Neuronal behaviors: A control perspective

The purpose of this tutorial is to introduce and analyze models of neurons from a control perspective and to show how recently developed analytical tools help to address important biological questions. A first objective is to review the basic modeling principles of neurophysiology in which neurons are modeled as equivalent nonlinear electrical circuits that capture their excitable properties. The specific architecture of the models is key to the tractability of their analysis: in spite of their high-dimensional and nonlinear nature, the model properties can be understood in terms of few canonical positive and negative feedback motifs localized in distinct timescales. We use this insight to shed light on a key problem in experimental neurophysiology, the challenge of understanding the sensitivity of neuronal behaviors to underlying parameters in empirically-derived models. Finally, we show how sensitivity analysis of neuronal excitability relates to robustness and regulation of neuronal behaviors.This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office. G.D. is a Marie-Curie COFUND postdoctoral fellow at the University of Liege. Co-funded by the European Union. J.D. is supported by the F.R.S.-FNRS (Belgian Fund for Scientific Research. The scientific responsibility rests with its authors.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/CDC.2015.740249

Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle

Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function. To address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of spikes from the cerebral cortex (V1 and S1) and hippocampus (HP) of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from V1, S1 and HP. Altogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.Comment: 14 pages, 9 figures, supporting material included (published in Plos One

Cellular automata and artificial brain dynamics

[EN] Brain dynamics, neuron activity, information transfer in brains, etc., are a vast field where a large number of questions remain unsolved. Nowadays, computer simulation is playing a key role in the study of such an immense variety of problems. In this work, we explored the possibility of studying brain dynamics using cellular automata, more precisely the famous Game of Life (GoL). The model has some important features (i.e., pseudo-criticality, 1/f noise, universal computing), which represent good reasons for its use in brain dynamics modelling. We have also considered that the model maintains sufficient flexibility. For instance, the timestep is arbitrary, as are the spatial dimensions. As first steps in our study, we used the GoL to simulate the evolution of several neurons (i.e., a statistically significant set, typically a million neurons) and their interactions with the surrounding ones, as well as signal transfer in some simple scenarios. The way that signals (or life) propagate across the grid was described, along with a discussion on how this model could be compared with brain dynamics. Further work and variations of the model were also examined.This work was partially supported by the European Union's Seventh Framework Programme (FP7-REGPOT-2012-2013-1) under grant agreement no 316165. This work was done with the support of the Czech Science Foundation, project 17-17921S.Fraile, A.; Panagiotakis, E.; Christakis, N.; Acedo Rodríguez, L. (2018). Cellular automata and artificial brain dynamics. Mathematical and Computational Applications (Online). 23(4):1-23. https://doi.org/10.3390/mca23040075S123234TURING, A. M. (1950). I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind, LIX(236), 433-460. doi:10.1093/mind/lix.236.433Sarkar, P. (2000). A brief history of cellular automata. ACM Computing Surveys, 32(1), 80-107. doi:10.1145/349194.349202Ermentrout, G. B., & Edelstein-Keshet, L. (1993). Cellular Automata Approaches to Biological Modeling. Journal of Theoretical Biology, 160(1), 97-133. doi:10.1006/jtbi.1993.1007Boccara, N., Roblin, O., & Roger, M. (1994). Automata network predator-prey model with pursuit and evasion. Physical Review E, 50(6), 4531-4541. doi:10.1103/physreve.50.4531Gerhardt, M., & Schuster, H. (1989). A cellular automaton describing the formation of spatially ordered structures in chemical systems. Physica D: Nonlinear Phenomena, 36(3), 209-221. doi:10.1016/0167-2789(89)90081-xZhu, M. F., Lee, S. Y., & Hong, C. P. (2004). Modified cellular automaton model for the prediction of dendritic growth with melt convection. Physical Review E, 69(6). doi:10.1103/physreve.69.061610KANSAL, A. R., TORQUATO, S., HARSH, G. R., CHIOCCA, E. A., & DEISBOECK, T. S. (2000). Simulated Brain Tumor Growth Dynamics Using a Three-Dimensional Cellular Automaton. Journal of Theoretical Biology, 203(4), 367-382. doi:10.1006/jtbi.2000.2000Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554-2558. doi:10.1073/pnas.79.8.2554TSOUTSOURAS, V., SIRAKOULIS, G. C., PAVLOS, G. P., & ILIOPOULOS, A. C. (2012). SIMULATION OF HEALTHY AND EPILEPTIFORM BRAIN ACTIVITY USING CELLULAR AUTOMATA. International Journal of Bifurcation and Chaos, 22(09), 1250229. doi:10.1142/s021812741250229xAcedo, L., Lamprianidou, E., Moraño, J.-A., Villanueva-Oller, J., & Villanueva, R.-J. (2015). Firing patterns in a random network cellular automata model of the brain. Physica A: Statistical Mechanics and its Applications, 435, 111-119. doi:10.1016/j.physa.2015.05.017Chialvo, D. R. (2010). Emergent complex neural dynamics. Nature Physics, 6(10), 744-750. doi:10.1038/nphys1803Priesemann, V. (2014). Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Frontiers in Systems Neuroscience, 8. doi:10.3389/fnsys.2014.00108Langton, C. G. (1990). Computation at the edge of chaos: Phase transitions and emergent computation. Physica D: Nonlinear Phenomena, 42(1-3), 12-37. doi:10.1016/0167-2789(90)90064-vFriedman, N., Ito, S., Brinkman, B. A. W., Shimono, M., DeVille, R. E. L., Dahmen, K. A., … Butler, T. C. (2012). Universal Critical Dynamics in High Resolution Neuronal Avalanche Data. Physical Review Letters, 108(20). doi:10.1103/physrevlett.108.208102Kello, C. T. (2013). Critical branching neural networks. Psychological Review, 120(1), 230-254. doi:10.1037/a0030970Werner, G. (2007). Metastability, criticality and phase transitions in brain and its models. Biosystems, 90(2), 496-508. doi:10.1016/j.biosystems.2006.12.001Bak, P., Chen, K., & Creutz, M. (1989). Self-organized criticality in the ’Game of Life". Nature, 342(6251), 780-782. doi:10.1038/342780a0Hemmingsson, J. (1995). Consistent results on ‘Life’. Physica D: Nonlinear Phenomena, 80(1-2), 151-153. doi:10.1016/0167-2789(95)90071-3Nordfalk, J., & Alstrøm, P. (1996). Phase transitions near the «game of Life». Physical Review E, 54(2), R1025-R1028. doi:10.1103/physreve.54.r1025Ninagawa, S., Yoneda, M., & Hirose, S. (1998). 1ƒ fluctuation in the «Game of Life». Physica D: Nonlinear Phenomena, 118(1-2), 49-52. doi:10.1016/s0167-2789(98)00025-6Allegrini, P., Menicucci, D., Bedini, R., Fronzoni, L., Gemignani, A., Grigolini, P., … Paradisi, P. (2009). Spontaneous brain activity as a source of ideal1/fnoise. Physical Review E, 80(6). doi:10.1103/physreve.80.061914Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8(9), 700-711. doi:10.1038/nrn2201Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J. (2001). Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations. The Journal of Neuroscience, 21(4), 1370-1377. doi:10.1523/jneurosci.21-04-01370.2001Gilden, D., Thornton, T., & Mallon, M. (1995). 1/f noise in human cognition. Science, 267(5205), 1837-1839. doi:10.1126/science.7892611Bédard, C., Kröger, H., & Destexhe, A. (2006). Does the1/fFrequency Scaling of Brain Signals Reflect Self-Organized Critical States? Physical Review Letters, 97(11). doi:10.1103/physrevlett.97.118102Wolfram, S. (1983). Statistical mechanics of cellular automata. Reviews of Modern Physics, 55(3), 601-644. doi:10.1103/revmodphys.55.601“Life Universal Computer”http://www.igblan.free-online.co.uk/igblan/ca/Bagnoli, F., Rechtman, R., & Ruffo, S. (1991). Some facts of life. Physica A: Statistical Mechanics and its Applications, 171(2), 249-264. doi:10.1016/0378-4371(91)90277-jGarcia, J. B. C., Gomes, M. A. F., Jyh, T. I., Ren, T. I., & Sales, T. R. M. (1993). Nonlinear dynamics of the cellular-automaton ‘‘game of Life’’. Physical Review E, 48(5), 3345-3351. doi:10.1103/physreve.48.3345Huang, S.-Y., Zou, X.-W., Tan, Z.-J., & Jin, Z.-Z. (2003). Network-induced nonequilibrium phase transition in the «game of Life». Physical Review E, 67(2). doi:10.1103/physreve.67.026107Blok, H. J., & Bergersen, B. (1999). Synchronous versus asynchronous updating in the «game of Life». Physical Review E, 59(4), 3876-3879. doi:10.1103/physreve.59.3876Schönfisch, B., & de Roos, A. (1999). Synchronous and asynchronous updating in cellular automata. Biosystems, 51(3), 123-143. doi:10.1016/s0303-2647(99)00025-8Reia, S. M., & Kinouchi, O. (2014). Conway’s game of life is a near-critical metastable state in the multiverse of cellular automata. Physical Review E, 89(5). doi:10.1103/physreve.89.052123De la Torre, A. C., & Mártin, H. O. (1997). A survey of cellular automata like the «game of life». Physica A: Statistical Mechanics and its Applications, 240(3-4), 560-570. doi:10.1016/s0378-4371(97)00046-0Beer, R. D. (2004). Autopoiesis and Cognition in the Game of Life. Artificial Life, 10(3), 309-326. doi:10.1162/1064546041255539Beer, R. D. (2014). The Cognitive Domain of a Glider in the Game of Life. Artificial Life, 20(2), 183-206. doi:10.1162/artl_a_00125Yuste, S. B., & Acedo, L. (2000). Number of distinct sites visited byNrandom walkers on a Euclidean lattice. Physical Review E, 61(3), 2340-2347. doi:10.1103/physreve.61.2340Lachaux, J.-P., Pezard, L., Garnero, L., Pelte, C., Renault, B., Varela, F. J., & Martinerie, J. (1997). Spatial extension of brain activity fools the single-channel reconstruction of EEG dynamics. Human Brain Mapping, 5(1), 26-47. doi:10.1002/(sici)1097-0193(1997)5:13.0.co;2-pMcDowell, J. E., Kissler, J. M., Berg, P., Dyckman, K. A., Gao, Y., Rockstroh, B., & Clementz, B. A. (2005). Electroencephalography/magnetoencephalography study of cortical activities preceding prosaccades and antisaccades. NeuroReport, 16(7), 663-668. doi:10.1097/00001756-200505120-00002Holsheimer, J., & Feenstra, B. W. . (1977). Volume conduction and EEG measurements within the brain: A quantitative approach to the influence of electrical spread on the linear relationship of activity measured at different locations. Electroencephalography and Clinical Neurophysiology, 43(1), 52-58. doi:10.1016/0013-4694(77)90194-8Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764Porooshani, H., Porooshani, A. H., Gannon, L., & Kyle, G. M. (2004). Speed of progression of migrainous visual aura measured by sequential field assessment. Neuro-Ophthalmology, 28(2), 101-105. doi:10.1076/noph.28.2.101.23739Hutsler, J. J. (2003). The specialized structure of human language cortex: Pyramidal cell size asymmetries within auditory and language-associated regions of the temporal lobes. Brain and Language, 86(2), 226-242. doi:10.1016/s0093-934x(02)00531-xWilson, H. R., & Cowan, J. D. (1972). Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons. Biophysical Journal, 12(1), 1-24. doi:10.1016/s0006-3495(72)86068-5Conway’s Game of Life. Examples of patternshttps://en.wikipedia.org/wiki/Conway%27s_Game_of_Life#Examples_of_patternsGardner, M. (1970). Mathematical Games. Scientific American, 223(4), 120-123. doi:10.1038/scientificamerican1070-120Packard, N. H., & Wolfram, S. (1985). Two-dimensional cellular automata. Journal of Statistical Physics, 38(5-6), 901-946. doi:10.1007/bf01010423Nunomura, A., Perry, G., Aliev, G., Hirai, K., Takeda, A., Balraj, E. K., … Smith, M. A. (2001). Oxidative Damage Is the Earliest Event in Alzheimer Disease. Journal of Neuropathology & Experimental Neurology, 60(8), 759-767. doi:10.1093/jnen/60.8.759Kitamura, T., Ogawa, S. K., Roy, D. S., Okuyama, T., Morrissey, M. D., Smith, L. M., … Tonegawa, S. (2017). Engrams and circuits crucial for systems consolidation of a memory. Science, 356(6333), 73-78. doi:10.1126/science.aam6808Anderson, P. W. (1972). More Is Different. Science, 177(4047), 393-396. doi:10.1126/science.177.4047.39

