set of work based on
chakra alignments
and music keys
color structures are based on chord structures and notes
painting structures based on combinations i ching hexagrams.
Tuesday, February 3, 2009
Monday, February 2, 2009
i ching
http://www.laetusinpraesens.org/docs/ching/achngcot.php
Hexagram Lookup Table
Upper →
Lower ↓
(☰)
QianHeaven
¦¦ (☳)
ZhenThunder
¦¦ (☵)
KanWater
¦¦ (☶)
GenMountain
¦¦¦ (☷)
KunEarth
¦ (☴)
XunWind
¦ (☲)
LiFlame
¦ (☱)
DuiSwamp
(☰)
QianHeaven
[edit] The hexagrams
The text of the I Ching describes each of the 64 hexagrams, and later scholars added commentaries and analyses of each one; these have been subsumed into the text comprising the I Ching.
Each hexagram's common translation is accompanied by the corresponding R. Wilhelm translation, which is the source for the Unicode names.
Hexagram
R. Wilhelm
Modern Interpretation
01. Force (乾 qián)
The Creative
Possessing Creative Power & Skill [13]
02. ¦¦¦¦¦¦ Field (坤 kūn)
The Receptive
Needing Knowledge & Skill; Do not force matters and go with the flow [14], [15]
03. ¦¦¦¦ Sprouting (屯 chún)
Difficulty at the Beginning [16]
Sprouting [17]
04. ¦¦¦¦ Enveloping (蒙 méng)
Youthful Folly
Detained, Enveloped and Inexperienced [18], [19]
05. ¦¦ Attending (需 xū)
Waiting
Uninvolvement (Wait for now), Nourishment [20]
06. ¦¦ Arguing (訟 sòng)
Conflict
Engagement in Conflict [21]
07. ¦¦¦¦¦ Leading (師 shī)
The Army
Bringing Together, Teamwork [22]
08. ¦¦¦¦¦ Grouping (比 bǐ)
Holding Together
Union [23]
09. ¦ Small Accumulating (小畜 xiǎo chù)
Small Taming
Accumulating Resources
10. ¦ Treading (履 lǚ)
Treading (Conduct)
Continuing with Alertness
11. ¦¦¦ Pervading (泰 tài)
Peace
Pervading
12. ¦¦¦ Obstruction (否 pǐ)
Standstill
Stagnation
13. ¦ Concording People (同人 tóng rén)
Fellowship
Fellowship, Partnership
14. ¦ Great Possessing (大有 dà yǒu)
Great Possession
Independence, Freedom
15. ¦¦¦¦¦ Humbling (謙 qiān)
Modesty
Being Reserved, Refraining
16. ¦¦¦¦¦ Providing-For (豫 yù)
Enthusiasm
Inducement, New Stimulus
17. ¦¦¦ Following (隨 suí)
Following
Following
18. ¦¦¦ Corrupting (蠱 gǔ)
Work on the Decayed
Repairing
19. ¦¦¦¦ Nearing (臨 lín)
Approach
Approaching Goal, Arriving [24]
20. ¦¦¦¦ Viewing (觀 guān)
Contemplation
The Withholding
21. ¦¦¦ Gnawing Bite (噬嗑 shì kè)
Biting Through
Deciding
22. ¦¦¦ Adorning (賁 bì)
Grace
Embellishing
23. ¦¦¦¦¦ Stripping (剝 bō)
Splitting Apart
Stripping, Flaying
24. ¦¦¦¦¦ Returning (復 fù)
Return
Returning
25. ¦¦ Without Embroiling (無妄 wú wàng)
Innocence
Without Rashness
26. ¦¦ Great Accumulating (大畜 dà chù)
Great Taming
Accumulating Wisdom
27. ¦¦¦¦ Swallowing (頤 yí)
Mouth Corners
Seeking Nourishment
28. ¦¦ Great Exceeding (大過 dà guò)
Great Preponderance
Great Surpassing
29. ¦¦¦¦ Gorge (坎 kǎn)
The Abysmal Water
Darkness, Gorge
30. ¦¦ Radiance (離 lí)
The Clinging
Clinging, Attachment
31. ¦¦¦ Conjoining (咸 xián)
Influence
Attraction
32. ¦¦¦ Persevering (恆 héng)
Duration
Perseverance
Hexagram
R. Wilhelm
Modern Interpretation
33. ¦¦ Retiring (遯 dùn)
Retreat
Withdrawing
34. ¦¦ Great Invigorating (大壯 dà zhuàng)
Great Power
Great Boldness
35. ¦¦¦¦ Prospering (晉 jìn)
Progress
Expansion, Promotion
36. ¦¦¦¦ Brightness Hiding (明夷 míng yí)
Darkening of the Light
Brilliance Injured
37. ¦¦ Dwelling People (家人 jiā rén)
The Family
Family
38. ¦¦ Polarising (睽 kuí)
Opposition
Division, Divergence
39. ¦¦¦¦ Limping (蹇 jiǎn)
Obstruction
Halting, Hardship
40. ¦¦¦¦ Taking-Apart (解 xiè)
Deliverance
Liberation, Solution
41. ¦¦¦ Diminishing (損 sǔn)
Decrease
Decrease
42. ¦¦¦ Augmenting (益 yì)
Increase
Increase
43. ¦ Parting (夬 guài)
Breakthrough
Separation
44. ¦ Coupling (姤 gòu)
Coming to Meet
Encountering
45. ¦¦¦¦ Clustering (萃 cuì)
Gathering Together
Association, Companionship
46. ¦¦¦¦ Ascending (升 shēng)
Pushing Upward
Growing Upward
47. ¦¦¦ Confining (困 kùn)
Oppression
Exhaustion
48. ¦¦¦ Welling (井 jǐng)
The Well
Replenishing, Renewal
49. ¦¦ Skinning (革 gé)
Revolution
Abolishing the Old
50. ¦¦ Holding (鼎 dǐng)
The Cauldron
Establishing the New
51. ¦¦¦¦ Shake (震 zhèn)
Arousing
Mobilizing
52. ¦¦¦¦ Bound (艮 gèn)
The Keeping Still
Immobility
53. ¦¦¦ Infiltrating (漸 jiàn)
Development
Auspicious Outlook, Infiltration
54. ¦¦¦ Converting The Maiden (歸妹 guī mèi)
