Main Page - Revision history

Bradley Monk at 05:38, 12 March 2024

2024-03-12T05:38:18Z

← Older revision		Revision as of 22:38, 11 March 2024
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	{{Box\|width=45%\|min-width=300px\|float=left\|font-size=14px\|[[Actin\|Actin Modeling]]\|		{{Box\|width=45%\|min-width=300px\|float=left\|font-size=14px\|[[Actin\|Actin Modeling]]\|
	The study of actin dynamics is centrally important to understanding synaptic plasticity. Fortunately, actin research has provided a vast pool of experimental studies, and several quantitative models that provide excellent characterizations of actin polymerization kinetics. To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state, monte carlo and stochastic models. The ability to simulate the evolution of actin networks in 3D makes this model unique.		The study of actin dynamics is centrally important to understanding synaptic plasticity. Fortunately, actin research has provided a vast pool of experimental studies, and several quantitative models that provide excellent characterizations of actin polymerization kinetics. To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state, monte carlo and stochastic models. The ability to simulate the evolution of actin networks in 3D makes this model unique.
			<br><br>
	[[File:Actin modeling.png\|right\|600px]]		[[File:Actin modeling.png\|right\|600px]]

Bradley Monk at 05:37, 12 March 2024

2024-03-12T05:37:43Z

← Older revision		Revision as of 22:37, 11 March 2024
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	The study of actin dynamics is centrally important to understanding synaptic plasticity. Fortunately, actin research has provided a vast pool of experimental studies, and several quantitative models that provide excellent characterizations of actin polymerization kinetics. To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state, monte carlo and stochastic models. The ability to simulate the evolution of actin networks in 3D makes this model unique.		The study of actin dynamics is centrally important to understanding synaptic plasticity. Fortunately, actin research has provided a vast pool of experimental studies, and several quantitative models that provide excellent characterizations of actin polymerization kinetics. To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state, monte carlo and stochastic models. The ability to simulate the evolution of actin networks in 3D makes this model unique.

	[[File: ~~Korobova Svitkina 2011 EM~~ Actin.png\|right\|~~340px~~]]		[[File:Actin modeling.png\|right\|600px]]

	}}		}}

	{{Box\|width=45%\|min-width=310px\|float=right\|font-size=14px\|[[:Category:Synaptic Plasticity\|Synaptic Plasticity]]\|[[File:Synapses web.jpg\|~~left~~\|~~540px~~\|link=Synaptic Plasticity]]{{Clear}}		{{Box\|width=45%\|min-width=310px\|float=right\|font-size=14px\|[[:Category:Synaptic Plasticity\|Synaptic Plasticity]]\|[[File:Synapses web.jpg\|center\|500px\|link=Synaptic Plasticity]]{{Clear}}
	It is now generally accepted that many forms of adaptive behavior, including learning and memory, engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory. Whenever I have to summarize my primary research focus using just a few words, they always include: "'''''synaptic plasticity'''''". Indeed, I feel that the key to fully understanding cognitive processes like memory formation is through studying neural dynamics at the cellular-network, synaptic, and molecular levels.		It is now generally accepted that many forms of adaptive behavior, including learning and memory, engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory. Whenever I have to summarize my primary research focus using just a few words, they always include: "'''''synaptic plasticity'''''". Indeed, I feel that the key to fully understanding cognitive processes like memory formation is through studying neural dynamics at the cellular-network, synaptic, and molecular levels.
	}}		}}
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	{{Box\|width=45%\|min-width=310px\|float=left\|font-size=14px\|[[Neural Nets\|Machine Learning Tutorial]]\|		{{Box\|width=45%\|min-width=310px\|float=left\|font-size=14px\|[[Neural Nets\|Machine Learning Tutorial]]\|
	[[File:~~Machine learning tutorial~~.png\|~~300px~~\|link=Neural Nets]]{{Clear}} <br><br>		[[File:Neural-net-01.png\|500px\|link=Neural Nets]]{{Clear}} <br><br>

