Two different opposite approaches have emerged in the field. Both directions have consumed far more resources time and effort with very few results in understanding complex cognitive aspects.
- Spike Timing Dogma (STD) The reductionist approach of temporal coding has assumed stereotype spikes and weight type connections between neurons Most books, papers and current projects fall into this reductionist category (Dayan and Abbott; Gerstner et al.; Eliasmith and Anderson; Izhikevich .... Dharmendra Modha IBM, SyNAPSE (2008). The legend of spike timing dogma (STD) continues today with the hypothesis of spike-timing-dependent plasticity (STDP). However, the occurrence of rhythmic temporal patterns from interactions can be simulated on a digital computer using difference equations and particle swarm algorithms. Resulting temporal patterns carry little information regarding input features compared to transient charge density characteristics [2] (Chapter 4: Models of Brain Computation, pp128) [6]. The observed temporal patterns (spike timing, STDP) are epiphenomena, do not describe the intrinsic computational properties, they result from complex electrical interactions and mediated release of neurotransmitters. These issues explain unsuccessful attempts to attain cognitive function by replicating theoretical spike timing work adding millions of “neurons” on a chip. The reductionist approach of spike timing dogma offers no convincing concepts or methods to understand how the computation is performed in the brain since the framework neglects fundamental computations that occur in neurons. Adding a good already tested mathematical model (e.g. Bayesian model) to fit temporal data does not make the spike timing theory better. The interactions within neurons cannot be modeled as simple weight type Hebbian connections, a different computational model is needed:
- The reverse engineering (RE) approach. The expected failure of STD/ STDP has encouraged a more naive attempt to map the full brain including synaptic structures between neurons. The Blue Brain idea and many other brain mapping projects argue that biological reverse engineering is possible and needed to understand the brain. By 2010 the project was expected to “challenge the foundations of our understanding of intelligence and generate new theories of consciousness” Henry Markram, Blue Brain (2005) [1]. The reverse engineering (RE) of biological complexity with a focus on synaptic connectivity has generated issues by itself. Do we really need to replicate every brain molecule, every neuropeptide to be able to understand how underlying cognitive processes are built? We didn’t learn to fly by copying every natural detail of bird feathers.
Besides numerous slogans and gimmicks of showing “huge” progress all these projects have real issues to provide human-like cognitive ability. In addition there was little progress in any connected areas. Many neuroscientists are throwing various analytic techniques at temporal data and hope that magically a meaningful outcome will appear. A similar behavior is shared in the hardware implementation. The idea is that putting several spike timing ”neurons” together with ~10 billion “synapses” per square centimeter will automatically generate “cognitive” powers.
Both trends have failed to show significant results. In general what we cannot replicate we do not understand. The celebrated Hebb’s rule of neurons that ‘fire and wire together’ explains a process of interaction between neurons rather than hard wired “connectivity” or temporal coding
Both trends have failed to show significant results. In general what we cannot replicate we do not understand. The celebrated Hebb’s rule of neurons that ‘fire and wire together’ explains a process of interaction between neurons rather than hard wired “connectivity” or temporal coding
(i) Information is synaptically and non-synaptically (e.g. electric field) received and continuously processed within every cell. The generation of AP is the moment when fragments of information are 'read' or 'written' from/within molecular structures (e.g. proteins) as an effect of electrical interaction mediated by many other factors. Therefore, the propagation of AP represents the fundamental moment when information is also processed within neurons and this phenomenon can be described by a non-Turing process of computation by interaction. http://neuroelectrodynamics.blogspot.com/p/computing-by-interaction.html
(ii) The synaptic and non-synaptic (e.g. electric field) transmission of information describe the process of information communication. Both, synaptic and non-synaptic transmissions are the result of information processing within the cell.
(iii) The process of information communication between cells is a different phenomenon, it is not information processing within the cell as described in (i)
Hebb was unaware about this distinction between computation and information transmission; he was trained as a psychologist. Since action potentials are considered digital stereotype signals, the fundamental process of computation within the neuron (i) is strongly simplified to reinforce STD. Unfortunately, in computational neuroscience over the past 60 years solely this process of information transmission between cells (ii)has captured the main focus of the researches and few of them have acknowledged the important role of electric field in computation. Therefore, to understand the brain a different mindset is required [2].
Indeed, we need to understand the major parts, however more important is to understand how these parts interact with each other in an emerging system[3]. While many models in neuroscience have identified components involved in information processing, the emerging properties of the global system are still missing.
Neuroelectrodynamics reflect the current trend in systems biology to rethink and reshape biology-based models and to provide an inter-disciplinary study of brain computing function on a system level by integrating information over multiple scales from molecular level to brain rhythms using dynamical systems theory.
Here is an analogy that should clarify the difference between STD and NED.
Imagine that you can see smoke coming under your door. You know that somewhere close there is a fire. The fire has several characteristics: generates heat, smoke and the ash is the final compound after combustion. Always ,there is a correlation between the fire, the generated heat and the smoke. However, the smoke is an epiphenomenon of fire, as temporal patterns or the reorganizations of temporal patterns are epiphenomena of computing by interaction. The smoke vanishes in the air, so does the timing code. This phenomenon happens at all temporal scales, temporal spikes, spike-timing-dependent plasticity (STDP). The fire (interaction) transforms everything into ash, so does the interaction that changes the molecular structures, the “memory” which is built inside neurons.
