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'''Computational neurogenetic modeling (CNGM)''' is concerned with the study and development of dynamic [[neuronal modeling|neuronal models]] for modeling brain functions with respect to [[gene]]s and dynamic interactions between genes. These include [[neural network]] models and their integration with gene network models. This area brings together knowledge from various scientific disciplines, such as [[computer science|computer]] and [[information science]], [[neuroscience]] and [[cognitive science]], [[genetics]] and [[molecular biology]], as well as [[engineering]]. | |||
== Levels of processing == | |||
=== Molecular kinetics === | |||
Models of the [[Chemical_kinetics|kinetics]] of proteins and [[ion channels]] associated with [[neuron]] activity represent the lowest level of modeling in a computational neurogenetic model. The altered activity of proteins in some diseases, such as the [[amyloid beta]] protein in [[Alzheimer's disease]], must be modeled at the molecular level to accurately predict the effect on cognition.<ref name = pCNGM_AD >{{cite journal|last=Kasabov, Schliebs, Kojima|first=Nikola K., Reinhard, Hiroshi|title=Probabilistic Computational Neurogenetic Modeling: From Cognitive Systems to Alzheimer's Disease.|journal=Ieee Transactions on Autonomous Mental Development|year=2011|volume=3|issue=4|pages=300–311. http://dx.doi.org/10.1109/TAMD.2011.2159839}}</ref> Ion channels, which are vital to the propagation of [[action potentials]], are another molecule that may be modeled to more accurately reflect biological processes. For instance, to accurately model [[synaptic plasticity]] (the strengthening or weakening of [[synapse|synapses]]) and memory, it is necessary to model the activity of the [[NMDA receptor]] (NMDAR). The speed at which the NMDA receptor lets Calcium ions into the cell in response to [[Glutamate]] is an important determinant of [[Long-term potentiation]] via the insertion of [[AMPA receptor]]s (AMPAR) into the [[plasma membrane]] at the synapse of the postsynaptic cell (the cell that receives the neurotransmitters from the presynaptic cell).<ref name = book /> | |||
=== Genetic regulatory network === | |||
[[Image:WagnerModel.png|thumb|right|An example of a [[Wagner's gene network model|model]] of a gene network. The genes, ''G''<sub>1</sub> through ''G''<sub>4</sub>, are modified by either inhibitory signals, represented by bars and negative coefficients, or excitatory signals, represented by arrows and positive coefficients. The interactions are represented numerically by the matrix on the right, '''R'''.]] | |||
In most models of neural systems neurons are the most basic unit modeled.<ref name = book /> In computational neurogenetic modeling, to better simulate processes that are responsible for synaptic activity and connectivity, the genes responsible are modeled for each [[neuron]]. | |||
A [[gene regulatory network]], protein regulatory network, or gene/protein regulatory network, is the level of processing in a computational neurogenetic model that models the interactions of [[gene|genes]] and proteins relevant to synaptic activity and general cell functions. Genes and proteins are modeled as individual [[Node_(graph_theory)|nodes]], and the interactions that influence a gene are modeled as excitatory (increases gene/protein expression) or inhibitory (decreases gene/protein expression) inputs that are weighted to reflect the effect a gene or protein is having on another gene or protein. Gene regulatory networks are typically designed using data from [[DNA microarrays|microarrays]].<ref name = book /> | |||
Modeling of genes and proteins allows individual responses of neurons in an artificial neural network that mimic responses in biological nervous systems, such as division (adding new neurons to the artificial neural network), creation of proteins to expand their cell membrane and foster [[neurite]] outgrowth (and thus stronger connections with other neurons), up-regulate or down-regulate receptors at synapses (increasing or decreasing the weight (strength) of synaptic inputs), uptake more [[neurotransmitters]], change into different types of neurons, or die due to [[necrosis]] or [[apoptosis]]. The creation and analysis of these networks can be divided into two sub-areas of research: the | |||
gene up-regulation that is involved in the normal functions of a neuron, such as growth, metabolism, and synapsing; and the effects of mutated genes on neurons and cognitive functions.<ref name = gene>Benuskova, L.; Kasabov, N. (2008). "Modeling brain dynamics using computational neurogenetic approach". Cognitive Neurodynamics 2 (4): 319–334. http://dx.doi.org/10.1007/s11571-008-9061-1</ref> | |||
