minhae0516 / computational-neuroscience

My new journey to computational neuroscience

computational neuroscience

I summarize a paper list that I read in the beginning of my research in computational neuroscience. These papers help me understand fundamental concepts of computational neuroscience.

• Efficient coding

1. H. Barlow, “Redundancy reduction revisited,” Network: computation in neural systems, vol. 12, no. 3, pp. 241–253, 2001.
2. J. H. van Hateren, “Real and optimal neural images in early vision,” Nature, vol. 360, no. 6399, p. 68, 1992.
3. S. Laughlin, “A simple coding procedure enhances a neuron’s information capacity,” Zeitschrift fur Naturforschung c, vol. 36, no. 9-10, pp. 910–912, 1981.
4. J. H. VanHateren, “Spatiotemporal contrastsen sitivity of early vision,”Vision research, vol.33, no.2, pp.257–267, 1993.
5. H. B. Barlow, “Possible principles underlying the transformations of sensory messages,” 1961.
6. H. B. Barlow, “Sensory mechanisms, the reduction of redundancy, and intelligence,” NPL Symposium on the Mechanization of Thought Process, no. 10, pp. 535–539, 1959.
7. J. J. Atick and A. N. Redlich, “Towards a theory of early visual processing,” Neural Computation, vol. 2, no. 3, pp. 308–320, 1990.

• Predictive coding

1. Y.Huangand R.P.Rao,“Predictivecoding,”WileyInterdisciplinaryReviews:CognitiveScience,vol.2,no.5,pp.580–593,
3. M. V. Srinivasan, S. B. Laughlin, and A. Dubs, “Predictive coding: a fresh view of inhibition in the retina,” Proc. R. Soc. Lond. B, vol. 216, no. 1205, pp. 427–459, 1982.

• Bayesian inference in brain

1. R.V.RajuandX.Pitkow,“Inferencebyreparameterizationinneuralpopulationcodes,”inAdvancesinNeuralInformation Processing Systems, 2016, pp. 2029–2037.
2. X. Pitkow and D. E. Angelaki, “Inference in the brain: statistics flowing in redundant population codes,” Neuron, vol. 94, no. 5, pp. 943–953, 2017.