[Fis] new year lecture

[Francesco Rizzo]

Caro J. L. Perez Velazquez,
grazie per questa problematica e specialistica introduzione che mi riservo
di meditare meglio nel prosieguo della discussione.
Qui intanto, in modo schematico, mi permetto da economista di fare le
seguenti considerazioni.
Bisogna considerare e distinguere:
*  coscienza fisio-bio-logica (a), coscienza psicologica (b),
comportamentale correntemente intesa come
 consapevolezza di sé (c), sistema di valori morali ed etici (d), coscienza
sociale o politica (e), etc., tutte comprensibili
 in base ai fondamenti onto-bio-logici della conoscenza;
* entropia o informazione in senso statistico-matematico quale misura
dell'equiprobabilità di una distribuzione statistica
uniforme alla fonte (a), termodinamica legata all'equilibrio molecolare o
degradazione energetica (b), in funzione della
teoria o pratica della comunicazione richiedente un codice
interpretativo-significativo e significante (c), etc.
Per non parlare:
* dell'informazione della neg-entropia o energia libera di E. Schrodinger,
assunta dall'ambiente per mantenere la vita (a);
* delle strutture dissipative di Ilya Prigogine che creano ordine
(neg-entropico) dal disordine (entropico) mediante fluttuazioni, etc.
Nella Nuova economia che ho elaborato la coscienza e l'entropia hanno un
ruolo fondamentale, ma inquadrabile in un contesto
più ampio sia in senso sostanziale e  formale, concettuale e terminologico
o linguistico.
Non pretendo di avere ragione di niente e sono sempre pronto ad onorare la
ragione degli altri .D'altra parte, senza l'accettazione
reciproca degli uni e degli altri non v'ha amore della conoscenza o
conoscenza dell'amore.
Grazie ancora e auguri.
Francesco

Il giorno ven 4 gen 2019 alle ore 14:41 jose luis perez velazquez <
jlpvjlpv at gmail.com> ha scritto:

>
>
> *Towards a statistical mechanics of cognition: **Consciousness as a
> global property of brain dynamic activity *
>
>
>
> As a new year’s lecture, we present our recent work that seeks general
> principles of the organization of the cellular collective activity in the
> brain associated with conscious awareness. Our purpose is to identify
> features of brain organization that are optimal for sensory processing, and
> that may guide the emergence of cognition and consciousness. We follow the
> thermodynamic approach: find a state functional which reflects the nature
> of the states attained by the system and that is influenced by some
> observables. Considering what is known about how the nervous system
> functions ―and that brain activity is described from EEG, MEG or functional
> neuroimaging as a superposition of dynamics at different time scales― the
> “nature of brain states” consists of patterns of coordinated activity, that
> is, correlations of cellular (neuronal) activity normally measured as
> coherence or synchrony, hence neural synchronization is a fundamental
> observable and constitutes an appropriate metric to characterise nervous
> system dynamics.
>
>  We also follow the classic approach in physics when it comes to
> understanding collective behaviours of systems composed of a myriad of
> units: the assessment of the number of possible configurations, or
> microstates, that the system can adopt.  In our study we focus on the
> collective level of description and assume that coordinated patterns of
> brain activity evolve due to interactions of mesoscopic areas. Thus we use
> several types of brain recordings (intracerebral EEG, scalp EEG and MEG)
> reflecting the mesoscale level to inspect not only superficial cortical
> activity but also that of deeper structures in conscious and unconscious
> states, and calculate the number of “connections” between these areas and
> the associated entropy and complexity.
>
> The methods are detailed in Guevara Erra et al. (2016) and Mateos et al.