The Marrying Maiden
Marrying
55. ¦¦¦ Abounding (豐 fēng)
Abundance
Goal Reached, Ambition Achieved
56. ¦¦¦ Sojourning (旅 lǚ)
The Wanderer
Travel
57. ¦¦ Ground (巽 xùn)
The Gentle
Subtle Influence
58. ¦¦ Open (兌 duì)
The Joyous
Overt Influence
59. ¦¦¦ Dispersing (渙 huàn)
Dispersion
Dispersal
60. ¦¦¦ Articulating (節 jié)
Limitation
Discipline
61. ¦¦ Centre Confirming (中孚 zhōng fú)
Inner Truth
Staying Focused, Avoid Misrepresentation
62. ¦¦¦¦ Small Exceeding (小過 xiǎo guò)
Small Preponderance
Small Surpassing
63. ¦¦¦ Already Fording (既濟 jì jì)
After Completion
Completion
64. ¦¦¦ Not-Yet Fording (未濟 wèi jì)
Before Completion
Incompletion
The hexagrams, though, are mere mnemonics for the philosophical concepts embodied in each one. The philosophy centres around the ideas of balance through opposites and acceptance of change.
autocorrelation & cross correlation
Autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal which has been buried under noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies. It is used frequently in signal processing for analyzing functions or series of values, such as time domain signals. Informally, it is the similarity between observations as a function of the time separation between them. More precisely, it is the cross-correlation of a signal with itself.
In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or inner-product. It is commonly used to search a long duration signal for a shorter, known feature. It also has applications in pattern recognition, single particle analysis, electron tomographic averaging, and cryptanalysis.
In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or inner-product. It is commonly used to search a long duration signal for a shorter, known feature. It also has applications in pattern recognition, single particle analysis, electron tomographic averaging, and cryptanalysis.
neural networks
Traditionally, the term neural network had been used to refer to a network or circuit of biological neurons[citation needed]. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks
control theory
[edit] Modern control theory
In contrast to the frequency domain analysis of the classical control theory, modern control theory utilizes the time-domain state space representation, a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations. To abstract from the number of inputs, outputs and states, the variables are expressed as vectors and the differential and algebraic equations are written in matrix form (the last one can be done when the dynamical system is linear and time invariant). The state space representation (also known as the "time-domain approach") provides a convenient and compact way to model and analyze systems with multiple inputs and outputs. With inputs and outputs, we would otherwise have to write down Laplace transforms to encode all the information about a system. Unlike the frequency domain approach, the use of the state space representation is not limited to systems with linear components and zero initial conditions. "State space" refers to the space whose axes are the state variables. The state of the system can be represented as a vector within that space.
Lyapunov stability
From Wikipedia, the free encyclopedia
Jump to: navigation, search
This article is about asymptotic stability of nonlinear systems. For stability of linear systems, see exponential stability.
In mathematics, the notion of Lyapunov stability occurs in the study of dynamical systems. In simple terms, if all solutions of the dynamical system that start out near an equilibrium point xe stay near xe forever, then xe is Lyapunov stable. More strongly, if all solutions that start out near xe converge to xe, then xe is asymptotically stable. The notion of exponential stability guarantees a minimal rate of decay, i.e., an estimate of how quickly the solutions converge. The idea of Lyapunov stability can be extended to infinite-dimensional manifolds, where it is known as structural stability, which concerns the behaviour of different but "nearby" solutions to differential equations. Input-to-state stability (ISS) applies Lyapunov notions to systems with inputs.
Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks
control theory
[edit] Modern control theory
In contrast to the frequency domain analysis of the classical control theory, modern control theory utilizes the time-domain state space representation, a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations. To abstract from the number of inputs, outputs and states, the variables are expressed as vectors and the differential and algebraic equations are written in matrix form (the last one can be done when the dynamical system is linear and time invariant). The state space representation (also known as the "time-domain approach") provides a convenient and compact way to model and analyze systems with multiple inputs and outputs. With inputs and outputs, we would otherwise have to write down Laplace transforms to encode all the information about a system. Unlike the frequency domain approach, the use of the state space representation is not limited to systems with linear components and zero initial conditions. "State space" refers to the space whose axes are the state variables. The state of the system can be represented as a vector within that space.
Lyapunov stability
From Wikipedia, the free encyclopedia
Jump to: navigation, search
This article is about asymptotic stability of nonlinear systems. For stability of linear systems, see exponential stability.
In mathematics, the notion of Lyapunov stability occurs in the study of dynamical systems. In simple terms, if all solutions of the dynamical system that start out near an equilibrium point xe stay near xe forever, then xe is Lyapunov stable. More strongly, if all solutions that start out near xe converge to xe, then xe is asymptotically stable. The notion of exponential stability guarantees a minimal rate of decay, i.e., an estimate of how quickly the solutions converge. The idea of Lyapunov stability can be extended to infinite-dimensional manifolds, where it is known as structural stability, which concerns the behaviour of different but "nearby" solutions to differential equations. Input-to-state stability (ISS) applies Lyapunov notions to systems with inputs.
post translational modification
http://www.biotech-online.com/featured-articles/ptm-of-brain-proteins-as-substrate-for-memories-that-last-a-lifetime/index.html
The brain mechanisms underlying the long-term storage of memories remains a fundamental unsolved problem in biology. The current prevailing view is that neural activity generated by learning alters synaptic relationships and then instigates de novo protein synthesis. These newly synthesised molecules then go to those very same synapses, stabilise them and as a consequence, stabilise the memory itself.
At our peril, we have advanced a model whereby post-translational modification (PTM) of proteins already present at synapses is sufficient for long-lasting memory storage. The PTM mechanisms are sufficiently large in number to enable rather subtle multidimensional scaling within the synapse itself. A few of these candidates – phosphorylation, polymerization, translocation, and proteolysis – have already been linked to memory formation, though not necessarily the long-lasting form.
Here we briefly describe our proposal that PTM of synaptic proteins is both necessary and sufficient for information storage acting to instruct and guide the formation of networks that represent the memories of lessons learned. In contrast, translational machinery acts to replenish depleted proteins in a permissive fashion.
The implications of this research for the development of drugs to aid memory are discussed.by Jerome L. Rekart and Aryeh RouttenbergPost-translational modification (PTM) of cellular proteins is universally recognised as a pivotal biochemical mechanism responsible for regulating the functionality of cellular proteins and thus critical to the normal functioning of eukaryotic cells. It is not too surprising, therefore, that it has also been found to be an important mediator of the neuronal changes associated with learning and memory in species ranging from insects [1] to primates [2].
The link between PTM of brain proteins and information storage in the brain has long been viewed as critical for the initial stages of memory formation [3, 4, 5] but not its long-lasting stage. Despite the ubiquity of PTMs and their necessity for cellular functioning, the prevailing view of memory storage holds that though necessary for the storage of information in the short-term, they are not sufficient for the long-term cellular changes underlying long-lasting memory.
Instead, long-term changes are believed to be protein synthesis (PS)-dependent, relying on signalling cascades that in turn regulate translational machinery [6]. De novo PS is thus seen to coordinate the cellular changes associated with long-lasting memory. This two-phase view of cellular learning, sometimes referred to as the dual-trace hypothesis [7], with an initial protein synthesis-independent step followed by a long-term process requiring protein synthesis (PS-dependent), is now current “wisdom” and thus articulated without reservation in major neuroscience textbooks [8].
The position taken here is that this view is essentially untenable because the fundamental cellular events underlying long-lasting memory formation require only PTM of synaptic proteins. Translational machinery serves to replace depleted proteins after molecular degradation brought on by protein PTM in the service of selective and instructive neural plasticity and memory-related processes.
Thus, protein synthesis is a permissive rather than an instructive process in the long-term storage of information. The centrality of protein synthesis to the cellular changes underlying learning and memory is thus far from settled. Indeed, there are a number of non-trivial logical criticisms and methodological concerns related to the study of protein synthesis, which have been enumerated in detail elsewhere [5, 9, 10, 11] including the requirements for the production, transport and targeting of new proteins to activated synapses.
The fact that many proteins have half-lives of a day or so, while memories last for a lifetime, is but one unresolved issue. The situation becomes overly complex when one imagines a system that would need to extract punctate temporal events from the inexorable flow of time’s arrow.