Active dendrites enhance neuronal dynamic range

Since the first experimental evidences of active conductances in dendrites, most neurons have been shown to exhibit dendritic excitability through the expression of a variety of voltage-gated ion channels. However, despite experimental and theoretical efforts undertaken in the last decades, the role of this excitability for some kind of dendritic computation has remained elusive. Here we show that, owing to very general properties of excitable media, the average output of a model of active dendritic trees is a highly non-linear function of their afferent rate, attaining extremely large dynamic ranges (above 50 dB). Moreover, the model yields double-sigmoid response functions as experimentally observed in retinal ganglion cells. We claim that enhancement of dynamic range is the primary functional role of active dendritic conductances. We predict that neurons with larger dendritic trees should have larger dynamic range and that blocking of active conductances should lead to a decrease of dynamic range.Comment: 20 pages, 6 figure

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications

In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, the steep curve of implementation and of development of CMOS is gradually reaching a plateau. It is clear the need of studying new elements that can combine the efficiency of transistors and at the same time increase the complexity of the operations. Memristors can be described as non-linear resistors capable of maintaining memory of the resistance state that they reached. From their first theoretical treatment by Professor Leon O. Chua in 1971, different research groups have devoted their expertise in studying the both the fabrication and the implementation of this new promising technology. In the following thesis a complete study on memristors and memristive elements is presented. The road map that characterizes this study departs from a deep understanding of the physics that govern memristors, focusing on the HP model by Dr. Stanley Williams. Other devices such as phase change memories (PCMs) and memristive biosensors made with Si nano-wires have been studied, developing emulators and equivalent circuitry, in order to describe their complex dynamics. This part sets the first milestone of a pathway that passes trough more complex implementations such as neuromorphic systems and neural networks based on memristors proving their computing efficiency. Finally it will be presented a memristror-based technology, covered by patent, demonstrating its efficacy for clinical applications. The presented system has been designed for detecting and assessing automatically chronic wounds, a syndrome that affects roughly 2% of the world population, through a Cellular Automaton which analyzes and processes digital images of ulcers. Thanks to its precision in measuring the lesions the proposed solution promises not only to increase healing rates, but also to prevent the worsening of the wounds that usually lead to amputation and death