Neural Communication or Neural Computation?
The research in communication theory highlights the Shannon Weaver model of information communication while in computer science, the basic model of computation is described by the Turing model. In case of Turing machines the difference between information communication and computation is clear:
(i) The communication of information occurs during transient events, can be actively modulated, however, this process of communication may not necessarily include information storage
(ii) The process of computation requires information communication, information processing and information storage. Example: Adding two values a and b requires information communication. Information regarding a and b is communicated to the arithmetic logic unit (ALU). Information is processed, the two values are added, communicated again and stored in memory. The simple process of communication of a and b values does not describe the entire process of computation.
(iii) Therefore the process of information communication modulated or not does not model the entire process of computation.
(iv) In order to describe computation the framework needs to clarify additional aspects, how information is processed and how is stored in the brain.
Neural computation: A major prediction from NED is that fragments of information regarding our memories are stored within molecular structures in proteins [2]. This prediction is consistent with several observations and theoretical models [12,13]. Since fragments of information are stored within biological substrate, many related processes that involve protein structures become part of information processing (e.g. folding, protein synthesis, protein transport). Therefore, within every neuron information is intracellularly processed. During AP generation, the information is bidirectionally, rapidly exchanged between molecular structures (e.g. proteins) and the electric flow (e.g ion fluxes) http://neuroelectrodynamics.blogspot.com/p/computing-by-interaction.html .
Here is an analogy that should clarify the difference between STD and NED.
Imagine that you can see smoke coming under your door. You know that somewhere close there is a fire. The fire has several characteristics: generates heat, smoke and the ash is the final compound after combustion. Always ,there is a correlation between the fire, the generated heat and the smoke. However, the smoke is an epiphenomenon of fire, as temporal patterns or the reorganizations of temporal patterns are epiphenomena of computing by interaction. The smoke vanishes in the air, so does the timing code. This phenomenon happens at all temporal scales, temporal spikes, spike-timing-dependent plasticity (STDP). The fire (interaction) transforms everything into ash, so does the interaction that changes the molecular structures, the “memory” which is built inside neurons.
Neural Communication or Neural Computation?
The research in communication theory highlights the Shannon Weaver model of information communication while in computer science, the basic model of computation is described by the Turing model. In case of Turing machines the difference between information communication and computation is clear:
(i) The communication of information occurs during transient events, can be actively modulated, however, this process of communication may not necessarily include information storage
(ii) The process of computation requires information communication, information processing and information storage. Example: Adding two values a and b requires information communication. Information regarding a and b is communicated to the arithmetic logic unit (ALU). Information is processed, the two values are added, communicated again and stored in memory. The simple process of communication of a and b values does not describe the entire process of computation.
(iii) Therefore the process of information communication modulated or not does not model the entire process of computation.
(iv) In order to describe computation the framework needs to clarify additional aspects, how information is processed and how is stored in the brain.
As presented today ‘neural computation’ describes a model of communication rather than the required model of computation. The synaptic weights and ‘connectivity’ express how often or the relative timing of communication between cells (see the STDP dogma). The information is temporally ‘coded’ and ‘decoded’, the entire process of communication is achieved through synaptic weight modulation.
Neural computation: A major prediction from NED is that fragments of information regarding our memories are stored within molecular structures in proteins [2]. This prediction is consistent with several observations and theoretical models [12,13]. Since fragments of information are stored within biological substrate, many related processes that involve protein structures become part of information processing (e.g. folding, protein synthesis, protein transport). Therefore, within every neuron information is intracellularly processed. During AP generation, the information is bidirectionally, rapidly exchanged between molecular structures (e.g. proteins) and the electric flow (e.g ion fluxes) http://neuroelectrodynamics.blogspot.com/p/computing-by-interaction.html .
If this strong process of interaction is rhythmically repeated it reorganizes the molecular structure and changes the inside states of the neuron. As a result fragments of information are stored in a topographic manner inside the cell within molecular structure [12]. Therefore, information is ‘written’ within the biological substrate that plays the role of ‘Turing tape’.
Synaptic Computation: Transient synaptic activities are part of ‘weak’ interactions when information is communicated between neurons. During synaptic activity (synaptic computation) information is communicated and can be stored in the biological substrate within synaptic proteins. The neurotransmitter interaction causes specific ion channels to open. Since the molecular structures embed information during the electric transport synaptic protein structures take part in computation. Again information is bidirectionally exchanged between proteins and the electric flow. In case of synaptic computation, the biological substrate represented by synaptic proteins play the‘Turing tape’ role.
Synaptic computations and computations that occur within the neuron are different processes. Information can be synaptically communicated between neurons without generating APs. The transient events (APs or synaptic activity) represent moments when information is ‘read’ or written from/in the molecular structure. The timing does not necessarily tell WHAT information is processed, communicated or stored. Similar temporal patterns may occur in very different neurons, it doesn’t mean they communicate or store the same information. Therefore, various theoretical models of temporal coding that have been presented as forms of ‘neural computation’ show hypothetical models of information communication. The real process of ‘neural computation’ occurs within neurons, the entire phenomenon of computation relates rhythmic fast transient events (APs) with slow changes in biological substrate where new information has to be stored. The reorganization at molecular level can significantly change temporal patterns, however recorded temporal patterns do not provide an approximation of computations that occur in neurons.