That is, the discontinuity required by our ability to attach chronology to a given memory requires that the cell disrupt the normal, continual dendritic transport of material to synapses in order to render it punctate at specific synaptic locales. This appears to be a formidable barrier.
In contrast, the PTM hypothesis obviates the need for this complex trafficking issue as part of the instructive mechanism. Instructions can thus be satisfactorily implemented by PTM of brain proteins already present at the synapse at the time of learning [5, 9] giving rise to a controlled, rapidly-adaptive, memory system that is resistant to disruption.
Perseverance of memory then results consequent to maintenance of some residual of the post-translational modification of proteins already present at activated sites. It is known that the initial stages of memory and the consequent activity-dependent modification of synaptic strength are regulated by neural plasticity-related enzymes, such as kinases and phosphatases and their protein substrates.
What is perhaps unique about the PTM model is that such modification implementing plasticity-related enzymes is sufficient for memory formation and its perseverance [9], is clearly necessary [12] and, given the wide range of mechanisms included under the PTM rubric, it is also possibly exclusive.The PTM model eschews the long-term commitment of energy and resources suggested by the PS-model.
Counterintuitively, a stable memory does not require a stable synapse. Indeed, this PTM-synaptic malleability is consistent with data and theories of neural homeostatic plasticity, which require reversibility of cellular changes [13] so as to thwart the occurrence of a thermonuclear engram [5].
The removal of translation as a necessary component for information storage increases the responsiveness and adaptability of the cell to input and removes the necessity for a targeting or “tagging” mechanism to direct nascent proteins to their proper destinations as all of the necessary enzymes and substrate molecules will already be present at the appropriate synapse.T
he PTM model is embedded within and builds upon the context of the neural network. Thus, the ongoing post-learning duplication of cortical and subcortical networks underlying a given memory provides a safeguard against the disruption of memory due to loss of circuitry, or indeed new learning, or traumatic experiences or local brain damage.
Moreover, cryptic rehearsal, previously thought of a ‘spontaneous activity’ can act to prolong PTM states, acting as a regulated positive feedback system [5].IMPLICATIONS OF THE PTM MODELInstead of the ‘dual trace’ model currently in vogue, the PTM model advocates a single trace mechanism, obviating the temporal categorisation of memory into distinct ‘stages.
’ The prevailing model of memory divides the phenomenon into distinct processes, in which short-term memory ‘processes’, believed to be protein synthesis-independent, may last after the learning event from seconds up to several hours. Then the trace becomes PS-dependent and thus categorised as either long-term. This may be preceded by the recently-discussed ‘intermediate-term’ memories [14], which last from hours to days.
Then at some undetermined time memory undergoes sufficient consolidation and transitions from ‘long-term’ to ‘long-lasting’, the latter believed to persist as long as the organism. The situation becomes murky when considering the view that multiple ‘waves’ of protein synthesis are necessary for long-term memory [15].
As such evidence is based on the use of protein synthesis inhibitors, all of the requisite caveats [5] apply.Although labels like ‘short-term’ or ‘long-lasting’ memory provide a useful shorthand for describing the age of a memory, the PTM model proposed renders the use of poorly defined temporal categories of memory stages as unnecessary because the mechanisms underlying memory storage are the same 50 years after the event as they are 50 minutes after its initial occurrence: post-translational modification of the molecules already present at
synapses.
RAMIFICATIONS OF THE PTM MODEL: APPROACHES TO ESTABLISHING ITS VALIDITY
In order for the PTM hypothesis to be of value it needs to be tested with strong inference experiments so that the outcome allows one to confirm or deny its suitability. Another value-added approach is to determine whether such a model can suggest pharmaceuticals that will reverse memory impairments in neuroclinical conditions and also enhance memory ability in normal, healthy adults.
As reviewed elsewhere, the PTM hypothesis is supported by
a) the ability to form memories in the presence of near total protein synthetic inhibition,
b) the demonstration of long-lasting PTM of proteins linked to long-lasting memory, and c) the disruption of memory by PTM inhibitors days after the memory is formed.
In a) above, such evidence serves to falsify the assertion that protein synthesis is instructive for memory.
In b) identifying substrate molecules that are phosphorylated, dephosphorylated, etc. in response to stimuli and their associated kinases and phosphatases (in the case of phosphorylation) provides initial clues for new drug targets.
In c) the central tenet of the PTM-model of information storage is evaluated: the meta-stability of the neural networks underlying memories as regulated by PTM.
In an initial test of the PTM theory, Holahan and Routtenberg [16] demonstrated that injection (3 weeks after learning and 1 hour before retention test) of H-7, the broad spectrum serine-threonine kinase inhibitor, into anterior cingulate cortex interferes with memory retention for the original event.
As a negative result would have been taken as evidence against the PTM hypothesis, in fact, the PTM model survives this strong inference test. Moreover, the work of Shema, Sacktor and Dudai (2007) [17], along with our work, suggests that a potentially important drug target opportunity may be synaptic proteins that are protein kinase C substrates.