	I have developed a [[Neural Nets\|machine learning tutorial]], focusing on supervised learning, but it also touches on techniques like t-SNE. It makes heavy use of Tensorflow Playground to visualize what is happening in multilayer neural networks during training. It also provides learners with an opportunity to try and solve problems classification problems live right on the web app.		I have developed a [[Neural Nets\|machine learning tutorial]], focusing on supervised learning, but it also touches on techniques like t-SNE. It makes heavy use of Tensorflow Playground to visualize what is happening in multilayer neural networks during training. It also provides learners with an opportunity to try and solve problems classification problems live right on the web app.

Bradley Monk at 05:25, 12 March 2024

2024-03-12T05:25:53Z

← Older revision		Revision as of 22:25, 11 March 2024
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	}}		}}

	{{Box\|width=45%\|min-width=310px\|float=right\|font-size=14px\|[[:Category:Synaptic Plasticity\|Synaptic Plasticity]]\|[[File:~~Neuron Synapse~~.~~png~~\|left\|~~340px~~\|link=Synaptic Plasticity]]{{Clear}}		{{Box\|width=45%\|min-width=310px\|float=right\|font-size=14px\|[[:Category:Synaptic Plasticity\|Synaptic Plasticity]]\|[[File:Synapses web.jpg\|left\|540px\|link=Synaptic Plasticity]]{{Clear}}
	It is now generally accepted that many forms of adaptive behavior, including learning and memory, engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory. Whenever I have to summarize my primary research focus using just a few words, they always include: "'''''synaptic plasticity'''''". Indeed, I feel that the key to fully understanding cognitive processes like memory formation is through studying neural dynamics at the cellular-network, synaptic, and molecular levels.		It is now generally accepted that many forms of adaptive behavior, including learning and memory, engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory. Whenever I have to summarize my primary research focus using just a few words, they always include: "'''''synaptic plasticity'''''". Indeed, I feel that the key to fully understanding cognitive processes like memory formation is through studying neural dynamics at the cellular-network, synaptic, and molecular levels.
	}}		}}

Bradley Monk at 05:22, 12 March 2024

2024-03-12T05:22:37Z

← Older revision		Revision as of 22:22, 11 March 2024
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	Molecular-level synaptic plasticity is among my primary interests. I've studied and quantified membrane [[:Category:Diffusion\|diffusion]] properties of excitatory and inhibitory receptors, and have developed models how these particles swarm to potentiate synapses. I find stochastic particle diffusion is intertwined with the first principles of [[:Category:Statistics\|statistics and probability]]. Given that synaptic potentiation is dependent on marshalling receptors undergoing stochastic diffusion, it seem that neurons have evolved into innate statistical computers. The result of 100 billion of these statistical computers making 100 trillion connections is the human brain. Here are some of my [[:Category:Diffusion\|notes and code for simulating membrane diffusion.]]		Molecular-level synaptic plasticity is among my primary interests. I've studied and quantified membrane [[:Category:Diffusion\|diffusion]] properties of excitatory and inhibitory receptors, and have developed models how these particles swarm to potentiate synapses. I find stochastic particle diffusion is intertwined with the first principles of [[:Category:Statistics\|statistics and probability]]. Given that synaptic potentiation is dependent on marshalling receptors undergoing stochastic diffusion, it seem that neurons have evolved into innate statistical computers. The result of 100 billion of these statistical computers making 100 trillion connections is the human brain. Here are some of my [[:Category:Diffusion\|notes and code for simulating membrane diffusion.]]
	----		----
	[[File: ~~Stochastic~~-Diffusion.gif\|350px\|center]]		[[File:Brownian-Diffusion.gif\|350px\|center]]
	}}		}}