Synaptic computations and computations that occur within the neuron are different processes. Information can be synaptically communicated between neurons without generating APs. The transient events (APs or synaptic activity) represent moments when information is ‘read’ or written from/in the molecular structure. The timing does not necessarily tell WHAT information is processed, communicated or stored. Similar temporal patterns may occur in very different neurons, it doesn’t mean they communicate or store the same information. Therefore, various theoretical models of temporal coding that have been presented as forms of ‘neural computation’ show hypothetical models of information communication. The real process of ‘neural computation’ occurs within neurons, the entire phenomenon of computation relates rhythmic fast transient events (APs) with slow changes in biological substrate where new information has to be stored. The reorganization at molecular level can significantly change temporal patterns, however recorded temporal patterns do not provide an approximation of computations that occur in neurons.
The NED model represents a computational framework required to provide an understanding of the brain computing function on a systems level.
(i)The molecular structures preserve (store) information within neurons;
(ii) The information is rhythmically “read” and “written” by electrical interactions within neurons and transmitted synaptically and non- synaptically (electric field) during every spike (action potentials, synaptic activation);
(iii) This aspect explains the required rhythmic reading/writing of information from/in particular cells where specific information regarding meaningful events is stored - e.g “concept” cells, “place” cells, “mirror” neurons;
(iv)These rhythms are naturally correlated with exploratory external events (image presentation, T-maze tasks...);
(v)Therefore, the information embedded in molecular structures is "read" and carried out primarily through electric flux which allows a fast integration of information from many neurons that fire in different brain regions (hippocampus, striatum , prefrontal cortex ....);
(vii) The neurons that process, store and communicate information are just parts of the computing "device".
(viii) The computing machine is the entire system – the brain.
(ii) The information is rhythmically “read” and “written” by electrical interactions within neurons and transmitted synaptically and non- synaptically (electric field) during every spike (action potentials, synaptic activation);
(iii) This aspect explains the required rhythmic reading/writing of information from/in particular cells where specific information regarding meaningful events is stored - e.g “concept” cells, “place” cells, “mirror” neurons;
(iv)These rhythms are naturally correlated with exploratory external events (image presentation, T-maze tasks...);
(v)Therefore, the information embedded in molecular structures is "read" and carried out primarily through electric flux which allows a fast integration of information from many neurons that fire in different brain regions (hippocampus, striatum , prefrontal cortex ....);
(vii) The neurons that process, store and communicate information are just parts of the computing "device".
(viii) The computing machine is the entire system – the brain.
Every reliable theory has to generate experimentally testable predictions. Unfortunately, even necessarily postdictive explanations are missing in spike timing dogma.
Predictions from NED: Sparse coding?
The representations of sensorial events in somatosensory cortex (e.g. odors) evoke heterogeneous, sparse activity across the cortical population. The functional role of sparseness is highly debated in temporal coding paradigms. Often this phenomenon is used as an argument to replace previous firing rate models with the interspike interval (ISI) approach.
However, the very weak sparse response in somatosensory cortex shows that these cells can provide fast a meaningful response. This phenomenon proves an efficient process of computation that occurs within these cells rather than "sparse" temporal coding (Olshausen and Field, 1997). In this case it is expected that the temporal occurrence of action potentials provides little information regarding sensorial inputs, they may appear random since the fundamental nature of the code is different http://neuroelectrodynamics.blogspot.com/p/computing-by-interaction.html
Predictions from NED: Therefore, somatosensory cortex cells are ideal to prove that meaningful computations occur within neurons. Either intracellular or extracellular recordings (at least four electrodes, tetrodes) can 'read' what information is processed during every spike (compute spike directivity) http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html . Since the fundamental process of computation occurs inside the cell http://neuroelectrodynamics.blogspot.com/p/computing-by-interaction.html these phenomena will be observed only in few neurons close-by the electrode tips. These neurons will show significant meaningful changes in action potential propagation correlated to different types of sensorial inputs. This rich information that can be extracted from electrical patterns from single spikes will not appear in temporal patterns.
The Phantom Limb Explained
The NED model explains better what STD fails to clarify. All neurons can store different fragments of “memory” in their molecular structure. Some neurons can carry information about the lost limb in addition to Jennifer Aniston or “spider”, see http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html
Incidentally, fragments of memory of the lost limb are “read” or can be “written” during interaction inside the cell, when spikes are generated. This information is electrically integrated and the effect is an experience of phantom sensations. This experience can be triggered when other parts of the body have nearby representation within the same neurons e.g. the limb becomes responsive to stimuli applied to the face [7]. The phenomenon shows that the 'body image' is obtained by integrating information “read” from many neurons carried out by electromagnetic field. The illusion that they feel their limbs demonstrates the outcome of computing by interaction where information is electrically integrated. A similar phenomenon occurs in case of phantom limb pain where the pain memory decreases with time since these "memory" fragments are likely to be “re-written” at molecular level in neurons as the time passes by. In fact the “plasticity” represents a reorganization of information processing within neurons, not just the change at synapse level. Therefore, in this case the therapy that can reshape the formation of “memory” processes will be more effective than peripheral analgesia.