> (2017). Suffice to say that we compute a phase synchrony index from two
> brain signals (corresponding to two brain areas) and declare the areas
> “connected” if the index is higher than the average synchrony obtained from
> surrogates, and “disconnected” if the index is lower.  It must be noted
> that, while normally neuroscientists use the words synchrony and
> connectivity as synonymous, in reality phase synchrony analysis reveals
> only a correlation between the phases of the oscillations between two
> signals, and not a real connectivity which depends on several other factors;
> this is an important topic but we have no space to discuss it here (some of
> these consideration are expounded in some chapters in ‘The
> Brain-Behaviour Continuum―The subtle transition between sanity and
> insanity’ (Perez Velazquez and Frantseva, 2011).
>
> Hence, the number of “connected” brain networks is determined from the
> recordings in the distinct states: conscious (awake) and unconscious (sleep
> −slow wave and REM―, coma and epileptic seizures), and the whole collection
> of connected and not connected networks constitutes our macrostate of the
> brain. An entropy value was then computed for the number of possible
> configurations of connected brain networks. The entropy of this
> macrostate is given by the logarithm of the number of combinations. We
> found a surprisingly simple result: normal wakeful states are characterised
> by the greatest number of possible configurations of interactions between
> brain networks, representing highest entropy values. Unconscious states
> have lower number of configurations, that is, lower entropy. Therefore, the
> information content is larger in the network associated to conscious
> states, suggesting that consciousness could be the result of an
> optimization of information processing. This result is not too surprising,
> for, as Shinbrot and Muzio (Nature 410:251-258, 2001) already said,
> Nature chooses states that maximize the number of particle rearrangements
> (in our case it is the rearrangement of connected cell networks).
>
> The following schematic figure summarises the main concept derived from
> the study.
>
>
> [image: image.png]
>
>
>
> The figure represents the proposed general scheme of the relation between
> global brain connectivity and behavioural states. Normal alertness resides
> at the top of the curve representing the number of configurations of
> connections the system can adopt, or the associated entropy. The
> maximisation of the configurations (microstates) provides the variability
> in brain activity needed for normal sensorimotor action. Abnormal, or
> unconscious states like sleep, are located farther from the top, and are
> characterised by either large (e.g. in epileptic seizures) or small number
> of “connected” networks therefore exhibiting lower number of microstates
> (hence lower entropy) that are not optimal for sensorimotor processing.
>
>
>
>
>
> However, the entropy thus computed, as explained in the two aforementioned
> papers, represents a global measure of the organization of brain cell
> ensembles, hence, at the macroscale level. Therefore, next we examined
> activity at the lower level, namely the variability in the connections
> between brain networks; let’s call this the “microscopic” level (although
> we are still working with signals that represent the macroscopic scale, do
> not get confused!). Having found maximal entropy in conscious states, the
> microscopic nature of the configurations of connections was evaluated using
> an adequate complexity measure derived from the Lempel-Ziv complexity, the
> Joint Lempel-Ziv Complexity (JLZC). This method allows for the assessment
> of the variability at short time scales of the configurations of connected
> networks: the establishment and dissolution of “connections” (for details
> of this study, please see Mateos et al., 2017). Higher complexity was found
> in states characterised not only by conscious awareness but also by
> subconscious cognitive processing, such as during sleep stages, where it is
> known there is information processing and not only during REM episodes
> (dreaming) but also during slow wave sleep (Stickgold, 2001). Thus, even
> in moments of global unconsciousness there can be substantial processing,
> which is revealed upon a closer scrutiny at the microscale level, as that
> provided by the JLZC.
>
> The results provide evidence for the notion that ongoing transformations
> of information in the brain are reflected in the variability and
> fluctuations in the functional connections among brain cell ensembles
> (large entropy of the number of possible configurations and concomitant
> large complexity in the variability of the configurations of the
> connections), which manifest in aspects of consciousness. The crucial
> aspect for a healthy brain dynamics then is not to reach maximum number of
> units (neurons or networks) interacting, but rather the largest possible
> number of configurations (allowed by the constraints). As such, the result
> of high global entropy at the macro level and concomitant high JLZC
> supports the global nature of conscious awareness, because even though
> there is high JLZC in some unconscious states, the macroscopic entropy is
> low in these states; therefore, conscious awareness needs high global
> entropy, whereas the high complexity in some unconscious states like sleep
> reflects information processing but does not reach “awareness”. On the
> other hand, we found that in pathological unconscious states like seizures
> or coma both the global entropy and the JLZC are low. In these pathological
> states, unlike during sleep, there is almost no information processing.