Bradley Monk at 10:35, 28 August 2021

2021-08-28T10:35:20Z

← Older revision		Revision as of 03:35, 28 August 2021
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	Molecular-level synaptic plasticity is among my primary interests. I've studied and quantified membrane [[:Category:Diffusion\|diffusion]] properties of excitatory and inhibitory receptors, and have developed models how these particles swarm to potentiate synapses. I find stochastic particle diffusion is intertwined with the first principles of [[:Category:Statistics\|statistics and probability]]. Given that synaptic potentiation is dependent on marshalling receptors undergoing stochastic diffusion, it seem that neurons have evolved into innate statistical computers. The result of 100 billion of these statistical computers making 100 trillion connections is the human brain. Here are some of my [[:Category:Diffusion\|notes and code for simulating membrane diffusion.]]		Molecular-level synaptic plasticity is among my primary interests. I've studied and quantified membrane [[:Category:Diffusion\|diffusion]] properties of excitatory and inhibitory receptors, and have developed models how these particles swarm to potentiate synapses. I find stochastic particle diffusion is intertwined with the first principles of [[:Category:Statistics\|statistics and probability]]. Given that synaptic potentiation is dependent on marshalling receptors undergoing stochastic diffusion, it seem that neurons have evolved into innate statistical computers. The result of 100 billion of these statistical computers making 100 trillion connections is the human brain. Here are some of my [[:Category:Diffusion\|notes and code for simulating membrane diffusion.]]
	----		----
	[[File: Stochastic-Diffusion.gif]]		[[File: Stochastic-Diffusion.gif\|350px\|center]]
	}}		}}



	{{Box\|width=45%\|min-width=~~310px~~\|float=left\|[[Hello]] internet person!\|		{{Box\|width=45%\|min-width=340px\|float=left\|[[Hello]] internet person!\|
	You've found [[User:Monakhos\|my wiki]]. This is where I horde random information. I have every intention of linking it all together someday. If you are so inclined, recent additions to this wiki can be found in the box on the right. For a non-curated glimpse of my activity you can check out the [[Special:RecentChanges\|latest wiki updates]]. Older wiki [[content]] can be accessed using the <nowiki>[search box]</nowiki> or perusing [[Special:AllPages\| all pages]]. If you would like to contact me, you can find this info on [http://bradleymonk.com my home page]. You can find a list of my [[publications]] here.		You've found [[User:Monakhos\|my wiki]]. This is where I horde random information. I have every intention of linking it all together someday. If you are so inclined, recent additions to this wiki can be found in the box on the right. For a non-curated glimpse of my activity you can check out the [[Special:RecentChanges\|latest wiki updates]]. Older wiki [[content]] can be accessed using the <nowiki>[search box]</nowiki> or perusing [[Special:AllPages\| all pages]]. If you would like to contact me, you can find this info on [http://bradleymonk.com my home page]. You can find a list of my [[publications]] here.
	}}		}}

Bradley Monk at 00:59, 22 August 2021

2021-08-22T00:59:21Z

← Older revision		Revision as of 17:59, 21 August 2021
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	{{Box\|width=45%\|min-width=310px\|float=left\|[[Hello]] internet person!\|		{{Box\|width=45%\|min-width=310px\|float=left\|[[Hello]] internet person!\|
	You've found [[User:Monakhos\|my wiki]]. This is where I horde random information. I have every intention of linking it all together someday. If you are so inclined, recent additions to this wiki can be found in the box on the right. For a non-curated glimpse of my activity you can check out the [[Special:RecentChanges\|latest wiki updates]]. Older wiki [[content]] can be accessed using the <nowiki>[search box]</nowiki> or perusing [[Special:AllPages\| all pages]]. If you would like to contact me, you can find this info on [http://bradleymonk.com my home page].		You've found [[User:Monakhos\|my wiki]]. This is where I horde random information. I have every intention of linking it all together someday. If you are so inclined, recent additions to this wiki can be found in the box on the right. For a non-curated glimpse of my activity you can check out the [[Special:RecentChanges\|latest wiki updates]]. Older wiki [[content]] can be accessed using the <nowiki>[search box]</nowiki> or perusing [[Special:AllPages\| all pages]]. If you would like to contact me, you can find this info on [http://bradleymonk.com my home page]. You can find a list of my [[publications]] here.
	}}		}}

