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The synapse theory of memory

December 1, 2009

Two recently publishes in Nature pushes the synapse theory of memory further with observational evidences with microscope.

Xu T. et. al., Rapid formation and selective stabilization of synapses for enduring motor memories.

Yang G. et. al., Stably maintained dendritic spines are associated with lifelong memories.

They trained the mice for simple task of motor skills and observed the change of synapses in some neurons in motor cortex with microscope. Since the neurons were marked with flurorescence, the numbers and morphology of the spines on dendrites could be lively observed under microscope. According to the synapse theory, the memory is stored and consolidated in the spines of synapses, and this two papers really showed the dynamic change of spines formation and elimination during the learning.  The newly formed spines could be stabilized for a long time without being eliminated, and the total number of  spines did decrease with life getting old. This data found the proof of our memory chip in spines of synapses, the next step may be to understand the coding and decoding system like 0/1 used by computer.

#A dendritic spine (or spine) is a small membranous protrusion from a neuron’s dendrite that typically receives input from a single synapse of an axon. Dendritic spines serve as a storage site for synaptic strength and help transmit electrical signals to the neuron’s cell body. Most spines have a bulbous head (the spine head), and a thin neck that connects the head of the spine to the shaft of the dendrite. The dendrites of a single neuron can contain from thousands up to a few hundred thousand spines. In addition to spines providing an anatomical substrate for memory storage and synaptic transmission, they may also serve to increase the number of possible contacts between neurons. (from wikipedia)

#Chemical synapses are specialized junctions through which neurons signal to each other and to non-neuronal cells such as those in muscles or glands. Chemical synapses allow neurons to form circuits within the central nervous system. They are crucial to the biological computations that underlie perception and thought. They allow the nervous system to connect to and control other systems of the body.(from wikipedia)

#memory is an organism’s ability to store, retain, and recall information. Traditional studies of memory began in the fields of philosophy, including techniques of artificially enhancing the memory. The late nineteenth and early twentieth century put memory within the paradigms of cognitive psychology. In recent decades, it has become one of the principal pillars of a branch of science called cognitive neuroscience, an interdisciplinary link between cognitive psychology and neuroscience. (from wikipedia)

 

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A talk by Larry Abbott

November 24, 2009

Talk: Studying and modifying the dynamics of neural networks by Larry Abbott

About the speaker: http://www.neurotheory.columbia.edu/~larry/

Comments: It’s the first time I listened to his talk, and he is really an impressive person, very smart and scientific. I haven’t started reading his book “Theoretical Neuroscience” yet, which is the text book of computational neuroscience in MIT. His experiment on neural network is quite enlightening, which hints that the chaotic world could be synchronized into an orderly output with a stimulus, no matter what inside is.  The model is quite interesting and may help to explain many mechanism of neural development or diseases.  I will read more before writing in details here.

 

Summary from “Generating Coherent Patterns of Activity from Chaotic Neural Networks” Neuron, 2009, 63, 544:
Neural circuits display complex activity patterns bothspontaneously and when responding to a stimulus orgenerating a motor output. How are these two formsof activity related? We develop a procedure calledFORCE learning for modifying synaptic strengthseither external to or within a model neural networkto change chaotic spontaneous activity into a widevariety of desired activity patterns. FORCE learningworks even though the networks we train are spontaneouslychaotic and we leave feedback loops intactand unclamped during learning. Using this approach,we construct networks that produce a wide variety ofcomplex output patterns, input-output transformationsthat require memory, multiple outputs that canbe switched by control inputs, and motor patternsmatching human motion capture data. Our resultsreproduce data on premovement activity in motorand premotor cortex, and suggest that synaptic plasticitymay be a more rapid and powerful modulator ofnetwork activity than generally appreciated.

 

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LaTex Example

November 7, 2009

Copyright by LaTex to WordPress

Look at the document source to see how to strike out text, how to use different colors, and how to link to URLs with snapshot preview and how to link to URLs without snapshot preview.

There is a command which is ignored by pdflatex and which defines where to cut the post in the version displayed on the main page Read the rest of this entry »

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Neuroanalysis by AVI PELED

October 31, 2009

Book: Neuroanalysis, bridging the gap between neuroscience, psychoanalysis and psychiatry

Author: Avi Peled MD

Comments: The author tried a new way of organizing the diagnosis standards of psychiatric diseases compared DSM used in clinic. It’s called clinical brain profiling (CBP), which  integrates all descriptive signs/symptoms seen in all psychiatric diseases into 3 categories: neural complexity disorder (NCD), neural resilience insufficiency (NRI), context sensitive processing decline (CPD). NCD includes connectivity imbalance (segregation vs integration) and hierarchy imbalance (bottom up insufficiency vs top down shift), NRI includes optimization imbalance (de-optimization vs hyper-optimization) and constrain frustration imbalance), CPD includes contexts biases and organization level. He gave a weight to each signs and made a  system map with input and output model.  With this new mapping, he gave some case analysis of patients to prove that it helps to better diagnose and treat patients than the DSM bible.

