[Fis] new year lecture

[Pedro C. Marijuan]

Dear Jose Luis and Ramon,

Many thanks for the elegant text. The general view provided looks 
plausible and as you say it seems to dovetail with other main approaches 
to brain integration, perhaps a little bit more realistic (some critics 
of Tononi's "phi" have argued that a mobile phone's circuitry has a 
higher metric of integrated information, thus conscious activity, that 
the conscious brain itself). Anyhow, some of my concerns with the text 
below would relate with:

1. The entropy concept presented. It appears as a measurement (log) of 
the possible configurations of the "connected" (relative synchrony) 
networks. Given that it is obtained from EEG or MEG recordings it 
displays an evident objectivity, but given all the theoretical weight 
that later on incorporates, do you think it has sufficient generativity 
or relevance to influence (to capture?) the ongoing brain dynamics? The 
subsequent complexity metrics JLZC would appear a little more potent or 
realistic on that regard. If my interpretation is not too wrong, they 
would respectively mean the possible combinatorics of info channels, and 
the actual flows between them. For my taste, this seems  to be a form of 
"neural entropy" to clearly distinguish from physical entropy (indicated 
for the non-physicists, like me, otherwise we easily incur into trouble).

2. Along that scheme, a working brain listening to its sensory 
affordances would experiment then a moderate entropy/complexity increase 
(isn't it?). Further if the inner processes ring some alarm, that 
entropy would escalate enormously. But later problem-solving mechanisms 
could efficiently decrease that entropy, if successful. Would you agree 
that behavioral problem solving could somehow be put in abstract terms 
of entropy/complexity management? But subsequently establishing a 
variational principle (Friston, Sengupta) could be tricky, for as you 
point out, the brain does not blindly maximize: it "optimizes."

3. The subconscious. It appeared in the previous discussion session (on 
narratives). Do you think the the brain rest activation (default mode 
network) could be considered as a more reliable referent when talking 
about the subconscious mechanisms of creativity, feelings, etc? All the 
brain areas relatively silent in the left side of your figure, when 
transiently connected with some portion of the central cluster of the 
conscious space, could not bring that stroke of creativity, geniality, 
etc.?

I will appreciate your responses on these crude reflections/comments.

Best wishes and Happy New Year to all FISers!
--Pedro




   El 04/01/2019 a las 14:40, jose luis perez velazquez escribió:
>
> *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 isto 
> 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.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 ourfindings 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.
>
>
>
>
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> http://listas.unizar.es/cgi-bin/mailman/listinfo/fis


-- 
-------------------------------------------------
Pedro C. Marijuán
Grupo de Bioinformación / Bioinformation Group

pcmarijuan.iacs at aragon.es
http://sites.google.com/site/pedrocmarijuan/
-------------------------------------------------



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