Bradley Monk at 11:22, 27 October 2020

2020-10-27T11:22:30Z

← Older revision		Revision as of 04:22, 27 October 2020
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	{{Box\|width=45%\|min-width=310px\|float=right\|Popular Categories\|		{{Box\|width=45%\|min-width=310px\|float=right\|Popular Pages and Categories\|
	{{SmallBox\|float=left\|clear=none\|margin=1px 1px 1px 10px\|padding=1px\|width=45%\|font-size=12px\|border-style=none\|text-align=left\|		{{SmallBox\|float=left\|clear=none\|margin=1px 1px 1px 10px\|padding=1px\|width=45%\|font-size=12px\|border-style=none\|text-align=left\|
	[[:Category:Synaptic Plasticity{{!}}Category:Plasticity]]		*[[:Category:Synaptic Plasticity{{!}}Category:Plasticity]]
	[[:Category:Journals]]		*[[APOE]]
	[[:Category:Math]]		*[[:Category:Journals]]
	[[:Category:Neuroscience Methods]]		*[[:Category:Math]]
			*[[:Category:Neuroscience Methods]]
	}}		}}
	{{SmallBox\|float=left\|clear=none\|margin=1px 1px 1px 10px\|padding=1px\|width=45%\|font-size=12px\|border-style=none\|text-align=left\|		{{SmallBox\|float=left\|clear=none\|margin=1px 1px 1px 10px\|padding=1px\|width=45%\|font-size=12px\|border-style=none\|text-align=left\|
	[[:Category:Diffusion]]		*[[Genomics Terminology]]
	[[:Category:Qual]]		*[[:Category:Diffusion]]
	[[:Category:Neurobiology]]		*[[:Category:Qual]]
	[[:Category:Malinow]]		*[[:Category:Neurobiology]]
			*[[:Category:Malinow]]
	}}		}}

Bradley Monk at 05:50, 24 October 2018

2018-10-24T05:50:02Z

← Older revision		Revision as of 22:50, 23 October 2018
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	{{Box\|width=45%\|min-width=310px\|float=right\|font-size=14px\|[[:Category:Synaptic Plasticity\|Synaptic Plasticity]]\|[[File:Neuron Synapse.png\|~~right~~\|~~300px~~\|link=Synaptic Plasticity]]{{Clear}}		{{Box\|width=45%\|min-width=310px\|float=right\|font-size=14px\|[[:Category:Synaptic Plasticity\|Synaptic Plasticity]]\|[[File:Neuron Synapse.png\|left\|340px\|link=Synaptic Plasticity]]{{Clear}}
	It is now generally accepted that many forms of adaptive behavior, including learning and memory, engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory. Whenever I have to summarize my primary research focus using just a few words, they always include: "'''''synaptic plasticity'''''". Indeed, I feel that the key to fully understanding cognitive processes like memory formation is through studying neural dynamics at the cellular-network, synaptic, and molecular levels.		It is now generally accepted that many forms of adaptive behavior, including learning and memory, engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory. Whenever I have to summarize my primary research focus using just a few words, they always include: "'''''synaptic plasticity'''''". Indeed, I feel that the key to fully understanding cognitive processes like memory formation is through studying neural dynamics at the cellular-network, synaptic, and molecular levels.
	}}		}}

