Volume 14

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1951

Volume 14

3232+03 3232+04

Information in machines Markov process / chain affecting a machine Information in machines Information ways of loosing Markov process / chain affecting a machine Summary: Information when a stochastic parameter changes infrequently. 3233 3234

Information in machines Summary: Ways of losing information. 3274 Information in machines 3235 3236

Information in machines DAMS (Dispersive and Multistable System) [13]: Possible patterns for joining output and inputs, ●, ●. Summary: Wiring pattern of DAMS. Information in machines Resting state maximal number 3237 3238

Information in machines Information in machines Summary: Conditions that a machine shall have the maximal number of resting states. This can be specified further... 3239 3240

Information in machines Summary: Maximal number of resting states. (3308) Information in machines Summary: Information when A drives B. 3241 3242

Absolute system why not x=f-1 (x')? Canonical equations why not [x=f-1 (x')]? Information and canonical equations Information in machines Experiment when it stops Information and experiment Information in machines Summary: The inverse of the canonical equations. 3243 3244

Information and experiment Information in machines Epistemology [5]: When does an experiment stop? ●. Summary: An experiment stops when the exchange of information has reached equilibrium. (3248, 3254, 3691) Independence and information Information and independence Information in machines 3245 3246

Information in machines Summary: Independence does not in general cause loss of information. (3274) Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3247 3248

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Summary: Entropies in the parts do not sum to that of the whole. Entropy of a part may equal that of the whole. Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Summary: Information and experiment. 3249 3250

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3251 3252

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Summary: Information in an absolute machine. [deleted] Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3253 3254

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3255 3256

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3257 3258

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3259 3260

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3261 3262

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Summary: Information and the experimenting on dynamic systems. Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3263 3264

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Summary: This then is the maximal information obtainable in an absolute system of σ states by starting it at a state selected arbitrarily and then observing how it's behaviour goes from state to state. 3265 3266

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } 3267 3268

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Summary: Information always decreases, step by step, as an unknown line of behaviour unfolds. 3269 3270

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { ●●●●●●●●●●●●●●●●●●●●●●●● } Summary: Uncertainty about the details within a line of behaviour is independent of whether that line, or some other, will occur. 3274 Latent roots distribution of 3271 3272

Information and machines, collected 3273 3274

Markov process / chain information from Parameter as source of information Resting state and information Transition probability and information 3275 3276

3277 3278

Personal notes [14]: New wiring avoids feedback within the 'cortical' part, ●. Summary: Results collected from the last hundred pages (since 3164) on the subject 'Information in absolute systems.' 3297 3500 Summary: New layout for DAMS, and an unsolved problem. Summary: I have just reviewed the notes on the pages mentioned (2955, 2996, 3001, 3003, 3014, 3026, 3028, 3056, 3059, 3071, 3082, 3087, 3115, 3138, 3140, 3149 and the previous note), all dealing with the relations between environment, essential variables, and the 'red mass' of 2957, once the essential variables have been specialised and separated. Here are my conclusions... 3279 3280

Environment REVIEW Essential variables REVIEW OF Multistable system REVIEWED DAMS (Dispersive and Multistable System) [94]: Review of relations between multistable system, environment, and specialised essential variables, ●. 3281 3282

Amplitude limitation postulated in some systems 3283 3284

Arc in 'cortex' of DAMS (Dispersive and Multistable System) 3285 3286

3287 3288

Summary: A review of essential variables. (3484, 3521) 3289 3290

The Multistable System [42]: Behaviour of systems having a state from which the transition probability is very small ●. 3291 3292

The Multistable System [18]: Length of line of behaviour and chance that a step function will change, ●. Summary: Equations of density in phase of systems that tend to stick at certain states. 4153 3293 3294

3295 3296

Information when only part is observed Summary: The longer the line of behaviour, the higher the chance of step-function change. 3297 3298

3299 3300

Information in a continuous system Summary: Information in machines. 3301 3302

Information in a sub-system Summary: The continuous system can gain information though absolute. Summary: As soon as a sub-system is isolated it starts losing information. Personal notes [13]: Discussion with Wiener, May '51. ●. 3303 3304

Dispersion examples Constancy and network Network cut up by constancies Part-function in network 3305 3306

