@ebrown p,
ebrown p wrote:
Quote:If atheist applied the same type of reasoning to quantum physics that they typically apply to the idea of a soul or reincarnation the exploration of physics beyond Newton would come to a screeching halt.
In fact quantum physics seems to point to awareness (The Observer) as the cause behind random events overthrowing determinism much to the chagrin of atheist and theological determinist alike. Most atheist like predetermined behaviorism just as theologians like predetermined design to prove their God concepts. And for the same reason. So that those in power can dominate the sheep who give up their freewill to appease the psychology of "the norm" so as to fit in or to appease the church they belong to that they are obedient so they can get the benefit of the group.
I must object. This is a gross misunderstanding of Physics. Quantum Physics (as is any Physics) is the exact opposite of the Spirituality-- and thus the findings of Quantum Physics have nothing to do with the soul.
Quantum Physics is rigorously tested and supported by quantitative experiment. Any ideas about Quantum Physics that people take seriously as real have been used to make predictions that are tested. Theories that don't match experimental observation are rejected.
There are no experiments supporting the existence of a soul. There is no experimental way to measure a soul. There are no quantitative predictions that can be based on the existence of a soul and then tested. There are no mathematical models defining a soul, or even an empirical definition of the word "soul".
How is physics the exact opposite of spirituality? When you say there is no experimental way to measure a soul couldn't you say the same thing about consciousness? Isn't this what the "hard problem" is in philosophy? As for predictions I am talking about a freewilled entity. Does any one here know the argument about Zombies?
"A philosophical zombie, p-zombie or p-zed is a hypothetical being that is indistinguishable from a normal human being except that it lacks conscious experience, qualia, or sentience. When a zombie is poked with a sharp object, for example, it does not feel any pain. While it behaves exactly as if it does feel pain (it may say "ouch" and recoil from the stimulus, or tell us that it is in intense pain), it does not actually have the experience of pain as a putative 'normal' person does"
Quantum Physics does unlike Newtonian physics bring in the observer as a cause. I am simply claiming that an observer is actually aware and not just an event in the brain.
I am not saying quantum physics proves the souls existence. I am saying that a universe that has been predetermined would not allow for a soul. I am also saying that quantum physics lets us empirically observe what a indeterminate universe would look like.
I have observed that my awareness is there independent of my senses or thoughts. Are you saying if I can not make a mathematical model of something that it doesn't exist? Do you have a mathematical model of consciousness? If could make such a model then computers could have consciousness. What is the empirical definition of consciousness? If consciousness isn't a quantum state why are scientist slowly coming to the conclusion that only a quantum computer would be capable of consciousness if a computer were ever to be expected to pass a Turing test?
Are you really claiming that your brain is generating thoughts that claim to be conscious of being you but you yourself can not be empirically proven to exist? Are you one of these zombies that have consciousness but no aware experience of consciousness? What is your solution to the hard problem in consciousness?
Not only do I believe that the human brain simply generates wave forms that allow awareness to be received by QM (Quantum Modulation) I further believe that the brain is not fully capable of tuning in the station. That consciousness is the brain receiving transmission of the soul but can not reproduce the whole transmission.
O.k again instead of trying to "prove" my point I will simply give you an example of quantum biology in accepted science. (And yes on a side note there is a such thing as quantum modulation in the development of quantum computing as well)
Quantum 'Social' Intelligences Expressed by Microbes
Follow this page
Email this page
Print this page
Give feedback
Dr Kevin Clark
4229 SE Harney Street, Portland, OR 97206-0941, USA
Last updated on 31 March 2010
Introduction
All microbes, from bacteria to protozoa, make decisions throughout their lifespan. They sense, interpret, and react to changing internal homeostatic states and local ambient environments, often staying with the same strategy or switching between alternative strategies of differential fitness to determine, for example, vegetative and reproductive cycles, phenotype, motility, and social-like cell-cell interactions. Successful strategies can increase a cell's viability and/or fecundity and may vary with inherited life-history traits, random or directed mutations and epigenetic modifications, and traditional forms of dual-process nonasscoiative and associative learning and memory. Strategy acquisition, modification, selection, and execution by microbes frequently require the coordinated workings of sensory transduction pathways, gene regulatory networks, membrane and intracellular transport systems, metabolism, and motility and adhesion apparati. The nanoscale physical dimensions of each of these components of a cell's computational machinery indicate microbial decision making operates within quantum mechanical limits even at biologically relevant temperatures and times (cf. Clark, 2010a, b).