=== Artificial neural network === | |||
[[Image:artificial neuron.png|thumb|right|A [[Artificial neuron|model]] of an individual neuron. The inputs, ''x''<sub>0</sub> to ''x''<sub>''m''</sub>, are modified by the input weights, ''w''<sub>0</sub> to ''w''<sub>''m''</sub>, and then combined into one input, ''v''<sub>k</sub>. The transfer function, <math>\varphi</math>, then uses this input to determine the output, ''y''<sub>k</sub>.]] | |||
An [[artificial neural network]] generally refers to any computational model that mimics the [[central nervous system]], with capabilities such as learning and pattern recognition. With regards to computational neurogenetic modeling, however, it is often used to refer to those specifically designed for biological accuracy rather than computational efficiency. Individual neurons are the basic unit of an artificial neural network, with each neuron acting as a node. Each node receives weighted signals from other nodes that are either [[excitatory postsynaptic potential|excitatory]] or [[Inhibitory postsynaptic potential|inhibitory]]. To determine the output, a [[transfer function]] (or [[activation function]]) evaluates the sum of the weighted signals and, in some artificial neural networks, their input rate. Signal weights are strengthened ([[long-term potentiation]]) or weakened ([[long-term depression]]) depending on how synchronous the presynaptic and postsynaptic activation rates are ([[Hebbian theory]]).<ref name = book /> | |||
The synaptic activity of individual neurons is modeled using equations to determine the temporal (and in some | |||
cases, spatial) summation of synaptic signals,[[membrane potential]], threshold for action potential | |||
generation, the absolute and relative [[Refractory period (physiology)|refractory period]], and optionally ion receptor channel [[Chemical_kinetics|kinetics]] and [[Gaussian noise]] (to increase biological accuracy by incorporation of random elements). In addition to connectivity, some types of artificial neural networks, such as [[spiking neural network|spiking neural networks]], also model the distance between neurons, and its effect on the synaptic weight (the strength of a synaptic transmission).<ref name=CN_sum /> | |||
=== Combining gene regulatory networks and artificial neural networks === | |||
For the parameters in the gene regulatory network to affect the neurons in the artificial neural network as intended there must be some connection between them. In an organizational context, each node (neuron) in the artificial neural network has its own gene regulatory network associated with it. The weights (and in some networks, frequencies of synaptic transmission to the node), and the resulting membrane potential of the node (including whether an [[action potential]] is produced or not), affect the expression of different genes in the gene regulatory network. Factors affecting connections between neurons, such as [[synaptic plasticity]], can be modeled by inputting the values of synaptic activity-associated genes and proteins to a function that re-evaluates the weight of an input from a particular neuron in the artificial neural network. | |||
=== Incorporation of other cell types === | |||
Other cell types besides neurons can be modeled as well. [[Glial cells]], such as [[astroglia]] and [[microglia]], as well as [[endothelial cells]], could be included in an artificial neural network. This would enable modeling of diseases where pathological effects may occur from sources other than neurons, such as Alzheimer's disease.<ref name = pCNGM_AD /> | |||
== Factors affecting choice of artificial neural network == | |||
While the term artificial neural network is usually used in computational neurogenetic modeling to refer to models of the central nervous system meant to possess biological accuracy, the general use of the term can be applied to many gene regulatory networks as well. | |||
=== Time variance === | |||
Artificial neural networks, depending on type, may or may not take into account the timing of inputs. Those that do, such as [[spiking neural network|spiking neural networks]], fire only when the pooled inputs reach a membrane potential is reached. Because this mimics the firing of biological neurons, spiking neural networks are viewed as a more biologically accurate model of synaptic activity.<ref name = book /> | |||
=== Growth and shrinkage === | |||