Summary: Dispersion. Resting state number of 3307 3308

3309 3310

Summary: How many resting states has DAMS? (Continued 3319) 3311 3312

Summary: Resting states in DAMS will be few. (Continued 3319) 3313 3314

Part-function approximate Summary: Pneumatic controllers. 3315 3316

Constant intrinsic stability properties Summary: Measuring how much one variable affect another. The part-function as a limit. 3317 3318

Resting state maximal number 3319 3320

3321 3322

Constant intrinsic stability and resting states 3323 3324

Summary: Designing parts for a system with many resting states. (3333) 3325 3326

3327 3328

3329 3330

3331 3332

Summary: How to find the distribution of values in a system of many parts. 3333 3334

3335 3336

3337 3338

3339 3340

3341 3342

3343 3344

Summary: (1) I ignore resting cycles here, as they will probably be rare. (2) I treat only of parts of constant intrinsic stability with equations of form x-i=Ki{Φi(x1,...,*,...,xn)-xi)}. (3) The variables in the parentheses (above) are the 'inputs' to the part, and Φi is the 'output'. (xi merely follows Φi). (3323) (4) Just solving f(X)=0 is of little use, for an unknown, and large, number of roots may be complex. The total number of roots, real and complex, is the product of the degrees of the several f's regarded as polynomials (3308) (5) To get the real distinct resting states, find geometrically the real intersections of the surfaces, f(X)=0. (6) Figure of 3322 shows that, if we want to get our resting states into a certain region of phase-space then the surfaces must waggle within it, and also across it. (3325 top). 3345 3346

Summary: (Continued) (7) If Φi tends to a form resembling ρi parallel planes (3334), then the number of resting states (stable and unstable) tends to Πρi. (3336) (8) (3340: a method, of little importance, for getting the sets of planes all orthogonal. (9) (3342: what happens when all parts are identical. (Not the case with DAMS) (10) To get the maximal number of resting states within a given region: (a) construct each part so that the output consists of many parallel planes, (b) join them so that the sets of planes are orthogonal. (12) If the number of resting states is increased, we can expect the number of stable states to be increased in about the same proportion. (3345) DAMS (Dispersive and Multistable System) [20]: Why DAMS was changed to give more resting states, ●. Summary: How many resting states has a system assembled from parts of known properties? Also 3496 3347 3348

3349 3350

Summary: Modifying a stable field. Stimulus measuring its size 3351 3352

Summary: Ways of altering a system's sensitiveness to disturbance. Equilibrium and coupling Joining and state of equilibrium Summary: Effect of richness of joining on the number of stable resting states. 3353 3354

Arc needs no feedback Cortex needs no feedback Feedback not in cortex Summary: The nervous system should not have internal feedbacks (unless for special reasons) But see 3396. Confirm 3425, 3521 3355 3356

Environment must be able to carry sufficient information 3357 3358

Summary: Information and adaptation. 3521 Personal notes [12]: Idea for new book: the building of DAMS, ●. Summary: How many nerve calls has an earthworm? 3359 3360

Input control possible Parameter control by 3361 3362

Hover mouse here to display note 3363 3363+01

Summary: Conditions that one system may control another in detail. 3364 3365

Constant intrinsic stability useful only for static questions Ergodism DAMS (Dispersive and Multistable System) is not ergodic Summary: Control in systems of Constant Intrinsic Stability. The Conditioned Reflex [39]: Anrep's experiment, ●. Summary: My machines are not ergodic. 3366 3367

Entropy calculation of Information calculation of Markov process / chain information from Transducer theory of 3368 3369

3370 3371

3372 3373

3374 3375

Information through a transducer Transducer destroying information Summary: Information going through a transducer. 3376 3377

3379 3380

Summary: Solution of the paradox of 3379. DAMS (Dispersive and Multistable System) [19]: Essential variables should be able to send much information in the rest of the system if they are specialised. ●. Summary: The essential variables must be able to send much information into the rest of the system. 3500 3381 3382

DAMS (Dispersive and Multistable System) [18]: Advantage to the Essential Variables, if specialised, of using noise ●. 3383 3384

Summary: Designing an essential variable that works by emitting noise. 3521. 3385 3386

DAMS (Dispersive and Multistable System) [17]: Environment for DAMS must be more than minimally complex, ●. Summary: DAMS needs a complex environment, but a simple training-schedule. Oddments [33]: My work is the 'chemistry' of machines, ●. 3387 3388