Ultimately, the PTM hypothesis should provide important leads in the development of drugs to aid memory, for those who are memory-impaired and for those normal, healthy adults who may wish to enhance this ability. We have previously provided models of how phosphorylation regulated by protein kinase C, alters the protein-protein interaction between a PKC substrate and those proteins that regulate transmitter release [18].
Discerning the epitopes where these synaptic phosphoproteomic intereactions occur could provide the substrate for selectively facilitating synaptic function and thereby facilitating the memory formation process.
Clearly, this is a bold suggestion at this juncture. Nonetheless, it serves to illustrate how a new hypothesis of memory formation may allow the formulation of a new direction in regulating our information storage capacity through the selective modification of critical sites on memory-related,
PTM-modified synaptic proteins. REFERENCES1. Fiala, A. et al. Journal of Neuroscience 1999:19;10125-10134. 2. Nelson, R.B et al. Brain Research 1987:416;387-392. 3. Routtenberg, A. Progress in Neurobiology 1979:12(2);85-113. 4. Routtenberg, A. (1982). Memory formation as a post-translational modification of brain proteins. In C.A. Marsden and H. Matthies (Eds.), Mechanisms and Models of Neural Plasticity: IBRO Symposium on Learning and Memory,Vol. 9 (pp. 17-24). New York: Raven Press.5. Routtenberg, A., & Rekart, J.L. Trends in Neurosciences 2005:28(1);12-19.6. Kandel, E.R. Science 2001:294; 1030-1038.7. Hebb, D. O. (1949). The Organization of Behavior. New York: John Wiley.8. Bear, M.F. et al. (2006). Neuroscience. New York: Lippincott. 9. Routtenberg, A. Neurobiology of Learning and Memory 2008:89(3);225-233. 10. Gold, P.E. Neurobiology of Learning and Memory 2008:89(3);201-211.11. Rudy, J.W. Neurobiology of Learning and Memory 2008:89(3);219-224.12. Colley, P.A. et al. Journal of Neuroscience 1990:10(10);3353-3360.13. Turrigiano, G. Current Opinion in Neurobiology 2007:17(3);318-324.14. Stough, S. et al. Current Opinion in Neurobiology 2006:16(6);672-678. 15. Richter, K et al. Learning & Memory 2005:12(4);407-413.16. Holahan, M.R. & Routtenberg, A. Hippocampus 2007:17;93-97.17. Shema, R. et al. Science:2007;317, 951-953.18. Routtenberg, A. et al. Proceedings of the National Academy of Sciences U.S.A 2000:97;7657-7662.AUTHORSJerome L. RekartDepartment of PsychologyRivier CollegeNashuaNH, USAAryeh Routtenberg*Departments of Psychology, Neurobiology & PhysiologyNeuroscience InstituteSwift Hall Room 102Northwestern University2029 Sheridan Rd, Evanston, IL, USATel: +1 (847) 491-3628Fax: +1 (847) 491-3557Email: aryeh@northwestern.eduAuthor for correspondance
The brain mechanisms underlying the long-term storage of memories remains a fundamental unsolved problem in biology. The current prevailing view is that neural activity generated by learning alters synaptic relationships and then instigates de novo protein synthesis. These newly synthesised molecules then go to those very same synapses, stabilise them and as a consequence, stabilise the memory itself.
At our peril, we have advanced a model whereby post-translational modification (PTM) of proteins already present at synapses is sufficient for long-lasting memory storage. The PTM mechanisms are sufficiently large in number to enable rather subtle multidimensional scaling within the synapse itself. A few of these candidates – phosphorylation, polymerization, translocation, and proteolysis – have already been linked to memory formation, though not necessarily the long-lasting form.
Here we briefly describe our proposal that PTM of synaptic proteins is both necessary and sufficient for information storage acting to instruct and guide the formation of networks that represent the memories of lessons learned. In contrast, translational machinery acts to replenish depleted proteins in a permissive fashion.
The implications of this research for the development of drugs to aid memory are discussed.by Jerome L. Rekart and Aryeh RouttenbergPost-translational modification (PTM) of cellular proteins is universally recognised as a pivotal biochemical mechanism responsible for regulating the functionality of cellular proteins and thus critical to the normal functioning of eukaryotic cells. It is not too surprising, therefore, that it has also been found to be an important mediator of the neuronal changes associated with learning and memory in species ranging from insects [1] to primates [2].
The link between PTM of brain proteins and information storage in the brain has long been viewed as critical for the initial stages of memory formation [3, 4, 5] but not its long-lasting stage. Despite the ubiquity of PTMs and their necessity for cellular functioning, the prevailing view of memory storage holds that though necessary for the storage of information in the short-term, they are not sufficient for the long-term cellular changes underlying long-lasting memory.