To accurately model the central nervous system, creation and death of neurons should be modeled as well.<ref name = book /> To accomplish this, constructive artificial neural networks that are able to grow or shrink to adapt to inputs are often used. [[Evolving classification function|Evolving connectionist systems]] are a subtype of constructive artificial neural networks ([[Evolving classification function|evolving]] in this case referring to changing the structure of its neural network rather than [[Evolutionary_computation|by mutation and natural selection]]).<ref name = ECoS /> | |||
=== Randomness === | |||
Both synaptic transmission and gene-protein interactions are [[Stochastic_process|stochastic]] in nature. To model biological nervous systems with greater fidelity some form of randomness is often introduced into the network. Artificial neural networks modified in this manner are often labeled as probabilistic versions of their neural network sub-type (e.g., p[[spiking neural network|SNN]]).<ref name = pSNN>Kasabov, N.; Schliebs, S., Mohemmed, A. (2012). "Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling". Computational Intelligence Methods for Bioinformatics and Biostatistics 7548: 1-9. http://dx.doi.org/10.1007/978-3-642-35686-5_1</ref> | |||
=== Incorporation of fuzzy logic === | |||
[[Fuzzy logic]] is a system of reasoning that enables an artificial neural network to deal in non-[[Binary data|binary]] and linguistic variables. Biological data is often unable to be processed using [[Boolean logic]], and moreover accurate modeling of the capabilities of biological nervous systems requires fuzzy logic. Therefore, artificial neural networks that incorporate it, such as [[Evolving classification function|evolving fuzzy neural networks (EFuNN) or Dynamic Evolving Neural-Fuzzy Inference Systems (DENFIS)]], are often used in computational neurogenetic modeling. The use of fuzzy logic is especially relevant in gene regulatory networks, as the modeling of protein binding strength often requires non-binary variables.<ref name = book /><ref name = ECoS>Watts, Michael J. (2009). "A Decade of Kasabov’s Evolving Connectionist Systems: A Review". IEEE Transactions on Systems, Man, And Cybernetics—Part C: Applications and Reviews 39 (3): 253-269. http://dx.doi.org/10.1109/TSMCC.2008.2012254</ref> | |||
=== Types of learning === | |||
Artificial Neural Networks designed to simulate of the human brain require an ability to learn a variety of tasks that is not required by those designed to accomplish a specific task. [[Supervised learning]] is a mechanism by which an artificial neural network can learn by receiving a number of inputs with a correct output already known. An example of an artificial neural network that uses supervised learning is a [[multilayer perceptron]] (MLP). In [[unsupervised learning]], an artificial neural network is trained using only inputs. Unsupervised learning is the learning mechanism by which a type of artificial neural network known as a [[self-organizing map]] (SOM) learns. Some types of artificial neural network, such as evolving connectionist systems, can learn in both a supervised and unsupervised manner.<ref name = book /> | |||
== Improvement == | |||
Both gene regulatory networks and artificial neural networks have two main strategies for improving their accuracy. In both cases the output of the network is measured against known biological data using some function, and subsequent improvements are made by altering the structure of the network. A common test of accuracy for artificial neural networks is to compare some parameter of the model to data acquired from biological neural systems, such as from an [[EEG]].<ref name = EEG>Benuskova, L.; Wysoski, S. G., Kasabov, N. (16-21). "Computational Neurogenetic Modeling: A Methodology to Study Gene Interactions Underlying Neural Oscillations". 2006 International Joint Conference on Neural Networks. http://dx.doi.org/10.1109/IJCNN.2006.1716743</ref> In the case of EEG recordings, the [[local field potential]] (LFP) of the artificial neural network is taken and compared to EEG data acquired from human patients. The [[relative intensity ratios|relative intensity ratio]] (RIRs) and [[fast Fourier transform]] (FFT) of the EEG are compared with those generated by the artificial neural networks to determine the accuracy of the model.<ref name = FFT>Kasabov, N.; Benuskova, L., Wysoski, S. G. (2005). "Computational neurogenetic modeling: Integration of spiking neural networks, gene networks, and signal processing techniques.". Artificial Neural Networks: Formal Models and Their Applications - Icann 2005, Pt 2, Proceedings 3697: 509–514. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.5223&rep=rep1&type=pdf</ref> | |||
=== Genetic algorithm === | |||