Summary: My work is the 'chemistry' of machines. Progress in it will be largely empirical. Review 4141 3389 3390

DAMS (Dispersive and Multistable System) [16]: For many resting states, the steps functions' behaviours should be almost uncorrelated. ●. Summary: That a set of step-functions should provide many resting states it is necessary that they should be uncorrelated. This can be achieved by many cross-connexions. 3521 3391 3392

Summary: If the number of resting states is increased by some change of design, take care that the number stable is not actually reduced. Equilibrium proportion stable 3393 3394

Summary: Of the resting states, the number stable can be anything from none to all. Feedback destroying information Transducer in a ring 3395 3396

DAMS (Dispersive and Multistable System) [15]: How to retain information in DAMS, ●, ●. Summary: A system joined in a circuit is likely to have very few resting states. Confirmed 3426 but see 3571. Summary: To retain information in DAMS Mark 13, use output 3 and either of 1 or 2 in all cases. 3397 3398

Oddments [30]: Probabilities in continuous statistical systems should always be obtained as limits from finite countable states, ●. Summary: Thinking of the machine as having a finite number of states is the fundamentally sound method. Constant intrinsic stability integration of equations Output integration and output Summary: How to integrate step by step when parts have outputs. 4498 shows how it should be done. 3399 3400

Resting state maximal number Summary: Further data on what is required for many resting states. 3401 3402

Information necessary for resting states Resting state and information Transducer destroying information 3403 3404

Information destruction in machine Summary: A machine's tendency to destroy or conserve information (as uncertainty of state) depends slightly on certain necessary factors in the parts but depends more on the holistic factor of assembly. 3405 3406

Summary: Two similar parts that will give many stable resting states. 3407 3408

DAMS (Dispersive and Multistable System) [14]: Essential variable that will inject noise proportional to its deviation ●. Summary: Design of a part and the number of resting states. 3409 3410

3411 3412

3413 3414

Summary: My standardised vocabulary, collected. (Standard symbols, 2004) DAMS (Dispersive and Multistable System) [21]: In DAMS, the other conditions have no 'usual' values; and this increases the difficulty we have in apprehending it. ●. 3415 3416

Summary: With a new and complex system there are no 'usual' values for the parameters, and this increases the difficulty of getting to know it. 3514 The Multistable System [19]: Law of the Invariance of Distribution ●. The Multistable System [20]: In a multistable system, counting the number of resting states is not practically possible, ●. Summary: Law of the Invariance of Distribution. 3417 3418

Information destroyed by part function Latent roots distribution DAMS (Dispersive and Multistable System) [22]: DAMS must contain its information reduntantly ●. Cortex, sensory layering in DAMS (Dispersive and Multistable System) [23]: DAMS' variables must be arranged in layers at its sensory input, in order not to lose information, ●. 3419 3420

Summary: How to stop part-functions from destroying information. 5291.9 Resting state maximal number DAMS (Dispersive and Multistable System) [24]: For DAMS to have many resting states it must have much independence internally ●. 3422 3423

Feedback not in cortex Independence to pattern DAMS (Dispersive and Multistable System) [25]: Main properties of DAMS at the 20-variable size, ●. Summary: The conclusion is, then, that for many resting states we must have plenty of independence. 3424 3425

Summary: Historical note. Epistemology [7]: Peculiarities in studying a large active system ●. Summary: Complex wholes are unstudiable. 3474, 3496, 3513 3426 3427

DAMS (Dispersive and Multistable System) [15]: How to retain information in DAMS, ●, ●. Summary: Information in DAMS. Hover mouse here to display note 3428 3428+01

Higher geometry of fields and matrix theory [14]: Determinant and latent roots of a matrix formed by adding a diagonal and a 'permutation' matrix, ●, applied ●. Summary: Value of a determinant. 3429 3430

DAMS (Dispersive and Multistable System) [67]: Latent roots of a system arranged in a circle of levels, ●. DIAGRAM 3431 3432

Higher geometry of fields and matrix theory [14]: Determinant and latent roots of a matrix formed by adding a diagonal and a 'permutation' matrix, ●, applied ●. Summary: The latent roots of a system formed as a circular chain of levels. Cf. 3573 3433 3434