Quantum Cell Biology
The superposition, entanglement, interference, and tunneling properties of quantum mechanics allow for improved information storage capacity, processing speeds, and error correction by computational systems (cf. Nielsen & Chuang, 2000). As with information processing by artificial and hybrid technologies, these computational advantages also figure to play a prominent role for the function of live biological systems, including free-living, parasitic, solitary, and colonial microbes. A growing body of peer-reviewed scientific literature disputes the once widely accepted notion that quantum mechanical phenomena exert, at best, trivial influences over bioprocesses (cf. Davies, 2004). Criticisms still tend to focus on the capacity of biological systems to settle in a quantum regime long enough to accomplish quantum computation (cf. Davies, 2004). However, quantum decoherence, the collapse of the Schrödinger wave function into a single classical or macroscopic state due to thermodynamic processes involving a system and its environment, is much less a problem for cellular enzymatic processes reliant on small, thermally-shielded protein reaction sites and/or on local temperature gradients which can drive cellular substrate from decoherent to coherent activity (cf. Davies, 2004). In keeping with these concepts, a number of cellular substrate are already associated with quantum performance characteristics, such as cytoskeletal lattices (Craddock, Beauchemin, & Tuszynnski, 2009; Hameroff, 1994; Matsuno, 2006), photosynthetic protein complexes (Hu et al., 1998; Sener et al., 2005), the citric acid cycle (Matsuno, 2006) and metabolism (Demetrius, 2003), molecular ratchets (Cooper, W.G., 2009; Matsuno, 2006, 2009; McFadden & Al-Khalili, 1999; Patel, 2001), fire-diffuse-fire reaction rates of intracellular messengers (Clark, 2010a, b), molecule folding (Cieplak & Hoang, 2003; Gutin, Abkevich, & Shakhnovich, 1996), and genetic logical switches (Mayo et al., 2006; Mobius et al., 2005; Sudarsan et al., 2006). Quantum effects at both informational and physical degrees of freedom thus seem to appear in every major aspect of cell structure and function, from sensory transduction to gene expression to cellular metabolism to cell motility.
Quantum "?Social' Decision Making by Microbes
Introduction
All microbes, from bacteria to protozoa, make decisions throughout their lifespan. They sense, interpret, and react to changing internal homeostatic states and local ambient environments, often staying with the same strategy or switching between alternative strategies of differential fitness to determine, for example, vegetative and reproductive cycles, phenotype, motility, and social-like cell-cell interactions. Successful strategies can increase a cell's viability and/or fecundity and may vary with inherited life-history traits, random or directed mutations and epigenetic modifications, and traditional forms of dual-process nonasscoiative and associative learning and memory. Strategy acquisition, modification, selection, and execution by microbes frequently require the coordinated workings of sensory transduction pathways, gene regulatory networks, membrane and intracellular transport systems, metabolism, and motility and adhesion apparati. The nanoscale physical dimensions of each of these components of a cell's computational machinery indicate microbial decision making operates within quantum mechanical limits even at biologically relevant temperatures and times (cf. Clark, 2010a, b).