[[File:A-Numerical-Approach-to-Ion-Channel-Modelling-Using-Whole-Cell-Voltage-Clamp-Recordings-and-a-pcbi.0030169.sv001.ogv|thumb|right|An example of a model being refined through successive generations, using a genetic algorithm, to match experimental data.]] | |||
Because the amount of data on the interplay of genes and neurons and their effects is not enough to construct a rigorous model, | |||
[[evolutionary computation]] is used to optimize artificial neural networks and gene regulatory networks, a common technique being the [[genetic algorithm]]. A genetic algorithm is a process that can be used to refine models by mimicking the process of natural selection observed in biological ecosystems. The primary advantages are that, due to not requiring derivative information, it can be applied to [[black box]] problems and [[Evolutionary multimodal optimization|multimodal optimization]]. The typical process for using genetic algorithms to refine a gene | |||
regulatory network is: first, create a population; next, to create offspring via a crossover operation and | |||
evaluate their fitness; then, on a group chosen for high fitness, simulate mutation via a mutation operator; | |||
finally, taking the now mutated group, repeat this process until a desired level of fitness is demonstrated. | |||
<ref name = evol>Kasabov, N. (2006). "Neuro-, genetic-, and quantum inspired evolving intelligent systems". International Symposium on Evolving Fuzzy Systems, Proceedings: 63–73. http://dx.doi.org/10.1109/ISEFS.2006.251165</ref> | |||
=== Evolving systems === | |||
Methods by which artificial neural networks may alter their structure without simulated mutation and fitness selection have been developed. A [[Evolving classification function|dynamically evolving neural network]] is one approach, as the creation of new connections and new neurons can | |||
be modeled as the system adapts to new data. This enables the network to evolve in modeling accuracy without simulated natural selection. One method by which dynamically evolving networks may be optimized, called evolving layer neuron aggregation, combines neurons with sufficiently similar input weights into one neuron. This can take place during the training of the network, referred to as online aggregation, or between periods of training, referred to as offline aggregation. Experiments have suggested that offline aggregation is more efficient.<ref name = ECoS /> | |||
== Potential applications == | |||
A variety of potential applications have been suggested for accurate computational neurogenetic models, such as simulating genetic diseases, examining the impact of potential treatments,<ref name = nano>Kasabov, N.; Benuskova, L., Wysoski, S. G. (2005). "Biologically plausible computational neurogenetic models: Modeling the interaction between genes, neurons and neural networks". Journal of Computational and Theoretical Nanoscience 2 (4): 569–573. http://www.ingentaconnect.com/content/asp/jctn/2005/00000002/00000004/art00012</ref> better understanding of learning and cognition,<ref name = sci>{{cite journal|last=Benuskova, Jain, Wysoski, Kasabov|first=L., V., S. G., N. K.|title=Computational neurogenetic modelling: A pathway to new discoveries in genetic neuroscience|journal=International Journal of Neural Systems|year=2006|volume=16(2)|pages=47–61|pmid=17044242}}</ref> and development of hardware able to interface with neurons.<ref name=CN_sum>{{cite journal|last=Kasabov, Benuskova|first=Nikola, Lubica|title=Computational Neurogenetics|journal=Journal of Computational and Theoretical Nanoscience|year=2004|volume=1|pages=47–61. http://dx.doi.org/10.1166/jctn.2004.006}}</ref> | |||
The simulation of disease states is of particular interest, as modeling both the neurons and their genes and proteins allows linking genetic mutations and protein abnormalities to pathological effects in the central nervous system. Among those diseases suggested as being possible targets of computational neurogenetic modeling based analysis are epilepsy, schizophrenia, mental retardation, brain aging and Alzheimer's disease, and Parkinson's disease.<ref name = book>Benuskova, Kasabov, Lubica, Nikola (2007). Computational Neurogenetic Modeling. New Yorn: Springer. ISBN 0-387-48353-5.</ref> | |||
== See also == | |||
* [[Memristor]] | |||
== References == | |||
{{reflist}} | |||
== External links == | |||
*http://ecos.watts.net.nz/Algorithms/ | |||
[[Category:Cognitive science]] | |||
[[Category:Neural networks]] | |||
Latest revision as of 09:14, 9 January 2013
Computational neurogenetic modeling (CNGM) is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.