ECT (electroconvulsive therapy) explanation Fatigue explanation The Multistable System [21]: Some of the step-functions can be used only once, so the system will tend gradually to lose versatility, ●. 3435 3436

Induction (physiological) explanation of Summary: A multistable system tends to lose reactivity, which will often be restored by applying some strong, but unrelated, stimulus, at the cost of some forgetting. ? Action of E.C.T. (Corollary 3464). ? Explanation of 'induction'. 3656, 4628, 4524. Reynolds' number Step function laminar and turbulent flow The Multistable System [41]: Eddies resemble the subsystems of a multistable system. ●. 3437 3438

DAMS (Dispersive and Multistable System) [26]: Look of for dimensionless numbers in DAMS, ●. DAMS (Dispersive and Multistable System) [27]: Look for large-scale examples of step-function, resting state, absolute system, in DAMS, especially when it is large, ●. Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } 3439 3440

Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } 3441 3442

Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } 3443 3444

Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } 3445 3446

Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } 3447 3448

Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } Quotations [4]: Mataphysics is the finding of bad reasons for what we believe upon instinct, ●. Quotations [3]: Ninety quotations, { ●●●●●●●●●●● } Summary: Ninety quotations. 3449 3450

Natural Selection [33]: In the cortex the relentless necessity for survival may lead to highly specialised properties in the patterns, having only that aim, i.e. useless to the individual, but not, of course, actually harmful, ●. The Multistable System [36]: The subsystems should gradually develop highly specialised means for each others' disruption ●. Summary: In the cortex the relentless necessity for survival may lead to some interesting consequences. (See 3454) (Review, 4155) 3451 3452

Army organisation of Society [37]: Clausewitz on the principles of war ●. The Multistable System [34]: The struggle between subsystems as 'war' ●. 3453 3454

Psychiatric applications [14]: Neurosis resembles two armies struggling, ●, ●. 3455 3456

Summary: The art of war - in the cortex. Review 4155, 4589 Inhibition retroactive Retroactive inhibition The Conditioned Reflex [40]: Neutral stimuli play a part in reactions, ●. 3457 3458

Dispersion control of Multistable reserve origin demonstrated The Multistable System [95]: Dispersion depends on 'neutral' as well as on the active variables, ●. Summary: The animal reacts to all its surroundings. Retroactive inhibition and the theory of interaction in a Multistable System. DAMS (Dispersive and Multistable System) [28]: When DAMS is being demonstrated, let it be increasingly slightly disturbed, ●. Summary: Let DAMS keep moving. 3459 3460

Natural Selection [45]: An objection to be answered, and its answer, ●. Psychiatric applications [14]: Neurosis resembles two armies struggling, ●, ●. Summary: Society. 3461 3462

Summary: How DAMS can be made neurotic. (See next section) (See 3480) 3463 3464

Summary: Neurosis by conflict must use up a system's resources of step-functions. Statistical mechanics nature of DAMS (Dispersive and Multistable System) [29]: DAMS is a 'statistical' machine: what does this mean? Answer, ●. 3465 3466

3467 3468

3469 3470

3471 3472

Summary: The question 'what is a 'statistical' machine?' answered. 3473 3474

Summary: What makes a complex machine 'statistical'? Review 4141 The Multistable System [43]: Probability of an absolute system whose variables are statistics of a much larger system, ●. 3475 3476

DAMS (Dispersive and Multistable System) [30]: DAMS should show absolute systems of few variables, each a statistic from the larger system, ●. Summary: How does a statistical machine work? The Multistable System [22]: The multistable system should react sucessfully to the probabilities in its environment, ●. DAMS (Dispersive and Multistable System) [31]: Experiment to be tried on DAMS, ●. Summary: DAMS should demonstrate that it can manage the statistics of its environment as well as the exact details. 3477 3478

Society [38]: We would expect activities in time to avoiding impinging on amplifiers like the newspapers, ●. The Multistable System [35]: Multistable system will tend to avoid activating variables that ramify widely or magnify much, ●. Summary: DAMS should tend to avoid activating variables with widespread effects. 4155 Psychiatric applications [15]: Nature of neurotic symptoms, ●. 3479 3480

Money reduction of all social variables to Society [39]: Society, and the nervous system, reduce all variables to a common form, ●. Summary: On neurosis. DAMS (Dispersive and Multistable System) [32]: Practical method for DAMS' environment, ●. 3481 3482