The NED model explains better what STD fails to clarify. All neurons can store different fragments of “memory” in their molecular structure. Some neurons can carry information about the lost limb in addition to Jennifer Aniston or “spider”, see http://neuroelectrodynamics.blogspot.com/p/spike-directivity.html
Incidentally, fragments of memory of the lost limb are “read” or can be “written” during interaction inside the cell, when spikes are generated. This information is electrically integrated and the effect is an experience of phantom sensations. This experience can be triggered when other parts of the body have nearby representation within the same neurons e.g. the limb becomes responsive to stimuli applied to the face [7]. The phenomenon shows that the 'body image' is obtained by integrating information “read” from many neurons carried out by electromagnetic field. The illusion that they feel their limbs demonstrates the outcome of computing by interaction where information is electrically integrated. A similar phenomenon occurs in case of phantom limb pain where the pain memory decreases with time since these "memory" fragments are likely to be “re-written” at molecular level in neurons as the time passes by. In fact the “plasticity” represents a reorganization of information processing within neurons, not just the change at synapse level. Therefore, in this case the therapy that can reshape the formation of “memory” processes will be more effective than peripheral analgesia.
The Mystery of Mirror Neurons
The discovery of neurons that respond both when the subject executes a class of actions and when the subject sees others similar action has led researchers to speculate in many connected fields from neuroscience to psychology, philosophy and even sociology[7][8]. With spike timing dogma the mystery of mirror neurons remained unexplained, however the explanation arise naturally in NED. We already know that all neurons store distinct various fragments of information. These fragments of information are stored in particular neurons at the molecular level and ‘read’ or ‘written’ during action potential propagation. The propagation of action potential and implicitly the so called “wiring” is a result of information processing (computation by interaction, http://neuroelectrodynamics.blogspot.com/p/computing-by-interaction.html )
During every spike information is transferred between molecular structures and the flow of electric charges when information is ‘read’ and between the flow of electric charges and molecular structures (e.g proteins) and information is ‘written’.
(i) The presence of adequate sensorial input is sufficient to trigger an entire process;
(ii) Far from being random the “wiring” between neurons is the result of information processing in neurons.
(iii) The propagation of action potential selectively points to a specific synaptic transmission and local propagation.
(iv) The synapses can modulate the transfer of information between neurons, only specific neurons are targeted
(v) Therefore, the entire propagation in the brain depends on WHAT information is processed.
(vi) Once the electric flow is triggered by a particular sensorial input (artificially electrically generated see Penfield, 1938 [9]) the path of propagation is activated based on input.
(vii) Since, key fragments of information stored in a certain neuron are needed either to perform motor actions or to “explain” actions executed by someone else, the neuron will fire in both cases.
(viii) This neuron that embeds (stores) such specific information is the mirror neuron.
(ix) Example: Since fragments regarding emotion like disgust are stored only in some neurons, they will be activated either the subject experiences this emotion or the emotion is perceived in others (mirror neurons for disgust).
(x) There are several types of mirror neurons, for motor action, for pain, for emotions... The existence of mirror neurons reinforce the model presented in NED where fragments of information regarding motor action, pain, emotion .... are stored in neurons;
(xi) In fact the “mirror” neuron is an effect of key information embodied within molecular structure;
(xii) In time, the reorganization of stored information at molecular level can make some ‘mirror’ neurons disappear and other ones to appear;
(i) The effect of mirroring in some cases can be stronger; the subject can take the role of the other.
Examples: With adequate induced stimuli the perceptual illusion of body swapping can be triggered [10]. The baby-feeding phenomenon is another example of mirror action. In both cases the subject embodies the behavior of the other during interaction (e.g.opens the mouth when the spoon nears the baby's mouth).
The system cannot afford to keep different groups of neurons, some for perception, others for motor action and another group to explain what happens in the environment. The mirror neuron story illustrates the fundamental principle of efficiency in information processing and storage in the brain ( see the principle of optimality in NED).
Predicting Alzheimer’s Disease
To understand the progression of Alzheimer's Disease one need to know where our memories are stored and how they are altered. The subject experiences short-term memory loss, confusion, semantic deficits that require longer periods of time to complete simple tasks. The NED model has predicted that fragments of information regarding semantic knowledge are stored in neurons in molecular structures (proteins). These fragments of information are ‘read’ or ‘written’ during the generation of APs and synaptic activities. The presence of structural order at a molecular level is required to preserve and accumulate new information in neurons. Various proteins represent an ideal biological substrate to embed fragments of information. In general proteins can maintain their structural order within arrays of amino acids, preferred spatial orientation of covalent bonds, specific spatial arrangement of electric charges and intraprotein electric fields.
(i) The loss of information can be generated by mechanisms that alter the life cycle of proteins, local aberrant protein degradation in neurons (Steward and Schuman, 2003)
(ii) Therefore, the control of protein degradation and protein synthesis is highly regulated within every neuron. An entire cycle from DNA to RNA through appropriate gene selection for protein synthesis is maintained in response to a wide variety of extracellular signals and electrical events (Pozo and Goda, 2010).
(iii) The process of biophysical interaction and new protein synthesis is built to preserve previous fragments of information and to accumulate new information over time. Correct protein folding and binding depends on the local environment where proteins are synthesized. These processes are critically important for information preservation within molecular structures. Always, information is transferred to newly formed structures and different electric rhythms, various neurotransmitters, hormones are involved in this process. Incompletely folded, misfolded proteins are required to establish a correct conformation or initiate the protein degradation process (Malgaroli et al., 2006). During sleep phases different categories of genes provide the needed support for new protein synthesis (Cirelli, et al., 2004).The internal molecular structure in neurons is continuously reshaped, the regulation and maintenance of the life cycle of proteins is a vital required process to preserve and incorporate new information.
Alzheimer’s Disease (AD) is systemically induced by metabolic or genetic risk factors (APP, PS1, PS2) that in different forms alter the life cycle of proteins, disrupt the unique repertoire of proteins secreted within neural cells and lead to distinct pathological features (e.g intracellular neurofibrillary tangles and extracellular plaques). Since similar fragments of information are distributed and stored within a large number of cells, few changes in a small number of neurons or the death of some cells do not necessarily generate memory loss or cognitive impairments.
Therefore, in order to determine significant effects:
(i) A large number of neurons have to be affected by the progression of disease
(ii) These changes in neurons have to generate a significant transformation of brain rhythms since the rhythm is critical to ‘read’ or integrate specific fragments of information
With aging additional factors accumulate and may trigger cellular dysfunctions and influence the life cycle of proteins:
(i) Even in absence of a specific disease, aging is associated with an increased aggregation of a large number of proteins (Kenyon, 2010). Since aggregated proteins are insoluble they determine changes in electrical characteristics in intraprotein electric fields that can alter how information is stored or transferred between the flow of charges and biological substrate during AP generation or synaptic activities. In addition with aging reduced periods of sleep may affect the process of writing fragments of information within molecular structures. Periods of sleep (REM, SWS) required to reshape the internal structure within neurons significantly decrease in Alzheimer's disease compared to age-matched control subjects (Onen and Onen, 2003).
(ii) Exaggerated Aβ production is correlated with aneuploidy induced by different mutant forms (e.g. amyl, PS proteins). The buildup of amyloid-β protein and tau disrupts the axonal transport and can limit the propagation of the signal only in few axonal branches during AP generation. The synaptic dysfunction can be determined by a local buildup of amyloid-β protein at synapses or related to accumulation of intraneuronal amyloid-β and disrupts APs propagation. This phenomenon that can occur only in some axonal branches has devastating effects on how spatial interactions with downstream neurons occur. Since in some regions the occurrence of interactions during AP propagation is diminished fragments of information embedded in specific biological substrate (e.g. axonal branch) cannot be ‘read’ during AP propagation (see the image, amyloid-β in yellow color, in a single axonal branch). A similar phenomenon can occur within dendritic branches and leads to preferred spike directivity propagation instead of a more uniform propagation of AP. If the development of amyloid-β protein or similar dysfunctions at protein level are generalized in many neurons then memory loss, cognitive impairments gradually occur.
A better control of AP propagation in these neurons can have positive effects and delay AD progression
Example: Learning and using a second language postpones the earlier onset of AD manifestations (Hernández et al.,2010). This phenomenon can be simply explained since bilingualism may require the activation of different paths of interaction within specific neurons, different axonal or dendritic branches during AP generation. Therefore, bilingualism involuntarily generates better control enhancement of spatial propagation of APs in neurons which can delay the development of intracellular neurofibrillary tangles and extracellular plaques.
(iii) In addition, this dysfunction in spatial activity (preferred spike directivity) influences the regulation of neuronal activity. A more uniform spatial propagation of APs generates smooth rhythms while a persistent preferred spike directivity leads to simultaneous action potential firing in targeted regions followed by chaotic dynamics.
Predicting Alzheimer’s Disease
To understand the progression of Alzheimer's Disease one need to know where our memories are stored and how they are altered. The subject experiences short-term memory loss, confusion, semantic deficits that require longer periods of time to complete simple tasks. The NED model has predicted that fragments of information regarding semantic knowledge are stored in neurons in molecular structures (proteins). These fragments of information are ‘read’ or ‘written’ during the generation of APs and synaptic activities. The presence of structural order at a molecular level is required to preserve and accumulate new information in neurons. Various proteins represent an ideal biological substrate to embed fragments of information. In general proteins can maintain their structural order within arrays of amino acids, preferred spatial orientation of covalent bonds, specific spatial arrangement of electric charges and intraprotein electric fields.
(i) The loss of information can be generated by mechanisms that alter the life cycle of proteins, local aberrant protein degradation in neurons (Steward and Schuman, 2003)
(ii) Therefore, the control of protein degradation and protein synthesis is highly regulated within every neuron. An entire cycle from DNA to RNA through appropriate gene selection for protein synthesis is maintained in response to a wide variety of extracellular signals and electrical events (Pozo and Goda, 2010).
(iii) The process of biophysical interaction and new protein synthesis is built to preserve previous fragments of information and to accumulate new information over time. Correct protein folding and binding depends on the local environment where proteins are synthesized. These processes are critically important for information preservation within molecular structures. Always, information is transferred to newly formed structures and different electric rhythms, various neurotransmitters, hormones are involved in this process. Incompletely folded, misfolded proteins are required to establish a correct conformation or initiate the protein degradation process (Malgaroli et al., 2006). During sleep phases different categories of genes provide the needed support for new protein synthesis (Cirelli, et al., 2004).The internal molecular structure in neurons is continuously reshaped, the regulation and maintenance of the life cycle of proteins is a vital required process to preserve and incorporate new information.