Levels of processing
Molecular kinetics
Models of the kinetics of proteins and ion channels associated with neuron activity represent the lowest level of modeling in a computational neurogenetic model. The altered activity of proteins in some diseases, such as the amyloid beta protein in Alzheimer's disease, must be modeled at the molecular level to accurately predict the effect on cognition.[1] Ion channels, which are vital to the propagation of action potentials, are another molecule that may be modeled to more accurately reflect biological processes. For instance, to accurately model synaptic plasticity (the strengthening or weakening of synapses) and memory, it is necessary to model the activity of the NMDA receptor (NMDAR). The speed at which the NMDA receptor lets Calcium ions into the cell in response to Glutamate is an important determinant of Long-term potentiation via the insertion of AMPA receptors (AMPAR) into the plasma membrane at the synapse of the postsynaptic cell (the cell that receives the neurotransmitters from the presynaptic cell).[2]
Genetic regulatory network
In most models of neural systems neurons are the most basic unit modeled.[2] In computational neurogenetic modeling, to better simulate processes that are responsible for synaptic activity and connectivity, the genes responsible are modeled for each neuron.
A gene regulatory network, protein regulatory network, or gene/protein regulatory network, is the level of processing in a computational neurogenetic model that models the interactions of genes and proteins relevant to synaptic activity and general cell functions. Genes and proteins are modeled as individual nodes, and the interactions that influence a gene are modeled as excitatory (increases gene/protein expression) or inhibitory (decreases gene/protein expression) inputs that are weighted to reflect the effect a gene or protein is having on another gene or protein. Gene regulatory networks are typically designed using data from microarrays.[2]
Modeling of genes and proteins allows individual responses of neurons in an artificial neural network that mimic responses in biological nervous systems, such as division (adding new neurons to the artificial neural network), creation of proteins to expand their cell membrane and foster neurite outgrowth (and thus stronger connections with other neurons), up-regulate or down-regulate receptors at synapses (increasing or decreasing the weight (strength) of synaptic inputs), uptake more neurotransmitters, change into different types of neurons, or die due to necrosis or apoptosis. The creation and analysis of these networks can be divided into two sub-areas of research: the gene up-regulation that is involved in the normal functions of a neuron, such as growth, metabolism, and synapsing; and the effects of mutated genes on neurons and cognitive functions.[3]
Artificial neural network
An artificial neural network generally refers to any computational model that mimics the central nervous system, with capabilities such as learning and pattern recognition. With regards to computational neurogenetic modeling, however, it is often used to refer to those specifically designed for biological accuracy rather than computational efficiency. Individual neurons are the basic unit of an artificial neural network, with each neuron acting as a node. Each node receives weighted signals from other nodes that are either excitatory or inhibitory. To determine the output, a transfer function (or activation function) evaluates the sum of the weighted signals and, in some artificial neural networks, their input rate. Signal weights are strengthened (long-term potentiation) or weakened (long-term depression) depending on how synchronous the presynaptic and postsynaptic activation rates are (Hebbian theory).[2]
The synaptic activity of individual neurons is modeled using equations to determine the temporal (and in some cases, spatial) summation of synaptic signals,membrane potential, threshold for action potential generation, the absolute and relative refractory period, and optionally ion receptor channel kinetics and Gaussian noise (to increase biological accuracy by incorporation of random elements). In addition to connectivity, some types of artificial neural networks, such as spiking neural networks, also model the distance between neurons, and its effect on the synaptic weight (the strength of a synaptic transmission).[4]
Combining gene regulatory networks and artificial neural networks
For the parameters in the gene regulatory network to affect the neurons in the artificial neural network as intended there must be some connection between them. In an organizational context, each node (neuron) in the artificial neural network has its own gene regulatory network associated with it. The weights (and in some networks, frequencies of synaptic transmission to the node), and the resulting membrane potential of the node (including whether an action potential is produced or not), affect the expression of different genes in the gene regulatory network. Factors affecting connections between neurons, such as synaptic plasticity, can be modeled by inputting the values of synaptic activity-associated genes and proteins to a function that re-evaluates the weight of an input from a particular neuron in the artificial neural network.