Summary: A more practical form of environment for DAMS. 3483 3484

Summary: Details of the Essential Variables. 3485 3486

Arc multiple arcs traversing environment Orthogonality of control Summary: How long should an arc be? 3511, 3514, 3557 Equilibrium neutral equilibrium and part-functions Part-function neutral equation and 3487 3488

Summary: Part-functions are apt to lead to many useless neutral equilibria. 3491 - No they are acceptable; 3495 Constant intrinsic stability when part-function Part-function if of constant intrinsic stability Summary: Variable of constant intrinsic stability as part-function. (See also below) 3489 3490

3491 3492

Summary: Equilibria in systems of part-functions. (See below) Essential variables and neutral equilibrium 3493 3494

Summary: Types of equilibria in DAMS. Conclusion: Neutral equilibria, with esential variables within limits, are acceptable. Summary: How to study a complex system. 3495 3496

Resting state shift when one variable is shifted Stimulus reaction is proportional to size of Summary: For a small disturbance, the effects everywhere tend to be proportional to the size of the disturbance. 3497 3498

Code inverse Environment as transducer Transducer inverse Transformation inverse Oddments [36]: If the environment is operator E, the brain must become -E-1. ●. Environment must be non-singular Essential variables information from Step function carries information Summary: If the environment is E the brain must become -E-1 4294 3499 3500

Natural Selection [10]: Example how apparently useless characteristics may be selected ●. Natural Selection [16]: Haldane on natural selection, ●. Summary: Two new types of information in the multistable system. 3521 3501 3502

Dispersion in motor cortex Summary: Haldane's book. Summary: Example of dispersion. Equilibrium number of states DAMS (Dispersive and Multistable System) [33]: Number of resting states in DAMS, ●. 3503 3504

Summary: How many resting states has DAMS? Cause gene as Genes Grüneberg's Natural Selection [4]: Example of a gene that has many and far-reaching effects ●. 3505 3506

Society [40]: Moreno's diagrams of the structure of social groups, ●. 3507 3508

Natural Selection [5]: There are two sexes so as to bring together all mutations. Each valve in DAMS has two inputs for the same reason ●. DAMS (Dispersive and Multistable System) [34]: History of DAMS: taken down for uniformisation, ●. DAMS (Dispersive and Multistable System) [35]: The two inputs to each value cause mixing just as do two sexes, ●. Summary: History of DAMS. Summary: Sex and the Multistable System Information nature of Redundancy organisms prefer highly redundant information 3509 3510

Mathematics emphasis on linear and additive methods Natural Selection [36]: Evolution as a random search for solutions of problems, ●. The Multistable System [23]: Multistable system as searcher for linear methods, ●. 3511 3512

Design empirical Epistemology of complex systems Natural Selection [19]: How to improve a very complex system, ●. Natural Selection [44]: Method for improving, ●. DAMS (Dispersive and Multistable System) [36]: How to improve DAMS, ●. Summary: Testing DAMS. 4511 Arc in 'cortex' of DAMS (Dispersive and Multistable System) 3513 3514

Essential variables how many is optimal? Summary: Designing DAMS. Summary: More complexity means more essential variables, which then have all to be satisfied. 3515 3516

Summary: Improvement by the purely empirical is as old as industry. Environment relation to essential variables Essential variables relations to environment etc. 3517 3518

Receptors reason for multistable systems Signal multistable system responding to The Multistable System [24]: Multistable system responding to 'signal', ●. 3519 3520

Environment REVIEW Essential variables REVIEW OF Summary: How to arrange DAMS. DAMS can react to a 'signal' or 'symbol'. Money reduction of all social variables to Summary: Index to Essential Variables since 3289. 3582 Summary: Reduction of all variables to a common form is of no importance. 3521 3522

Learning must use step-functions Step function necessary in central nervous system 3523 3524

Summary: Fundamental theorem that the nervous system must contain step-functions. 3525 3526

Natural Selection [34]: The development of histological staining followed essentially Darwinian lines, ●. Also pastry-making, ●. Chess how to play super chess Society [42]: Advanced society planned as a super brain ●. Should be able to undertake super-chess, ibid. 3527 3528