Quantum Cell Biology
The superposition, entanglement, interference, and tunneling properties of quantum mechanics allow for improved information storage capacity, processing speeds, and error correction by computational systems (cf. Nielsen & Chuang, 2000). As with information processing by artificial and hybrid technologies, these computational advantages also figure to play a prominent role for the function of live biological systems, including free-living, parasitic, solitary, and colonial microbes. A growing body of peer-reviewed scientific literature disputes the once widely accepted notion that quantum mechanical phenomena exert, at best, trivial influences over bioprocesses (cf. Davies, 2004). Criticisms still tend to focus on the capacity of biological systems to settle in a quantum regime long enough to accomplish quantum computation (cf. Davies, 2004). However, quantum decoherence, the collapse of the Schrödinger wave function into a single classical or macroscopic state due to thermodynamic processes involving a system and its environment, is much less a problem for cellular enzymatic processes reliant on small, thermally-shielded protein reaction sites and/or on local temperature gradients which can drive cellular substrate from decoherent to coherent activity (cf. Davies, 2004). In keeping with these concepts, a number of cellular substrate are already associated with quantum performance characteristics, such as cytoskeletal lattices (Craddock, Beauchemin, & Tuszynnski, 2009; Hameroff, 1994; Matsuno, 2006), photosynthetic protein complexes (Hu et al., 1998; Sener et al., 2005), the citric acid cycle (Matsuno, 2006) and metabolism (Demetrius, 2003), molecular ratchets (Cooper, W.G., 2009; Matsuno, 2006, 2009; McFadden & Al-Khalili, 1999; Patel, 2001), fire-diffuse-fire reaction rates of intracellular messengers (Clark, 2010a, b), molecule folding (Cieplak & Hoang, 2003; Gutin, Abkevich, & Shakhnovich, 1996), and genetic logical switches (Mayo et al., 2006; Mobius et al., 2005; Sudarsan et al., 2006). Quantum effects at both informational and physical degrees of freedom thus seem to appear in every major aspect of cell structure and function, from sensory transduction to gene expression to cellular metabolism to cell motility.
Quantum "?Social' Decision Making by Microbes
The above empirical and theoretical biophysics observations hint quantum mechanical behavior of intracellular molecules and biochemical pathways might cause measurable effects at the scale of intact cells performing decisional tasks which modify cellular fitness in unstable environments. Microbes may act alone or in concert with other microbes and organisms to overcome ecological barriers to survival and reproduction. In many respects, the cooperative and competitive "?social' behaviors exhibited by microbes, such as assisted reproduction, altruistic suicide, reproductive cheating, quorum sensing, kinship recognition, induced defenses, and foraging (cf. Crespi, 2001), resemble rudimentary animal "?social' intelligences. However, questions remain about how well microbes meet criteria for associatively learned performance, determined over one hundred years ago from animal research as a common trait of intelligence (cf. Romanes, 1884; Thorndike, 1911). Noted faults with the scientific method of early to mid-twentieth century microbiology research usually confound any strong conclusions for or against the expression of associative microbial intelligences (cf. Applewhite, 1979; Clark, 2010a; Corning & Von Burg, 1973). Newer, better controlled techniques and paradigms indicate microbes are good learners capable of at least comparatively simpler decision making scaled to learning sophistication (Armus, Montgomery, & Gurney, 2006; Clark, 2010a, b; Hennessey, Rucker, & McDiarmid, 1979; Hellingwerf et al., 1995; Nakagaki, Yamada, & Troth, 2000; Wolf et al., 2008). Contractile ciliates, for instance, have been recently shown to utilize learned quantum processing to increase the efficiency of heuristic-guided behavioral strategy searches, planning, and execution when deceptively or altruistically signaling mating availability and prowess in simulated "?social' contexts (Clark, 2010a, b). Such practiced "?courting' reciprocation encourages presumptive mates and deters presumptive rivals, importantly suggesting primitive intra- and intermate selection emerged along evolutionary branches near and perhaps well before the animal-fungal divergence.
