature. Sporadic bursts across cortical areas that spread across columns, or even larger macroscopic areas.Google Scholar

  • Stationary dynamics - stationary resting state. The brain has continuous oscillations such as alpha, delta, beta and mu even when it is not processing information. i.e. just sitting doing nothing.Google Scholar

  • Striatum: The major input station of the basal ganglia system.PubMedGoogle Scholar

  • Subthalamic nucleus: The subthalamic nucleus is a small lens-shaped nucleus in the brain where it is, from a functional point of view, part of the basal ganglia system Symmetry: For disambiguation in cross discipline use in this paper, mathematical symmetry and asymmetry are different from biological e.g. radial symmetry or bilateral asymmetry. This is clarified as here we attempt to use CML to predict brain signals and structures that are using the biological definitions. So if we mention such terms in a neuroscience context it will not be mathematical. An example is we may say an alpha wave emerges with symmetrical spread over a symmetrical structure like the thalamus, but if we were to describe the computations the use of the term symmetry would not automatically infer a mathematical invariance.Google Scholar

  • System consolidation: The ability of the brain to filter out irrelevant information over longer term time periods. Thalamic reticular nucleus: A sheet of inhibitory neurons which surround the thalamus.Google Scholar

  • Thalamus: The brain module where nearly all incoming information passes through.Google Scholar

  • Thalamocortical: Refers to the interaction between thalamus and cortex which is usually an ongoing loop driven by locked Delta, Gamma and Alpha oscillations.Google Scholar

  • Third ventricle: The brain ventricle most central to the brain, in between the thalamus. Google Scholar

About the article

Received: 2013-08-01

Accepted: 2013-11-17

Published Online: 2014-04-25

Published in Print: 2013-12-01

Citation Information: Journal of Artificial General Intelligence, Volume 4, Issue 3, Pages 44–88, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/jagi-2013-0006.

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© by Felix Lanzalaco. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. BY-NC-ND 3.0

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