Transformation random transformation Organisation frequency of trials Trial and error internal between trials Natural Selection [17]: Mutation rate must not be too high, ●. Natural Selection [20]: Developing a super-chess playing system, ●. Summary: Fully developed form of the 'mechanical brain'; design for a chess-playing 'machine'.4563 3529 3530

The Multistable System [25]: Some step-functions should be hardly accessible, ●. Summary: The brain should have some step-functions almost inaccessible to the environment. Intelligence magnification of 3531 3532

3533 3534

Resting state 'intelligence' at Summary: Definition of a system's 'intelligence' at a resting state. Anatomy as constraint Environment relation to anatomy 3535 3536

Summary: Anatomical features gives quick success but lose generality. (Next page) 4563 DAMS (Dispersive and Multistable System) [37]: Wiring counts as 'anatomy' in DAMS, ●. Summary: Fixed qualities in a system. 3537 3538

Instinct Tinbergen on Signal in instinct Summary: Neurofibrils exist. Cause meaning of Hawk as stimulus Stimulus usually transformed Transformation of stimuli 3539 3540

Transformation and inate releasing mechanisms Conflict between instincts Learning gets fixed 3541 3542

Natural Selection [18]: Instability of sexual signals, ●. Neon lamp 3543 3544

Natural Selection [35]: Natural selection of soap-bubble combinations, ●. DAMS (Dispersive and Multistable System) [38]: Neon lamp as absolute system, ●. Summary: The neon in DAMS as absolute system. Natural Selection [12]: Grains of sand falling through a hole ●. The Multistable System [39]: Crystals as multistable system, ●. 3545 3546

Habituation in metal crystal Summary: Some statistical systems. Review 4141 DAMS (Dispersive and Multistable System) [39]: Effect of joining by subsystems, ●. 3547 3548

DAMS (Dispersive and Multistable System) [40]: Subsystems formed when many units are joined at random, ●. 3549 3550

Summary: Joining at random and by sub-systems. 3551 3552

Logic proof that we do not think with it Psychiatric applications [41]: We think dynamically, not logically, ●. Summary: We think dynamically, not logically. 3553 3554

Basic pattern various, on DAMS (Dispersive and Multistable System) Network some types tested DAMS (Dispersive and Multistable System) [41]: Various patterns of joining tried, ●. 3555 3556

DAMS (Dispersive and Multistable System) [42]: Principle for eliminating oscillating circuits ●. Summary: How to join DAMS. 3557 3558

Essential variables relations to environment etc. DAMS (Dispersive and Multistable System) [43]: Environment, Essential Variables, and network, ●. 3559 3560

Arc length of Summary: Joining up Essential Variables, Environment, and network. Review 3582 Independence testing for, in DAMS (Dispersive and Multistable System) Neuron numbers of DAMS (Dispersive and Multistable System) [44]: Method of testing for 'illegitamate' and wandering actions, ●. DAMS (Dispersive and Multistable System) [45]: Number of neurone in monkey's nuclei ●. 3561 3562

Arc multiply traversing Environment representation of Summary: Representation of a typical environment. 3563 3564

3565 3566

3567 3568

Arc number of Summary: The number of circuits that passes through each valve in DAMS is large. 3569 3570

DAMS (Dispersive and Multistable System) [46]: Effect of a 'bottleneck' in the levels ●. 3571 3572

3573 3574

3575 3576

DAMS (Dispersive and Multistable System) [68]: Roots of a system joined in levels and with one level reduced to a single variable, ●. DIAGRAM Summary: Proof that circuits of levels that include a one- variable level arc easy to stabilise. 3577 3578

Canonical equations simple examples Summary: Processes for elementary study. 3579 3580

Environment reviewed again Equilibrium multistable system around state of The Multistable System [81]: In a system of part-functions, all infinitesimal displacements, in whatever direction, from a resting state activate the same set of variables, ●, ●. Summary: Infinitesimal displacements activate a unique set of variables. (3599) Environment reviewed again Essential variables Reviewed again 3581 3582

Environment reviewed again Essential variables Reviewed again Environment reviewed again Essential variables Reviewed again 3583 3584

Environment reviewed again Essential variables Reviewed again Environment reviewed again Essential variables Reviewed again 3585 3586

3586+01 3586+02

These images are reproduced courtesy of The Estate of W. Ross Ashby. Copyright 1972, 2008 © The Estate of W. Ross Ashby

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