Bradley Monk at 05:44, 24 October 2018

2018-10-24T05:44:09Z

← Older revision		Revision as of 22:44, 23 October 2018
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	{{Box\|width=45%\|min-width=300px\|float=left\|font-size=14px\|[[Actin\|Actin Modeling]]\|		{{Box\|width=45%\|min-width=300px\|float=left\|font-size=14px\|[[Actin\|Actin Modeling]]\|
	The study of actin dynamics is centrally important to understanding synaptic plasticity. Fortunately, actin research has provided a vast pool of experimental studies, and several quantitative models that provide excellent characterizations of actin polymerization kinetics. To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state, monte carlo and stochastic models. The ability to simulate the evolution of actin networks in 3D makes this model unique.		The study of actin dynamics is centrally important to understanding synaptic plasticity. Fortunately, actin research has provided a vast pool of experimental studies, and several quantitative models that provide excellent characterizations of actin polymerization kinetics. To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state, monte carlo and stochastic models. The ability to simulate the evolution of actin networks in 3D makes this model unique.
	~~<mediaplayer image='http~~:~~//www.bradleymonk.com/w/images/d/da/Kasai_GluUncaging_S7~~.png~~' width='100%'>http://www.bradleymonk.com/w/images/1/18/Kasai_GluUncaging_S7.mov</mediaplayer>~~
			[[File: Korobova Svitkina 2011 EM Actin.png\|right\|340px]]

	}}		}}

Bradley Monk at 23:32, 11 June 2018

2018-06-11T23:32:09Z

← Older revision		Revision as of 16:32, 11 June 2018
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	Using SNP profiles ~~of over 10k individuals from the Alzheimer's Disease Sequencing Project ([https://www.niagads.org/adsp/content/home ADSP])~~ we have developed a computational framework for making diagnostic predictions regarding the likelihood that someone will develop ~~Alzheimer's Disease (AD)~~. A key feature of this framework is a neural network algorithm that, through machine learning, has been trained to predict AD patients or ~~non-AD~~ controls with high accuracy. Importantly, these predictions ~~were made on individuals never seen by~~ the classifier~~, suggesting high accuracy diagnoses could transfer to~~ the general population. ~~In fact, only a few hundred~~ genomic loci ~~are needed~~, ~~and have been identified by~~ the ~~learning algorithms. The~~ neural net outputs a ‘confidence’ ~~level~~ for each prediction; ~~for individuals registering~~ high-confidence predictions, the classifier is over 90% accurate. Since network weights have ~~already~~ been trained, and only a relatively small number of ~~key variant loci~~ are needed, this system ~~could aid in~~ clinical ~~diagnostics~~; ~~and as new~~ genomes ~~and clinical status are added~~, ~~the system will~~ continue to improve ~~performance over time.~~		Using SNP profiles we have developed a computational framework for making diagnostic predictions regarding the likelihood that someone will develop dementia. A key feature of this framework is a neural network algorithm that, through machine learning, has been trained to predict patients or controls with high accuracy. Importantly, these predictions have proven to generalize well to hold-out genomes from independent sequencing projects, suggesting the classifier may perform well across samples of the general population. The bp status of just ~1k genomic loci was sufficient to to have 80% prediction accuracy. Furthermore, the neural net outputs a ‘confidence’ score for each prediction; on high-confidence predictions the classifier is over 90% accurate (''confidence'' is not quantified ''post hoc'', it is divined ''a priori'' by deep neural nets). Since the neural network weights have been trained, and because only a relatively small number of genomic targets are needed, we hope this system can be further developed into a clinical diagnostic tool. As it is, this is still far off; many independent test genomes will be required to validate such a tool. In the meanwhile, we hope to continue to improve the classifier's performance using novel data and methods.

	~~Here I provide a step-by-step analysis-walkthrough towards~~ the ~~goal of developing a platform for Alzheimer~~'s ~~Disease diagnosis based on machine learning techniques. Here are some entry pages: [[ADSP\|Intro]], [[ADSP Neural Nets\|More Neural Nets]], [[ADSP PCA\|PCA]], [[ADSP t-SNE\|t-SNE]]~~.
	}}		}}