Incorporation of other cell types
Other cell types besides neurons can be modeled as well. Glial cells, such as astroglia and microglia, as well as endothelial cells, could be included in an artificial neural network. This would enable modeling of diseases where pathological effects may occur from sources other than neurons, such as Alzheimer's disease.[1]
Factors affecting choice of artificial neural network
While the term artificial neural network is usually used in computational neurogenetic modeling to refer to models of the central nervous system meant to possess biological accuracy, the general use of the term can be applied to many gene regulatory networks as well.
Time variance
Artificial neural networks, depending on type, may or may not take into account the timing of inputs. Those that do, such as spiking neural networks, fire only when the pooled inputs reach a membrane potential is reached. Because this mimics the firing of biological neurons, spiking neural networks are viewed as a more biologically accurate model of synaptic activity.[2]
Growth and shrinkage
To accurately model the central nervous system, creation and death of neurons should be modeled as well.[2] To accomplish this, constructive artificial neural networks that are able to grow or shrink to adapt to inputs are often used. Evolving connectionist systems are a subtype of constructive artificial neural networks (evolving in this case referring to changing the structure of its neural network rather than by mutation and natural selection).[5]
Randomness
Both synaptic transmission and gene-protein interactions are stochastic in nature. To model biological nervous systems with greater fidelity some form of randomness is often introduced into the network. Artificial neural networks modified in this manner are often labeled as probabilistic versions of their neural network sub-type (e.g., pSNN).[6]
Incorporation of fuzzy logic
Fuzzy logic is a system of reasoning that enables an artificial neural network to deal in non-binary and linguistic variables. Biological data is often unable to be processed using Boolean logic, and moreover accurate modeling of the capabilities of biological nervous systems requires fuzzy logic. Therefore, artificial neural networks that incorporate it, such as evolving fuzzy neural networks (EFuNN) or Dynamic Evolving Neural-Fuzzy Inference Systems (DENFIS), are often used in computational neurogenetic modeling. The use of fuzzy logic is especially relevant in gene regulatory networks, as the modeling of protein binding strength often requires non-binary variables.[2][5]
Types of learning
Artificial Neural Networks designed to simulate of the human brain require an ability to learn a variety of tasks that is not required by those designed to accomplish a specific task. Supervised learning is a mechanism by which an artificial neural network can learn by receiving a number of inputs with a correct output already known. An example of an artificial neural network that uses supervised learning is a multilayer perceptron (MLP). In unsupervised learning, an artificial neural network is trained using only inputs. Unsupervised learning is the learning mechanism by which a type of artificial neural network known as a self-organizing map (SOM) learns. Some types of artificial neural network, such as evolving connectionist systems, can learn in both a supervised and unsupervised manner.[2]
Improvement
Both gene regulatory networks and artificial neural networks have two main strategies for improving their accuracy. In both cases the output of the network is measured against known biological data using some function, and subsequent improvements are made by altering the structure of the network. A common test of accuracy for artificial neural networks is to compare some parameter of the model to data acquired from biological neural systems, such as from an EEG.[7] In the case of EEG recordings, the local field potential (LFP) of the artificial neural network is taken and compared to EEG data acquired from human patients. The relative intensity ratio (RIRs) and fast Fourier transform (FFT) of the EEG are compared with those generated by the artificial neural networks to determine the accuracy of the model.[8]
Genetic algorithm
File:A-Numerical-Approach-to-Ion-Channel-Modelling-Using-Whole-Cell-Voltage-Clamp-Recordings-and-a-pcbi.0030169.sv001.ogv Because the amount of data on the interplay of genes and neurons and their effects is not enough to construct a rigorous model, evolutionary computation is used to optimize artificial neural networks and gene regulatory networks, a common technique being the genetic algorithm. A genetic algorithm is a process that can be used to refine models by mimicking the process of natural selection observed in biological ecosystems. The primary advantages are that, due to not requiring derivative information, it can be applied to black box problems and multimodal optimization. The typical process for using genetic algorithms to refine a gene regulatory network is: first, create a population; next, to create offspring via a crossover operation and evaluate their fitness; then, on a group chosen for high fitness, simulate mutation via a mutation operator; finally, taking the now mutated group, repeat this process until a desired level of fitness is demonstrated. [9]