Alzheimer’s Disease (AD) is systemically induced by metabolic or genetic risk factors (APP, PS1, PS2) that in different forms alter the life cycle of proteins, disrupt the unique repertoire of proteins secreted within neural cells and lead to distinct pathological features (e.g intracellular neurofibrillary tangles and extracellular plaques). Since similar fragments of information are distributed and stored within a large number of cells, few changes in a small number of neurons or the death of some cells do not necessarily generate memory loss or cognitive impairments.
Therefore, in order to determine significant effects:
(i) A large number of neurons have to be affected by the progression of disease
(ii) These changes in neurons have to generate a significant transformation of brain rhythms since the rhythm is critical to ‘read’ or integrate specific fragments of information
With aging additional factors accumulate and may trigger cellular dysfunctions and influence the life cycle of proteins:
(i) Even in absence of a specific disease, aging is associated with an increased aggregation of a large number of proteins (Kenyon, 2010). Since aggregated proteins are insoluble they determine changes in electrical characteristics in intraprotein electric fields that can alter how information is stored or transferred between the flow of charges and biological substrate during AP generation or synaptic activities. In addition with aging reduced periods of sleep may affect the process of writing fragments of information within molecular structures. Periods of sleep (REM, SWS) required to reshape the internal structure within neurons significantly decrease in Alzheimer's disease compared to age-matched control subjects (Onen and Onen, 2003).
(ii) Exaggerated Aβ production is correlated with aneuploidy induced by different mutant forms (e.g. amyl, PS proteins). The buildup of amyloid-β protein and tau disrupts the axonal transport and can limit the propagation of the signal only in few axonal branches during AP generation. The synaptic dysfunction can be determined by a local buildup of amyloid-β protein at synapses or related to accumulation of intraneuronal amyloid-β and disrupts APs propagation. This phenomenon that can occur only in some axonal branches has devastating effects on how spatial interactions with downstream neurons occur. Since in some regions the occurrence of interactions during AP propagation is diminished fragments of information embedded in specific biological substrate (e.g. axonal branch) cannot be ‘read’ during AP propagation (see the image, amyloid-β in yellow color, in a single axonal branch). A similar phenomenon can occur within dendritic branches and leads to preferred spike directivity propagation instead of a more uniform propagation of AP. If the development of amyloid-β protein or similar dysfunctions at protein level are generalized in many neurons then memory loss, cognitive impairments gradually occur.
A better control of AP propagation in these neurons can have positive effects and delay AD progression
Example: Learning and using a second language postpones the earlier onset of AD manifestations (Hernández et al.,2010). This phenomenon can be simply explained since bilingualism may require the activation of different paths of interaction within specific neurons, different axonal or dendritic branches during AP generation. Therefore, bilingualism involuntarily generates better control enhancement of spatial propagation of APs in neurons which can delay the development of intracellular neurofibrillary tangles and extracellular plaques.
(iii) In addition, this dysfunction in spatial activity (preferred spike directivity) influences the regulation of neuronal activity. A more uniform spatial propagation of APs generates smooth rhythms while a persistent preferred spike directivity leads to simultaneous action potential firing in targeted regions followed by chaotic dynamics.
Since in response to prolonged periods with chaotic dynamics the homeostatic regulation forces neurons to fire simultaneously periods with abnormal neural synchronization are often observed in AD patients(Uhlhaas and Singer, 2006; Dauwels et al., 2010) and are followed by periods with chaotic dynamics since extended neural synchronization generates overlap of resonances which may lead again to periods with chaotic dynamics. These phenomena are similar to changes observed in brain rhythms in epilepsy, where contrary to common belief chaotic dynamics occur in the focal epileptogenic focus and persist two, three minutes right before the seizure is generated (Aur et al., 2010; Aur, 2011). The presence of persistent chaotic dynamics and aberrant synchrony changes intrinsic properties of electrical fields determine a rapid cascade of molecular events which influence complex signaling pathway that regulate protein phosphorylation, transcription ... In fact all these changes alter the level of individual proteins where information is stored (Hameroff et al., 2010)
Example: Alone, the significance of a particular change (e.g. Zn) may not always reveal the entire image, however a systemic alteration on various proteins ( ZnT proteins) and Zn homeostasis can have profound effects on information transfer at molecular scale. Besides the effects of Zn, copper or iron , severe dysfunctions of calcium signaling and homeostasis can change the life-cycle of various proteins
Therefore, any significant alteration in intercellular regulation can determine changes in structural order inside biological substrate may impair information storage, information communication within neurons, between neurons and generate a cascade of events that triggers on a systems scale the pathogenesis of AD. A more systemic approach is required to analyze the impact of diverse factors that can generate a progression of AD . This relationship between structural changes at molecular level, preferred propagation of APs and the occurrence of abnormal rhythms (persistent chaotic dynamics, abnormal synchrony) is present in many other neurological disorders (e.g. Parkinson) and even in non-cognitive deficits, mood, psychotic ideas and experiences. However, many current approaches for treating the symptoms associated with AD are inadequate, they do not alleviate major effects on life cycle of proteins and resulting global emerging cognitive deficits.
A Systemic Model
The NED model was recently applied to explain and predict seizure generation with high accuracy, a problem that has not been solved by STD or various neuronal models over the last twenty years [4].
A large number of neurological disorders (Epilepsy, Alzheimer and Parkinson) can lead to marked changes in cognition and behavior. All these alterations can be explained as changes in interaction between different subsystems following the basic scheme described in NED:
This systemic model provides the required theoretical approach to understand the mechanisms underlying neurological disorders such as epilepsy, Alzheimer’s, and Parkinson’s Disease as well as depression [5].
References
1.Henry Markram, 2006, The Blue Brain Project, Nature Reviews Neuroscience 7, 153-160
2.Dorian Aur and Mandar Jog 2010, Neuroelectrodynamics- Understanding The Brain Language , IOS Press, http://dx.doi.org/10.3233/978-1-60750-473-3-i
3.Dorian Aur, 2011, From Neuroelectrodynamics to Thinking Machines, DOI: 10.1007/s12559-011-9106-3, Cognitive Computation, http://www.springerlink.com/content/x1l7388475323758/
4.Dorian Aur, Understanding the Physical Mechanism of Transition to Epileptic Seizures , Journal of Neuroscience Methods, Vol. 200, Issue 1, 2011, Pages 80-85
5. Dorian Aur, Mark Bower, Paul Buckmaster, Izumi Toyoda, System and Method for Seizure Prediction and Treatment of Epileptic Seizures, Stanford Reference: S10-419, http://otlportal.stanford.edu/techfinder/technology/ID=28515
6. Dorian Aur, Mandar Jog, 2011Why NeuroElectroDynamics is Better than Spike timing Models, IJCNN,P367, pp 111
7.Giacomo Rizzolatti (2008). Mirrors in the Brain: How our Minds Share Actions, Emotions. Oxford ; New York : Oxford University Press.
8. Lindsay M. Oberman, Edward M. Hubbard, Joseph P. McCleery, Eric L. Altschuler, Vilayanur S. Ramachandran, Jaime A. Pined, EEG evidence for mirror neuron dysfunction in autism spectrum disorders, Cognitive Brain Research, Volume 24, Issue 2, July 2005, Pages 190-198
9.Wilder Penfield, (1937) Somatic Motor And Sensory Representation In The Cerebral Cortex Of Man As Studied By Electrical Stimulation Brain 60(4): 389-443
10.Petkova, V.I., Ehrsson, H.H. 2008 If I were you: Perceptual illusion of body swapping PLoS ONE 3 (12), art. no. e3832 37
11. Olshausen BA; Field DJ, 1997 Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research Volume: 37 Issue: 23 Pages: 3311-3325
References
1.Henry Markram, 2006, The Blue Brain Project, Nature Reviews Neuroscience 7, 153-160
2.Dorian Aur and Mandar Jog 2010, Neuroelectrodynamics- Understanding The Brain Language , IOS Press, http://dx.doi.org/10.3233/978-1-60750-473-3-i
3.Dorian Aur, 2011, From Neuroelectrodynamics to Thinking Machines, DOI: 10.1007/s12559-011-9106-3, Cognitive Computation, http://www.springerlink.com/content/x1l7388475323758/
4.Dorian Aur, Understanding the Physical Mechanism of Transition to Epileptic Seizures , Journal of Neuroscience Methods, Vol. 200, Issue 1, 2011, Pages 80-85
5. Dorian Aur, Mark Bower, Paul Buckmaster, Izumi Toyoda, System and Method for Seizure Prediction and Treatment of Epileptic Seizures, Stanford Reference: S10-419, http://otlportal.stanford.edu/techfinder/technology/ID=28515
6. Dorian Aur, Mandar Jog, 2011Why NeuroElectroDynamics is Better than Spike timing Models, IJCNN,P367, pp 111
7.Giacomo Rizzolatti (2008). Mirrors in the Brain: How our Minds Share Actions, Emotions. Oxford ; New York : Oxford University Press.
8. Lindsay M. Oberman, Edward M. Hubbard, Joseph P. McCleery, Eric L. Altschuler, Vilayanur S. Ramachandran, Jaime A. Pined, EEG evidence for mirror neuron dysfunction in autism spectrum disorders, Cognitive Brain Research, Volume 24, Issue 2, July 2005, Pages 190-198
9.Wilder Penfield, (1937) Somatic Motor And Sensory Representation In The Cerebral Cortex Of Man As Studied By Electrical Stimulation Brain 60(4): 389-443
10.Petkova, V.I., Ehrsson, H.H. 2008 If I were you: Perceptual illusion of body swapping PLoS ONE 3 (12), art. no. e3832 37
11. Olshausen BA; Field DJ, 1997 Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research Volume: 37 Issue: 23 Pages: 3311-3325
12. Stuart Hameroff, Nip Alex, Mitchell Porter, Jack Tuszynski, 2002, Conduction pathways in microtubules, biological quantum computation, and consciousness. Biosystems 64, , pp. 149–168.