>
> The global nature of consciousness is advocated by several theories of
> cognition. In fact, we think our findings encapsulate three main current
> theories of cognition, as discussed in the papers, namely, the Global
> Workspace Theory, the Information Integrated Theory, and the notion of
> metastability of brain states. It is well known that neurophysiological
> recordings of brain activity demonstrate fluctuating patterns of cellular
> interactions, variability that allows for a wide range of states or
> configurations of connections of distributed networks exchanging
> information, that support the flexibility needed to process sensory inputs
> and execute motor actions. Recent years have seen a surge in the study of
> fluctuations in brain coordinated activity, studies that have raised
> conceptual frameworks such as that of metastable dynamics and that have
> motivated interest in the practical application of assessments of nervous
> system variability for clinical purposes. Of course the prominent question
> is how to describe the organising principles of this cellular collective
> activity which allow features associated with consciousness to emerge. This
> is the objective of our work.
>
> In conclusion, and as an extension of previous work [Perez Velazquez,
> 2009] where it was proposed that a general organizing principle of natural
> phenomena is the tendency toward maximal —more probable— distribution of
> energy, we venture that the brain organization optimal for conscious
> awareness will be a manifestation of the tendency towards a widespread
> distribution of energy (or, equivalently, maximal information exchange).
> Whereas we do not directly deal with energy or information in our work, as
> we focus on the number of (micro)states or combinations of connected
> signals derived from specific types of neurophysiological recordings, the
> results obtained are consistent with conscious awareness being associated
> with widespread distribution of “information” among brain cell ensembles.
>
> In summary, these studies represent our preliminary attempt at finding
> organising principles of brain function that will help to guide in a more
> formal sense inquiry into how consciousness arises from the organization of
> matter. The extension of this work that we are now carrying out includes a
> description of the evolution equation of brain dynamics using a
> probabilistic framework incorporating the probabilities of connections
> among brain cell networks. But this is a story for a future talk!  In the
> meantime, buena suerte for the new year we just started… even though I
> don’t really believe in luck but this is another story too, one about
> determinism and stochasticity..
>
>
>
>
>
> *References*
>
> R. Guevara Erra, D. M. Mateos, R. Wennberg, J.L. Perez Velazquez (2016) Statistical
> mechanics of consciousness: Maximization of information content of network
> is associated with conscious awareness. *Physical Review E*, 94, 052402
>
>
>
> D. M. Mateos, R. Wennberg, R. Guevara Erra, J. L. Perez Velazquez (2017)
> Consciousness as a global property of brain dynamic activity. *Physical
> Review E,* 96, 062410
>
>
>
> J.L. Perez Velazquez, M.V. Frantseva (2011). *The Brain-Behaviour
> Continuum ―The subtle transition between sanity and insanity*.  Imperial
> College Press/World Scientific
>   J. L. Perez Velazquez (2009) Finding simplicity in complexity: general
> principles of biological and nonbiological organization. *Journal of
> Biological Physics*, 35, 209-221
>
>
>
> R. Stickgold (2001) Watching the sleeping brain watch us —Sensory
> processing during sleep. *Trends in Neurosciences* 24, 307.
>
>
>
>
> _______________________________________________
> Fis mailing list
> Fis at listas.unizar.es
> http://listas.unizar.es/cgi-bin/mailman/listinfo/fis
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://listas.unizar.es/pipermail/fis/attachments/20190105/e21cb704/attachment.html>