How do brains work?
I mean, how do brains work at the level slightly higher than a synapse, but much lower than, e.g. psychology. “How is thought done?” etc.
Notes pertaining to large, artificial networks are filed under artificial neural networks. The messy, biological end of the stick is here. Since brains seem to be the seat of the most flashy and important bit of the computing taking place in our bodies, we understandably want to know how they works, in order to
- fix Alzheimers disease
- steal cool learning tricks
- endow the children of elites with superhuman mental prowess to cement their places as Übermenschen fit to rule the thousand year Reich
- …or whatever.
Real brains are different to the “neuron-inspired” computation of the simulacrum in very many ways, not just the usual difference between model and reality. The similitude between “neural networks” and neurons is intentionally weak for reasons of convenience.
For one example, most simulated neural networks are based on a continuous activation potential and discrete time, unlike spiking biological ones which are driven by discrete events in continuous time.
Also real brains support heterogeneous types of neuron, have messier layer organisation, use less power, don’t have well-defined backpropagation (or not in the same way), and many other things that I as a non-specialist do not know.
To learn more about:
- Just saw a talk by Dan Cireșan in which he mentioned a thing that I keep seeing in NN discussions in various forms: “Foveation” - blurring the edge of an image when training classifiers on it. The allure of ignoring stuff to learn it. What is, rigorously speaking, happening there? See also Elman. Nice actual crossover with deep learning.
- Algorithmic statistics of neurons sounds interesting.
- Amigó, J. M., Szczepański, J., Wajnryb, E., & Sanchez-Vives, M. V.(2004) Estimating the Entropy Rate of Spike Trains via Lempel-Ziv Complexity. Neural Computation, 16, 717–736. DOI.
- Barbieri, R., Quirk, M. C., Frank, L. M., Wilson, M. A., & Brown, E. N.(2001) Construction and analysis of non-Poisson stimulus-response models of neural spiking activity. Journal of Neuroscience Methods, 105(1), 25–37. DOI.
- Brette, R. (2008) Generation of Correlated Spike Trains. Neural Computation, 0(0), 80804143617793–28. DOI.
- Brette, R. (2012) Computing with Neural Synchrony. PLoS Comput Biol, 8(6), e1002561. DOI.
- Brown, E., Barbieri, R., Ventura, V., Kass, R., & Frank, L. (2002) The time-rescaling theorem and its application to neural spike train data analysis. Neural Computation, 14(2), 325–346. DOI.
- Buhusi, C. V., & Meck, W. H.(2005) What makes us tick? Functional and neural mechanisms of interval timing. Nature Reviews Neuroscience, 6(10), 755–765. DOI.
- Eden, U., Frank, L., Barbieri, R., Solo, V., & Brown, E. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering. Neural Computation, 16(5), 971–998. DOI.
- Elman, J. L.(1990) Finding structure in time. Cognitive Science, 14, 179–211. DOI.
- Elman, J. L.(1993) Learning and development in neural networks: the importance of starting small. Cognition, 48, 71–99. DOI.
- Fee, M. S., Kozhevnikov, A. A., & Hahnloser, R. H.(2004) Neural mechanisms of vocal sequence generation in the songbird. Annals of the New York Academy of Sciences, 1016, 153–170. DOI.
- Fernández, P., & Solé, R. V.(2007) Neutral fitness landscapes in signalling networks. Journal of The Royal Society Interface, 4(12), 41. DOI.
- Gibson, E. (1998) Linguistic complexity: Locality of syntactic dependencies. Cognition, 68(1), 1–76.
- Harrison, M., & Geman, S. (2009) A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains. Neural Computation, 21(5), 1244–1258. DOI.
- Harrison, M. T., Amarasingham, A., & Kass, R. E.(2013) Statistical Identification of Synchronous Spiking. In P. M. DiLorenzo & J. D. Victor (Eds.), Spike Timing: Mechanisms and Function. CRC Press
- Haslinger, R., Pipa, G., & Brown, E. (2010) Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking. Neural Computation, 22(10), 2477–2506. DOI.
- Hemmen, J. L. van, Longtin, A., & Vollmayr, A. N.(2011) Testing resonating vector strength: Auditory system, electric fish, and noise. Chaos: An Interdisciplinary Journal of Nonlinear Science, 21(4), 47508. DOI.
- Jin, D. Z.(2009) Generating variable birdsong syllable sequences with branching chain networks in avian premotor nucleus HVC. Physical Review E, 80(5), 51902. DOI.
- Kennel, M. B., Shlens, J., Abarbanel, H. D. I., & Chichilnisky, E. J.(2005) Estimating Entropy Rates with Bayesian Confidence Intervals. Neural Computation, 17(7). DOI.
- Lee, H., Battle, A., Raina, R., & Ng, A. Y.(2007) Efficient sparse coding algorithms. Advances in Neural Information Processing Systems, 19, 801.
- Nemenman, I., Bialek, W., & de Ruyter Van Steveninck, R. (2004) Entropy and information in neural spike trains: Progress on the sampling problem. Physical Review E, 69(5), 56111.
- Panzeri, S., Senatore, R., Montemurro, M. A., & Petersen, R. S.(2007) Correcting for the sampling bias problem in spike train information measures. Journal of Neurophysiology, 98, 1064–1072. DOI.
- Seth, A. K., Chorley, P., & Barnett, L. C.(2013) Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling. NeuroImage, 65, 540–555. DOI.
- Smith, A., & Brown, E. (2003) Estimating a state-space model from point process observations. Neural Computation, 15(5), 965–991. DOI.
- Smith, E. C., & Lewicki, M. S.(2004) Learning efficient auditory codes using spikes predicts cochlear filters. In Advances in Neural Information Processing Systems (pp. 1289–1296).
- Smith, E. C., & Lewicki, M. S.(2006) Efficient auditory coding. Nature, 439(7079), 978–982. DOI.
- Smith, E., & Lewicki, M. S.(2005) Efficient Coding of Time-Relative Structure Using Spikes. Neural Computation, 17(1), 19–45. DOI.
- Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R., & Bialek, W. (1998) Entropy and Information in Neural Spike Trains. Phys. Rev. Lett., 80(1), 197–200. DOI.
- Vargas-Irwin, C. E., Brandman, D. M., Zimmermann, J. B., Donoghue, J. P., & Black, M. J.(2015) Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis. Neural Computation, 27(1), 1–31. DOI.
- Victor, J. D.(2002) Binless strategies for estimation of information from neural data. Physical Review E, 66, 51903. DOI.
- Wibral, M., Lizier, J. T., & Priesemann, V. (2015) Bits from brains for biologically inspired computing. Computational Intelligence, 2, 5. DOI.