Instead, long-term changes are believed to be protein synthesis (PS)-dependent, relying on signalling cascades that in turn regulate translational machinery [6]. De novo PS is thus seen to coordinate the cellular changes associated with long-lasting memory. This two-phase view of cellular learning, sometimes referred to as the dual-trace hypothesis [7], with an initial protein synthesis-independent step followed by a long-term process requiring protein synthesis (PS-dependent), is now current “wisdom” and thus articulated without reservation in major neuroscience textbooks [8].
The position taken here is that this view is essentially untenable because the fundamental cellular events underlying long-lasting memory formation require only PTM of synaptic proteins. Translational machinery serves to replace depleted proteins after molecular degradation brought on by protein PTM in the service of selective and instructive neural plasticity and memory-related processes.
Thus, protein synthesis is a permissive rather than an instructive process in the long-term storage of information. The centrality of protein synthesis to the cellular changes underlying learning and memory is thus far from settled. Indeed, there are a number of non-trivial logical criticisms and methodological concerns related to the study of protein synthesis, which have been enumerated in detail elsewhere [5, 9, 10, 11] including the requirements for the production, transport and targeting of new proteins to activated synapses.
The fact that many proteins have half-lives of a day or so, while memories last for a lifetime, is but one unresolved issue. The situation becomes overly complex when one imagines a system that would need to extract punctate temporal events from the inexorable flow of time’s arrow.
That is, the discontinuity required by our ability to attach chronology to a given memory requires that the cell disrupt the normal, continual dendritic transport of material to synapses in order to render it punctate at specific synaptic locales. This appears to be a formidable barrier.
In contrast, the PTM hypothesis obviates the need for this complex trafficking issue as part of the instructive mechanism. Instructions can thus be satisfactorily implemented by PTM of brain proteins already present at the synapse at the time of learning [5, 9] giving rise to a controlled, rapidly-adaptive, memory system that is resistant to disruption.
Perseverance of memory then results consequent to maintenance of some residual of the post-translational modification of proteins already present at activated sites. It is known that the initial stages of memory and the consequent activity-dependent modification of synaptic strength are regulated by neural plasticity-related enzymes, such as kinases and phosphatases and their protein substrates.
What is perhaps unique about the PTM model is that such modification implementing plasticity-related enzymes is sufficient for memory formation and its perseverance [9], is clearly necessary [12] and, given the wide range of mechanisms included under the PTM rubric, it is also possibly exclusive.The PTM model eschews the long-term commitment of energy and resources suggested by the PS-model.
Counterintuitively, a stable memory does not require a stable synapse. Indeed, this PTM-synaptic malleability is consistent with data and theories of neural homeostatic plasticity, which require reversibility of cellular changes [13] so as to thwart the occurrence of a thermonuclear engram [5].
The removal of translation as a necessary component for information storage increases the responsiveness and adaptability of the cell to input and removes the necessity for a targeting or “tagging” mechanism to direct nascent proteins to their proper destinations as all of the necessary enzymes and substrate molecules will already be present at the appropriate synapse.T
he PTM model is embedded within and builds upon the context of the neural network. Thus, the ongoing post-learning duplication of cortical and subcortical networks underlying a given memory provides a safeguard against the disruption of memory due to loss of circuitry, or indeed new learning, or traumatic experiences or local brain damage.
Moreover, cryptic rehearsal, previously thought of a ‘spontaneous activity’ can act to prolong PTM states, acting as a regulated positive feedback system [5].IMPLICATIONS OF THE PTM MODELInstead of the ‘dual trace’ model currently in vogue, the PTM model advocates a single trace mechanism, obviating the temporal categorisation of memory into distinct ‘stages.
’ The prevailing model of memory divides the phenomenon into distinct processes, in which short-term memory ‘processes’, believed to be protein synthesis-independent, may last after the learning event from seconds up to several hours. Then the trace becomes PS-dependent and thus categorised as either long-term. This may be preceded by the recently-discussed ‘intermediate-term’ memories [14], which last from hours to days.
Then at some undetermined time memory undergoes sufficient consolidation and transitions from ‘long-term’ to ‘long-lasting’, the latter believed to persist as long as the organism. The situation becomes murky when considering the view that multiple ‘waves’ of protein synthesis are necessary for long-term memory [15].
As such evidence is based on the use of protein synthesis inhibitors, all of the requisite caveats [5] apply.Although labels like ‘short-term’ or ‘long-lasting’ memory provide a useful shorthand for describing the age of a memory, the PTM model proposed renders the use of poorly defined temporal categories of memory stages as unnecessary because the mechanisms underlying memory storage are the same 50 years after the event as they are 50 minutes after its initial occurrence: post-translational modification of the molecules already present at
synapses.
RAMIFICATIONS OF THE PTM MODEL: APPROACHES TO ESTABLISHING ITS VALIDITY
In order for the PTM hypothesis to be of value it needs to be tested with strong inference experiments so that the outcome allows one to confirm or deny its suitability. Another value-added approach is to determine whether such a model can suggest pharmaceuticals that will reverse memory impairments in neuroclinical conditions and also enhance memory ability in normal, healthy adults.