Evolving systems
Methods by which artificial neural networks may alter their structure without simulated mutation and fitness selection have been developed. A dynamically evolving neural network is one approach, as the creation of new connections and new neurons can be modeled as the system adapts to new data. This enables the network to evolve in modeling accuracy without simulated natural selection. One method by which dynamically evolving networks may be optimized, called evolving layer neuron aggregation, combines neurons with sufficiently similar input weights into one neuron. This can take place during the training of the network, referred to as online aggregation, or between periods of training, referred to as offline aggregation. Experiments have suggested that offline aggregation is more efficient.[5]
Potential applications
A variety of potential applications have been suggested for accurate computational neurogenetic models, such as simulating genetic diseases, examining the impact of potential treatments,[10] better understanding of learning and cognition,[11] and development of hardware able to interface with neurons.[4]
The simulation of disease states is of particular interest, as modeling both the neurons and their genes and proteins allows linking genetic mutations and protein abnormalities to pathological effects in the central nervous system. Among those diseases suggested as being possible targets of computational neurogenetic modeling based analysis are epilepsy, schizophrenia, mental retardation, brain aging and Alzheimer's disease, and Parkinson's disease.[2]
See also
References
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- ↑ Benuskova, L.; Kasabov, N. (2008). "Modeling brain dynamics using computational neurogenetic approach". Cognitive Neurodynamics 2 (4): 319–334. http://dx.doi.org/10.1007/s11571-008-9061-1
- ↑ 4.0 4.1 One of the biggest reasons investing in a Singapore new launch is an effective things is as a result of it is doable to be lent massive quantities of money at very low interest rates that you should utilize to purchase it. Then, if property values continue to go up, then you'll get a really high return on funding (ROI). Simply make sure you purchase one of the higher properties, reminiscent of the ones at Fernvale the Riverbank or any Singapore landed property Get Earnings by means of Renting
In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang
Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules
Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.
A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running
The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more
There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang - ↑ 5.0 5.1 5.2 Watts, Michael J. (2009). "A Decade of Kasabov’s Evolving Connectionist Systems: A Review". IEEE Transactions on Systems, Man, And Cybernetics—Part C: Applications and Reviews 39 (3): 253-269. http://dx.doi.org/10.1109/TSMCC.2008.2012254
- ↑ Kasabov, N.; Schliebs, S., Mohemmed, A. (2012). "Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling". Computational Intelligence Methods for Bioinformatics and Biostatistics 7548: 1-9. http://dx.doi.org/10.1007/978-3-642-35686-5_1
- ↑ Benuskova, L.; Wysoski, S. G., Kasabov, N. (16-21). "Computational Neurogenetic Modeling: A Methodology to Study Gene Interactions Underlying Neural Oscillations". 2006 International Joint Conference on Neural Networks. http://dx.doi.org/10.1109/IJCNN.2006.1716743
- ↑ Kasabov, N.; Benuskova, L., Wysoski, S. G. (2005). "Computational neurogenetic modeling: Integration of spiking neural networks, gene networks, and signal processing techniques.". Artificial Neural Networks: Formal Models and Their Applications - Icann 2005, Pt 2, Proceedings 3697: 509–514. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.5223&rep=rep1&type=pdf
- ↑ Kasabov, N. (2006). "Neuro-, genetic-, and quantum inspired evolving intelligent systems". International Symposium on Evolving Fuzzy Systems, Proceedings: 63–73. http://dx.doi.org/10.1109/ISEFS.2006.251165
- ↑ Kasabov, N.; Benuskova, L., Wysoski, S. G. (2005). "Biologically plausible computational neurogenetic models: Modeling the interaction between genes, neurons and neural networks". Journal of Computational and Theoretical Nanoscience 2 (4): 569–573. http://www.ingentaconnect.com/content/asp/jctn/2005/00000002/00000004/art00012
- ↑ One of the biggest reasons investing in a Singapore new launch is an effective things is as a result of it is doable to be lent massive quantities of money at very low interest rates that you should utilize to purchase it. Then, if property values continue to go up, then you'll get a really high return on funding (ROI). Simply make sure you purchase one of the higher properties, reminiscent of the ones at Fernvale the Riverbank or any Singapore landed property Get Earnings by means of Renting
In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang
Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules
Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.
A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running
The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more
There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang