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A Quantitative Neural Coding Model of Sensory Memory

Liu, PHD Peilei and Wang, Professor Ting (2014) A Quantitative Neural Coding Model of Sensory Memory. [Preprint]

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Abstract

The coding mechanism of sensory memory on the neuron scale is one of the most important questions in neuroscience. We have put forward a quantitative neural network model, which is self-organized, self-similar, and self-adaptive, just like an ecosystem following Darwin's theory. According to this model, neural coding is a “mult-to-one”mapping from objects to neurons. And the whole cerebrum is a real-time statistical Turing Machine, with powerful representing and learning ability. This model can reconcile some important disputations, such as: temporal coding versus rate-based coding, grandmother cell versus population coding, and decay theory versus interference theory. And it has also provided explanations for some key questions such as memory consolidation, episodic memory, consciousness, and sentiment. Philosophical significance is indicated at last.

Item Type:Preprint
Keywords:neural coding, sensory memory, synaptic plasticity, lateral competition
Subjects:Psychology > Cognitive Psychology
Neuroscience > Computational Neuroscience
Computer Science > Dynamical Systems
Computer Science > Machine Learning
Computer Science > Neural Nets
Computer Science > Statistical Models
Neuroscience > Neural Modelling
Philosophy > Logic
Philosophy > Philosophy of Mind
ID Code:9753
Deposited By: Liu, Mr. Peilei
Deposited On:24 Aug 2014 21:08
Last Modified:20 Apr 2015 11:40

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. F. Crick, The Astonishing Hypothesis: The Scientific Search for the Soul (Charles Scribner's Sons, New York, 1994).

2. G. Edelman, The Theory of Neuronal Group Selection (Basic Books, New York 1987).

3. C. Koch, K. Hepp, Quantum mechanics in the brain. Nature 440, 611-612 (2006).

4. T. Poggio, E. Bizzi, Generalization in vision and motor control. Nature 43, 768-774 (2004).

5. G. E. Hinton, R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313, 504-507 (2006).

6. S. Grossberg, Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks 37, 1-47 (2013).

7. D. E. Rumelhart, J. L. McClelland. Parallel Distributed Processing (The MIT Press, London, 1986).

8. J. Hawkins, S. Blakeslee, On Intelligence (Times Books, New York, 2004).

9. G. Miller, How are memories stored and retrieved. Science 309, 92 (2005).

10. A. L. Barabási, R. Albert, Emergence of scaling in random networks. Science 286, 509-512 (1999).

11. C. W. Reynolds, Flocks, herds, and schools: a distributed behavioral model. Comput. Graphics 21, 25-34 (1987).

12. W. S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 5: 115-133 (1943).

13. A. L. Hodgkin, A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500-544 (1952).

14. W. Gerstner, R. Kempter, J. L. Hemmen, H. Wagner, A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76-78 (1996).

15. H. Markram, J. Lübke, M. Frotscher, B. Sakmann, Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213-215 (1997).

16. C. R. Bramham, B. Srebro, Induction of long-term depression and potentiation by low- and high-frequency stimulation in the dentate area of the anesthetized rat: Magnitude, time course and EEG. Brain Res. 405:100-107 (1987).

17. D. O. Hebb, The Organization of Behavior: A Neuropsychological Approach (Jhon & Sons, New York, 1949).

18. E. Schrödinger, What Is Life? (Cambridge University Press, Cambridge, 1944).

19. J. V. Neumann, The Computer and the Brain (Yale University Press, New Haven, CT, 2000).

20. E. L. Bienenstock, L. N. Cooper, P. W. Munro, Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2, 28-32 (1982).

21. M. Schonewille et al., Reevaluating the role of LTD in cerebellar motor learning. Neuron 70, 43-50 (2011).

22. W. G. Regehr, M. R. Carey, A. R. Best, Activity-dependent regulation of synapses by retrograde messengers. Neuron 63, 154-170 (2009).

23. B. A. Olshausen, Sparse codes and spikes. R.P.N. Rao, B. A. Olshausen and M. S. Lewicki Eds. MIT press, 257-272 (2002).

24. C. G. Gross, Genealogy of the "grandmother cell". Neuroscientist 8, 512-518 (2002).M.

25. Minsky, S. Papert, Perceptrons: An Introduction to Computational Geometry (MIT Press, London, 1972).

26. R. B. Tootell, M. S. Silverman, R. L. De Valois, Spatial frequency columns in primary visual cortex. Science 214, 813-815 (1981).

27. G. B. Ermentrout, R. F. Galán, N. N. Urban, Relating neural dynamics to neural coding. Phys. Rev. 99, 248103 (2007).

28. M. J. Frank, L. C. Seeberger, R. C. O’Reilly, By carrot or by stick: cognitive reinforcement learning in parkinsonism. Science 306, 1940-1943 (2004).

29. D. H. Ackley, G. E. Hinton, T. J. Sejnowski, A learning algorithm for Boltzmann machine. Cognit. Sci. 9, 147-169 (1985).

30. C. G. Gross, Neurogenesis in the adult brain: death of a dogma. Nature Rev. Neurosci. 1, 67-73 (2000).

31. J. T. Wixted, On common ground: Jost’s (1897) law of forgetting and Ribot’s (1881) law of retrograde amnesia. Psychol. Rev. 111, 864-879 (2004).

32. T. D. Tomlinson, D. E. Huber, C. A. Rieth, E. J. Davelaar, An interference account of cue-independent forgetting in the no-think paradigm. Proc. Natl. Acad. Sci. U.S.A. 106, 15588-15593 (2009).

33. K. Oberauer, S. Lewandowsky, Forgetting in immediate serial recall: decay, temporal distinctiveness, or interference? Psychol. Rev. 115, 544-576 (2008).

34. J. V. Stone, P. E. Jupp, Falling towards forgetfulness: synaptic decay prevents spontaneous recovery of memory. PLoS Comput. Biol. 4, (2008). doi:10.1371/journal.pcbi.1000143.

35. R. Stickgold, Sleep-dependent memory consolidation. Nature 437, 1272-1278 (2005).

36. E. Tulving, H. J. Markowitsch, Episodic and declarative memory: Role of the hippocampus. Hippocampus 8, 198-204(1998).

37. P. W. Frankland, B. Bontempi, The organization of recent and remote memories. Nature Rev. 6, 119-130 (2005).

38. H. Lövheim, A new three-dimensional model for emotions and monoamine neurotransmitters. Med. Hypotheses 78, 341-348 (2012).

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