The evolutionary significance of this phylogenetically early mate selection is all the more impressive when considering signaling strategies are stored by ciliates into categorical groups or modules of disparate fitness content (Clark, 2010a). Ciliates form and access these groups with recursive, Hebbian-like learned heuristics previously thought to be only used by animals capable of associative learning, memory, and logics. The aneural basis of "?binding' together different "?social' information into behavioral strategies and heuristics putatively involves, as it does in animal nervous systems, classical and instrumental conditioning phenomena not yet wholly elucidated for prokaryotes and "?lower' eukaryotes (cf. Clark, 2010a, b). Nonetheless, the plastic, topologically invariant network features of behavioral heuristics enable ciliates to manipulate the combinatorial and computational complexity of encoding, storing, and retrieving information while being able to advertise contrasting levels of mating fitness to conspecifics. This is perhaps best illustrated by the speed with which some ciliates search for appropriate signaling strategies from among their entire behavioral repertoire. Ciliates most competent at flexible goal-directed cooperative or competitive "?courting' exchanges learn to improve their expertise in finding "?target' strategies until they often reach quantum efficiency. That is, the number of computational steps or operations taken by these ciliates to find a "?courting' solution is reduced by the square-root of the total number of search elements or strategies stored in an evolving heuristic. Known as the square-root algorithm or Grover's quantum search algorithm (Grover, 1996), this type of quantum computation shows quadratic acceleration over classical processes. Quantum search efficiencies exploiting the quantum superposition of mutational genetic states have been reported for DNA polymerases that sample and sort nucleotide base pairs during DNA replication and proof reading (Cooper, 2009; McFadden & Al-Khalili, 1999; Patel, 2001). But the discovery that a fast search algorithm, such as Grover's quantum search algorithm, is also implemented by an intact life form performing cognitive-like computations establishes a new standard for skillful decision making at any systematics level. Its appearance in ciliate information processing and storage specifically implies quantum changes in the reaction kinetics of chemical processes underlying optimal "?behavioral' planning and execution can be learned by microbes coping with universal ecological dilemmas.
Quantum-efficient strategy searches, planning, and execution permit a ciliate to out-compete mating rivals when perceiving and learning another's fitness compatibility as well as when projecting its own reproductive status. Learning to optimize the cellular bioenergetics of "?courtship' rituals lowers metabolic costs and tunes sparser structural resources. Similar to adaptive complex technological networks (e.g., Bianconi & Barabási, 2001), ciliates and probably other microbes obtain solutions to this kind of homeodynamic dilemma by controlling the timing of classical and quantum computational phases (Clark, 2010b). As previously mentioned, some ciliates try to maximize their "?courtship' opportunities by learning to switch between behavioral strategies that either altruistically or deceptively signal different levels of mating availability over brief periods (Clark, 2010a, b). This process is energetically expensive and is governed by a classical Maxwell-Boltzmann computational phase, where weighted strategy choices are distributed across different energy levels in thermal equilibrium unfavorable for quantum effects. Later Hebbian-like trial-and-error learning eventually anneals these ciliates' signaling performances into energetic ground states described by quantum Bose-Einstein statistics and the selection or condensation of a single, fittest behavioral strategy. The sequence of ciliate "?social' decision making from Maxwell-Boltzmann to Bose-Einstein computational phases parallels the emergence of fast strategy searches dependent on Hebbian-like learning. An interesting consequence of bioenergetics optimization learned by ciliates is that the annealing threshold for Bose-Einstein condensation should become proportional to the asymptote of associative strength between grouped strategies stored within a behavioral heuristic of ordered computational complexity. Moreover, transition from classical to quantum computations indicates decoherent chemical processes underlying strategy switching drives later choice behavior toward a fittest strategy mediated by lower-temperature coherent chemical processes important for socially valuable computations. This idea is consistent with cells behaving as mechanochemical heat engines, where loss of energy associated with the activity of specific cellular machinery, such as myonemes and axonemes, effectively reduces the temperature of selective biomolecules cohering into quantum superposition (Clark, 2010b; Matsuno, 1999, 2006).