13. Nancy J. Woolf, Avner Priel Jack A. Tuszynski, 2009, Nanoscience:Structural and Functional Roles of the Neuronal Cytoskeleton in Health and Disease, Springer Verlag;
14.Cirelli, C., Gutierrez, C.M., Tononi, G., (2004) Extensive and Divergent Effects of Sleep and
Wakefulness on Brain Gene Expression, Neuron 41 (1), pp. 35-43, 128.
15. Maret, W. 2005 Zinc coordination environments in proteins determine zinc functions Journal of Trace Elements in Medicine and Biology 19 (1 SPEC. ISS.), pp. 7-12
16.Liu, J., Jiang, Y.-G., Huang, C.-Y., Fang, H.-Y., Fang, H.-T., Pang, W. 2008 Depletion of intracellular zinc down-regulates expression of Uch-L1 mRNA and protein, and CREB mRNA in cultured hippocampal neurons, Nutritional Neuroscience 11 (3), pp. 96-102
17 Pozo K., Goda Y. 2010 Unraveling mechanisms of homeostatic synaptic plasticity
Neuron, 66 (3), pp. 337-351.)
18. David Della C.; Ollikainen Noah; Trinidad Jonathan C, MP Cary, AL Burlingame, C Kenyon, 2010, Widespread Protein Aggregation as an Inherent Part of Aging in C. elegans
PLOS BIOLOGY Vol: 8 Issue: 8
19. Dauwels, J., Vialatte, F., Cichocki, A. 2010 Diagnosis of Alzheimer's disease from EEG signals: Where are we standing? Current Alzheimer Research 7 (6), pp. 487-505
20. Uhlhaas, P.J., Singer, W. 2006, Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology Neuron 52 (1), pp. 155-168
21.Malgaroli, A., Vallar, L., Zimarino, V. 2006, Protein homeostasis in neurons and its pathological alterations Current Opinion in Neurobiology 16 (3), pp. 270-274
22. Hameroff SR. , Craddock T.J.A. and J.A. Tuszynski, 'Memory bytes' - A molecular match for CaMKII phosphorylation encoding of microtubule lattices, Journal of Integrative Neuroscience vol. 9, no. 3 Sept. (2010) 253-67.
23.Hernández, M., Costa, A., Caño, A., Juncadella, M., Gascón-Bayarri, J. 2010On the translation routes in early and highly proficient bilingual people: Evidence from an individual with semantic impairment, Aphasiology 24 (2), pp. 141-169
24.Onen, F., Onen, S.-H., 2003, Sleep rhythm disturbances in Alzheimer's disease Altérations des rythmes du sommeil dans la maladie d'Alzheimer Revue de Medecine Interne 24 (3), pp. 165-171
13. Nancy J. Woolf, Avner Priel Jack A. Tuszynski, 2009, Nanoscience:Structural and Functional Roles of the Neuronal Cytoskeleton in Health and Disease, Springer Verlag;
14.Cirelli, C., Gutierrez, C.M., Tononi, G., (2004) Extensive and Divergent Effects of Sleep and
Wakefulness on Brain Gene Expression, Neuron 41 (1), pp. 35-43, 128.
15. Maret, W. 2005 Zinc coordination environments in proteins determine zinc functions Journal of Trace Elements in Medicine and Biology 19 (1 SPEC. ISS.), pp. 7-12
16.Liu, J., Jiang, Y.-G., Huang, C.-Y., Fang, H.-Y., Fang, H.-T., Pang, W. 2008 Depletion of intracellular zinc down-regulates expression of Uch-L1 mRNA and protein, and CREB mRNA in cultured hippocampal neurons, Nutritional Neuroscience 11 (3), pp. 96-102
17 Pozo K., Goda Y. 2010 Unraveling mechanisms of homeostatic synaptic plasticity
Neuron, 66 (3), pp. 337-351.)
18. David Della C.; Ollikainen Noah; Trinidad Jonathan C, MP Cary, AL Burlingame, C Kenyon, 2010, Widespread Protein Aggregation as an Inherent Part of Aging in C. elegans
PLOS BIOLOGY Vol: 8 Issue: 8
19. Dauwels, J., Vialatte, F., Cichocki, A. 2010 Diagnosis of Alzheimer's disease from EEG signals: Where are we standing? Current Alzheimer Research 7 (6), pp. 487-505
20. Uhlhaas, P.J., Singer, W. 2006, Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology Neuron 52 (1), pp. 155-168
21.Malgaroli, A., Vallar, L., Zimarino, V. 2006, Protein homeostasis in neurons and its pathological alterations Current Opinion in Neurobiology 16 (3), pp. 270-274
22. Hameroff SR. , Craddock T.J.A. and J.A. Tuszynski, 'Memory bytes' - A molecular match for CaMKII phosphorylation encoding of microtubule lattices, Journal of Integrative Neuroscience vol. 9, no. 3 Sept. (2010) 253-67.
23.Hernández, M., Costa, A., Caño, A., Juncadella, M., Gascón-Bayarri, J. 2010On the translation routes in early and highly proficient bilingual people: Evidence from an individual with semantic impairment, Aphasiology 24 (2), pp. 141-169
24.Onen, F., Onen, S.-H., 2003, Sleep rhythm disturbances in Alzheimer's disease Altérations des rythmes du sommeil dans la maladie d'Alzheimer Revue de Medecine Interne 24 (3), pp. 165-171
No comments:
Post a Comment