As reviewed elsewhere, the PTM hypothesis is supported by
a) the ability to form memories in the presence of near total protein synthetic inhibition,
b) the demonstration of long-lasting PTM of proteins linked to long-lasting memory, and c) the disruption of memory by PTM inhibitors days after the memory is formed.
In a) above, such evidence serves to falsify the assertion that protein synthesis is instructive for memory.
In b) identifying substrate molecules that are phosphorylated, dephosphorylated, etc. in response to stimuli and their associated kinases and phosphatases (in the case of phosphorylation) provides initial clues for new drug targets.
In c) the central tenet of the PTM-model of information storage is evaluated: the meta-stability of the neural networks underlying memories as regulated by PTM.
In an initial test of the PTM theory, Holahan and Routtenberg [16] demonstrated that injection (3 weeks after learning and 1 hour before retention test) of H-7, the broad spectrum serine-threonine kinase inhibitor, into anterior cingulate cortex interferes with memory retention for the original event.
As a negative result would have been taken as evidence against the PTM hypothesis, in fact, the PTM model survives this strong inference test. Moreover, the work of Shema, Sacktor and Dudai (2007) [17], along with our work, suggests that a potentially important drug target opportunity may be synaptic proteins that are protein kinase C substrates.
Ultimately, the PTM hypothesis should provide important leads in the development of drugs to aid memory, for those who are memory-impaired and for those normal, healthy adults who may wish to enhance this ability. We have previously provided models of how phosphorylation regulated by protein kinase C, alters the protein-protein interaction between a PKC substrate and those proteins that regulate transmitter release [18].
Discerning the epitopes where these synaptic phosphoproteomic intereactions occur could provide the substrate for selectively facilitating synaptic function and thereby facilitating the memory formation process.
Clearly, this is a bold suggestion at this juncture. Nonetheless, it serves to illustrate how a new hypothesis of memory formation may allow the formulation of a new direction in regulating our information storage capacity through the selective modification of critical sites on memory-related,
PTM-modified synaptic proteins. REFERENCES1. Fiala, A. et al. Journal of Neuroscience 1999:19;10125-10134. 2. Nelson, R.B et al. Brain Research 1987:416;387-392. 3. Routtenberg, A. Progress in Neurobiology 1979:12(2);85-113. 4. Routtenberg, A. (1982). Memory formation as a post-translational modification of brain proteins. In C.A. Marsden and H. Matthies (Eds.), Mechanisms and Models of Neural Plasticity: IBRO Symposium on Learning and Memory,Vol. 9 (pp. 17-24). New York: Raven Press.5. Routtenberg, A., & Rekart, J.L. Trends in Neurosciences 2005:28(1);12-19.6. Kandel, E.R. Science 2001:294; 1030-1038.7. Hebb, D. O. (1949). The Organization of Behavior. New York: John Wiley.8. Bear, M.F. et al. (2006). Neuroscience. New York: Lippincott. 9. Routtenberg, A. Neurobiology of Learning and Memory 2008:89(3);225-233. 10. Gold, P.E. Neurobiology of Learning and Memory 2008:89(3);201-211.11. Rudy, J.W. Neurobiology of Learning and Memory 2008:89(3);219-224.12. Colley, P.A. et al. Journal of Neuroscience 1990:10(10);3353-3360.13. Turrigiano, G. Current Opinion in Neurobiology 2007:17(3);318-324.14. Stough, S. et al. Current Opinion in Neurobiology 2006:16(6);672-678. 15. Richter, K et al. Learning & Memory 2005:12(4);407-413.16. Holahan, M.R. & Routtenberg, A. Hippocampus 2007:17;93-97.17. Shema, R. et al. Science:2007;317, 951-953.18. Routtenberg, A. et al. Proceedings of the National Academy of Sciences U.S.A 2000:97;7657-7662.AUTHORSJerome L. RekartDepartment of PsychologyRivier CollegeNashuaNH, USAAryeh Routtenberg*Departments of Psychology, Neurobiology & PhysiologyNeuroscience InstituteSwift Hall Room 102Northwestern University2029 Sheridan Rd, Evanston, IL, USATel: +1 (847) 491-3628Fax: +1 (847) 491-3557Email: aryeh@northwestern.eduAuthor for correspondance
memory storage in the brain
MEDIA CONTACT: Elizabeth Crown at 312-503-8928 or e-crown@northwestern.edu
January 18, 2005
New Theory Challenges Current View of How Brain Stores Long-Term Memory
CHICAGO --- How do you remember your own name? Is it possible ever to forget it? The memory trace, or engram, “feels” like it is stored permanently in the brain and it will never be forgotten.