In fact, it’s a way of observing the patient in a systematic way, and not novel idea since it’s been used for thousands of years before the modern medicine. For example his considering the diseases  situations where connectivity or order disrupted is quite close to “Jing Luo”  and “Qi” theory, the Yin/Yang balance theory in  Chinese medicine. On the other hand, it tries to integrate the new knowledge of neuroscience research and psychology into the clinic practice, may help  physicians to have a better idea of the patient, and help neuroscientists to clarify the signs and symptoms described by physicians.

The mechanic system is accurately designed and run and brain is a complicated mechanic  system with plasticity. In the future, we may detect the weak point of each psychiatric patient, and fix it with correlate medicine. While the diagnosis is based on more objective observations like MRI, and the computation of the system input/output networks than subjective descriptions now used in DSM. The author would also agree that Mathematicians have the keys to the mysteries of brain, which is part of his interest declared in the book.

Is schizophrenia a disease? just like people have argued, is Alzheimer’s disease a disease? The medicine need new definitions…

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Reading lists on combinatorics application

October 26, 2009
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Reading lists on computational neuroscience

October 25, 2009

This’ a collection of books on computational neuroscience in Harvard’s library, keep a record for future reading:

  1. Memory and the computational brain : why cognitive science will transform neuroscience. Gallistel, C. R., 2009
  2. NeuroAnalysis : bridging the gap between neuroscience, psychoanalysis, and psychiatry. Peled, Avi, 2008
  3. International Conference on Intelligent Computing (4th : 2008 : Shanghai, China) Advanced intelligent computing theories and applications : with aspects of theoretical and methodological issues. 2008
  4. International Conference on Intelligent Computing (4th : 2008 : Shanghai, China) Advanced intelligent computing theories and applications : with aspects of artificial intelligence. 2008
  5. Computing the mind: how the mind really works Edelman, Shimon. 2008
  6. Computational neurogenetic modeling Beňušková, Ľ. 2007
  7. Dynamical systems in neuroscience : the geometry of  excitability and bursting. Izhikevich, Eugene M. 2007
  8. International Conference on Artificial Neural Networks (European Neural Network Society)  2009
  9. Handbook of neural engineering Hoboken, N.J. 2007
  10. Computational neuroscience : theoretical insights into brain function 2007
  11. Bayesian brain : probabilistic approaches to neural coding 2007
  12. Databasing the brain : from data to knowledge (neuroinformatics) Hoboken, N.J.  2005
  13. The computational neurobiology of reaching and pointing : a foundation for motor learning. Shadmehr, Reza. 2005
  14. International Work-Conference on the Interplay Between Natural and Artificial Computation (1st :     Mechanisms, symbols, and models underlying cognition : First International Work-Conference ).2005
  15. On intelligence Hawkins, Jeff,  2004
  16. Neural engineering : computation, representation, and  dynamics in neurobiological systems Eliasmith, Chris.2003
  17. The brain from 25,000 feet : high level explorations of brain complexity, perception, induction,  Changizi, Mark A. 2003
  18. Coherency model of hippocampal function  Soloveichik, David. 2002
  19. Spiking neuron models : single neurons, populations, plasticity Gerstner, Wulfram.  2002
  20. Theoretical neuroscience: computational and mathematical modeling of  neural systems. Dayan, Peter. 2001
  21. Computational neuroscience : realistic modeling for experimentalists Boca Raton, 2001
  22. Neuro-informatics and neural modelling 2001
  23. Computing the brain : a guide to neuroinformatics 2001
  24. Self-organizing map formation : foundations of neural computation 2001
  25. Biophysical neural networks : foundations of integrative neuroscience 2001
  26. Computational explorations in cognitive neuroscience : understanding the mind by simulating the… O’Reilly, Randall C.2000
  27. Unsupervised learning : foundations of neural computation 1999
  28. Neural codes and distributed representations : foundations of neural computation 1999
  29. Modeling in the neurosciences : from ionic channels to neural networks 1999
  30. Biophysics of computation : information processing in single neurons Koch, Christof.  1999
  31. Disorders of brain, behavior, and cognition : the neurocomputational perspective 1999
  32. Methods in neuronal modeling : from ions to networks 1998
  33. Fundamentals of neural network modeling : neuropsychology and cognitive neuroscience 1998
  34. Neurons, networks, and motor behavior 1997
  35. Computational neuroscience : trends in research 1997
  36. Neural network based algorithms for protein structure Chandonia, John-Marc. 1997
  37. Rethinking neural networks : quantum fields and biological data 1993
  38. The Neurobiology of neural networks 1993
  39. Computational neuroscience 1990
  40. Methods in neuronal modeling : from synapses to networks 1989
  41. Computational neuroscience: trends in research 2004
  42. Computational neuroscience, a comprehensive approach 2004

Electronic:

1 Neuroinformatics / edited by Chiquito Joaqium [sic] Crasto ; foreword by Stephen H. Koslow. 2007

2 Understanding events : from perception to action / edited by Thomas F. Shipley and Jeffrey M. Zacks. 2008

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Journals on computational neuroscience

October 25, 2009