Biomechanisms of Quantum "?Social' Decision Making by Microbes
Each key component of a cell's computational machinery is expected to contribute to the emergence of quantum "?social' decision making by microbes. However, the exact biomechanisms are unknown. One suitable candidate biomechanism involves Ca(2+)-induced Ca(2+) waves initiated by the activation of G-protein-coupled mechanosensitive Ca(2+) channels during preconjugal cell-cell contacts (Clark, 2010a, b). Ciliates, for example, touch each other at different stages in their "?courting' rituals evoked from the detection of secreted sexual pheromones. This mechanical pressure stimulates Ca(2+) entry into a cell from the surrounding aqueous habitat. Increased Ca(2+) concentration autocatically triggers further Ca(2+) release from intracellular stores that form compartmentalized networks throughout the cytosol. For several reasons, networks of intracellular Ca(2+) release sites offer potential substrates for ciliates and perhaps other microbes to store associatively learned "?social' information into memory circuits and to retrieve that same information with fast search algorithms capable of eliciting behavioral strategies (cf. Chen, Levine, & Rappel, 2008, 2009; Clark, 2010a, b, in preparation; De Pittà et al., 2008, 2009; Siso-Nadal et al., 2009). First, the spread of intracellular Ca(2+) waves generates modifiable motility used for "?courtship dances'. Second, phosphatase- and protein-kinase-dependent recurrent feedback, like Hebbian learning, inhibits or enhances Ca(2+) entry to future stimulation by changing the permeability of mechanoreceptors during behavioral learning. Third, subsets of Ca(2+) stores may "?bind' themselves together and are capable of computational selectivity and associative-like crosstalk with other response-regulator systems via their physical proximity to "?sensorimotor' systems and their tendencies to modulate the duration and concentration of Ca(2+) mobilization. Fourth, entrained synchronous activity between sites discharging local Ca(2+) sparks can increase the conduction velocity of Ca(2+) waves to a level supporting fast search algorithms that preferentially select over-learned, stored behavioral sequences. Such events likely occur as the kinetics of Ca(2+)-induced Ca(2+) reactions become dominated by rate-limiting chemical processes, leading to transitions from slow saltatory to fast continuous Ca(2+) wave fronts. The conduction velocity of saltatory Ca(2+) waves is proportional to the calcium diffusion coefficient and the conduction velocity of continuous Ca(2+) waves is proportional to the square root of the calcium diffusion coefficient (Ponce-Dawson, Keizer, & Pearson, 1999). The difference in the speed of propagation between saltatory and continuous Ca(2+) waves accounts for the root-rate increase in processing speed expected from Grover's quantum search algorithm over classical algorithms. Just such a change in wave velocity, and therefore the sensory information carried by Ca(2+) waves, explains how behavioral strategy search efficiencies improve to quantum levels along with a ciliate's expertise. Associative learning may also pare the number and distribution of activated Ca(2+) release sites to enhance metabolic and information processing efficiency to some critical threshold, such as that defined by optimal annealing and associative strength, where one strategy becomes favored over others to form network analogues of Bose-Einstein condensation. This later process may be mediated by intracellular temperature gradients that force "?long-range' coherence in enzymatic activity to unify cell processes into a single learned state.
Concluding Remarks
Microbes learn quantum processing to optimize decision making and shared fitness evolving under continued selective pressures. The emergence of this phenomenon appears to be dependent on Ca(2+) homeodynamics and autocatalytic reaction kinetics. The ubiquity of Ca(2+) as a response regulator of cell functions for all types of cellular life therefore suggests these findings have enormous value for a range of research topics, including host-parasite interactions, NMDA-dependent plasticity, smart biomachines, nervous system repair, evolution of social behaviors and intelligences, and onset of developmental transitions.
continue reading