Indeed, the current view of memory is that, at the molecular level, new proteins are manufactured, in a process known as translation, and it is these newly synthesized proteins that subsequently stabilize the changes underlying the memory. Thus, every new memory results in a permanent representation in the brain.
But Northwestern University neuroscientist Aryeh Routtenberg has presented a provocative new theory that takes issue with that view. Routtenberg, with doctoral student Jerome L. Rekart, outlined the new theory on memory storage in the January issue of the journal Trends in Neuroscience.
Rather than permanent storage, there is a “dynamic, meta-stable” process, the authors said. Our subjective experience of permanence is a result of the re-duplication of memories across many different brain networks.
For example, one’s name is represented in innumerable neural circuits; thus, it is extremely difficult to forget. But each individual component is malleable and transient, and as no particular neural network lasts a lifetime, it is theoretically possible to forget one’s own name.
This is seen in the most advanced stages of Alzheimer’s disease, the researchers stated.
The advantage of such a precarious storage mechanism is that it is a highly flexible system, enabling rapid retrieval even of infrequent elements, with great advantages over models of permanent storage, said Routtenberg, professor in the department of psychology and in the department of neurobiology and physiology, Judd A. and Marjorie Weinberg College of Arts and Sciences and a leading researcher in the Institute for Neuroscience, Northwestern University.
To achieve this high degree of flexibility, Routtenberg’s new theory goes on to propose that the brain stores long-term memory by rapidly changing the shape of proteins already present at those synapses activated by learning.
While it is universally agreed that brain proteins are critical for memory storage, Routtenberg’s hypothesis challenges the widely accepted, 40-year-old model that long-term memories are stabilized only once newly synthesized proteins are transported to recently activated synapses.
Indeed, this view is central to the theory of Eric Kandel, who in his Nobel Prize address reinforced the central position of this model in forming long-term memory.
So does memory form because you make more protein, as most neuroscientists believe, or because you change the shape of existing proteins, which are known to be strategically located to effect change within milliseconds of activation?
Part of the answer to this question lies in the fact that there are critical weaknesses in the prevailing view.
“There are enough instances of memory storage in the virtual absence of protein synthesis to compel consideration of alternative models,” said Routtenberg.
The authors noted that most of the evidence supporting the current view was obtained by studying the effects of certain drugs, called protein synthesis inhibitors, on memory, leading to the conclusion that synthesis was necessary. The authors outline specific evidence that calls those results into question.
For example, synthesis inhibitors that block the production of new proteins by more than 90 percent often cause no discernible memory impairments. Additionally, protein synthesis inhibitors cause a number of side effects that could lead to memory loss caused by something other than protein synthesis inhibition.
Routtenberg agrees with the view that it is the synapse that is modified in response to learning-associated activity, a position first articulated by Nobelist Ramon y Cajal a century ago. But the difference with the current theory is that he and Rekart do not believe that synaptic modification is brought about by recently synthesized proteins.
Routtenberg’s theory, derived from a consideration of extensive, fundamental biochemical information, advocates that learning leads to a post-synthesis (or, post-translational) synaptic protein modification that results in changes to the shape, activity and/or location of existing synaptic proteins. In the Routtenberg-Rekart proposal, this is the only mechanism required for long-term memory.
To maintain some residue of this modification, Routtenberg proposes that the “spontaneous activity” of the brain actually acts to “cryptically rehearse” past events. So, long-term memory storage relies on a positive-feedback rehearsal system that continually updates or fine-tunes post-translational modification of previously modified synaptic proteins. It is in this manner that this model allows for the continual modifications of memories.
In the Routtenberg-Rekart model, post-translational modifications within cells and synaptic dialog and endogenous activity between cells and networks work in concert to perpetuate and update memory representations.
A group of post-translational protein modifications that affect neuronal plasticity – present in activated pre-synaptic and post-synaptic elements and regulated by proteases, kinases and phosphatases – regulate the efficacy of the synapse in response to a learning event.
These modifications are, in turn, maintained via positive feedback between cells (dialog), which are regulated by synaptic excitation (e.g., via the neurotransmitter glutamate) or inhibition (e.g., via the neurotransmitter GABA).
Thus, the self-sustaining positive feedback system also carries built-in control mechanisms that would prevent runaway feedback leading to the detonation of one massive memory or “thermonuclear” engram.
Although Routtenberg’s model may represent a radical departure from the current view of how long-term memories are stored, he believes that scientists need to articulate alternative models other than the prevailing one.
A more accurate description will help address issues of memory loss in mental retardation, aging and Alzheimer’s disease. Indeed, new hypotheses can lead to the development of new chemical agents that would successfully target the chemical reactions necessary
“We would assert that there is enough substance both in the concerns raised and in the post-translational modification/positive feedback model proposed to energize the search for yet more plausible models of long-term memory storage, and to redirect and reinvigorate the quest to understand the brain substrates of information storage,” Routtenberg said.
This research was supported by research grants from the National Institute of Mental Health and the National Science Foundation and a training grant from the NIMH.
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