Searching for principles of brain computation

Current Opinion in Behavioral Sciences - Tập 11 - Trang 81-92 - 2016
Wolfgang Maass1
1Graz University of Technology, Institute for Theoretical Computer Science, Inffeldgasse 16b/I, A-8010 Graz, Austria

Tài liệu tham khảo

Dupre, 2015, Calcium imaging reveals multiple conduction systems in hydra Kato, 2015, Global brain dynamics embed the motor command sequence of Caenorhabditis elegans, Cell, 163, 656, 10.1016/j.cell.2015.09.034 Portugues, 2014, Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior, Neuron, 81, 1328, 10.1016/j.neuron.2014.01.019 Singer, 2013, Cortical dynamics revisited, Trends Cogn Sci, 17, 616, 10.1016/j.tics.2013.09.006 Yuste, 2015, On testing neural network models, Nat Rev Neurosci, 16, 10.1038/nrn4043 van Vreeswijk, 1996, Chaos in neuronal networks with balanced excitatory and inhibitory activity, Science, 274, 1724, 10.1126/science.274.5293.1724 Vogels, 2005, Neural network dynamics, Ann Rev Neurosci, 28, 357, 10.1146/annurev.neuro.28.061604.135637 Gerstner, 2014 Harris, 2015, The neocortical circuit: themes and variations, Nat Neurosci, 18, 170, 10.1038/nn.3917 Gupta, 2000, Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex, Science, 287, 273, 10.1126/science.287.5451.273 Larsen, 2015, Synapse-type-specific plasticity in local circuits, Curr Opin Neurobiol, 35, 127, 10.1016/j.conb.2015.08.001 Gjorgjieva, 2016, Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance, Curr Opin Neurobiol, 37, 44, 10.1016/j.conb.2015.12.008 Smolensky, 1990, Tensor product variable binding and the representation of symbolic structures in connectionist systems, Artif Intell, 46, 159, 10.1016/0004-3702(90)90007-M Maass, 2002, Real-time computing without stable states: a new framework for neural computation based on perturbations, Neural Computation, 14, 2531, 10.1162/089976602760407955 Maass, 2004, Fading memory and kernel properties of generic cortical microcircuit models, J Physiol, Paris, 98, 315, 10.1016/j.jphysparis.2005.09.020 Buonomano, 2009, State-dependent computations: spatiotemporal processing in cortical networks, Nat Rev Neurosci, 10, 113, 10.1038/nrn2558 Jaeger, 2001, The “echo state” approach to analysing and training recurrent neural networks Legenstein, 2007, Edge of chaos and prediction of computational performance for neural circuit models, Neural Netw, 20, 323, 10.1016/j.neunet.2007.04.017 Sussillo, 2009, Generating coherent patterns of activity from chaotic neural networks, Neuron, 63, 544, 10.1016/j.neuron.2009.07.018 Hoerzer, 2014, Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning, Cerebral Cortex, 24, 677, 10.1093/cercor/bhs348 Maass, 2005, Methods for estimating the computational power and generalization capability of neural microcircuits, vol 17, 865 Rigotti, 2013, The importance of mixed selectivity in complex cognitive tasks, Nature, 497, 585, 10.1038/nature12160 Bishop, 2006 Fusi, 2016, Why neurons mix: high dimensionality for higher cognition, Curr Opin Neurobiol, 37, 66, 10.1016/j.conb.2016.01.010 Olshausen, 2005, How close are we to understanding V1?, Neural Comput, 17, 1665, 10.1162/0899766054026639 Chen, 2013, Behaviour-dependent recruitment of long-range projection neurons in somatosensory cortex, Nature, 499, 336, 10.1038/nature12236 Maass, 2004, On the computational power of circuits of spiking neurons, J Comp System Sci, 69, 593, 10.1016/j.jcss.2004.04.001 Dranias, 2013, Short-term memory in networks of dissociated cortical neurons, J Neurosci, 33, 1940, 10.1523/JNEUROSCI.2718-12.2013 Marre, 2015, High accuracy decoding of dynamical motion from a large retinal population, PLoS Comput Biol, 11, e1004304, 10.1371/journal.pcbi.1004304 Nikolic, 2009, Distributed fading memory for stimulus properties in the primary visual cortex, PLoS Biol, 7, 1, 10.1371/journal.pbio.1000260 Klampfl, 2012, A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons, J Neurophysiol, 108, 1366, 10.1152/jn.00935.2011 Goldman, 2009, Memory without feedback in a neural network, Neuron, 61, 621, 10.1016/j.neuron.2008.12.012 Bernacchia, 2011, A reservoir of time constants for memory traces in cortical neurons, Nat Neurosci, 14, 366, 10.1038/nn.2752 Stokes, 2015, ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework, Trends Cogn Sci, 19, 394, 10.1016/j.tics.2015.05.004 Haeusler, 2007, A statistical analysis of information processing properties of lamina-specific cortical microcircuit models, Cereb Cortex, 17, 149, 10.1093/cercor/bhj132 Sussillo, 2007, Self-tuning of neural circuits through short-term synaptic plasticity, J Neurophysiol, 97, 4079, 10.1152/jn.01357.2006 Maass, 2007, Computational aspects of feedback in neural circuits, PLoS Comput Biol, 3, e165, 10.1371/journal.pcbi.0020165 Mante, 2013, Context-dependent computation by recurrent dynamics in prefrontal cortex, Nature, 503, 78, 10.1038/nature12742 Jaeger, 2004, Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication, Science, 304, 78, 10.1126/science.1091277 Thalmeier D, Uhlmann M, Kappen HJ, Memmesheimer RM. Learning Universal Computations with Spikes. 2015; arXiv preprint arXiv:150507866. Luczak, 2015, Packet-based communication in the cortex, Nat Rev Neurosci, 16, 745, 10.1038/nrn4026 Bathellier, 2012, Discrete neocortical dynamics predict behavioral categorization of sounds, Neuron, 76, 435, 10.1016/j.neuron.2012.07.008 Miller, 2014, Visual stimuli recruit intrinsically generated cortical ensembles, Proc Natl Acad Sci, 111, E4053, 10.1073/pnas.1406077111 Luczak, 2009, Spontaneous events outline the realm of possible sensory responses in neocortical populations, Neuron, 62, 413, 10.1016/j.neuron.2009.03.014 Sadovsky, 2014, Mouse visual neocortex supports multiple stereotyped patterns of microcircuit activity, J Neurosci, 34, 7769, 10.1523/JNEUROSCI.0169-14.2014 Fujisawa, 2008, Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex, Nat Neurosci, 11, 823, 10.1038/nn.2134 Harvey, 2012, Choice-specific sequences in parietal cortex during a virtual-navigation decision task, Nature, 484, 62, 10.1038/nature10918 Izhikevich, 2004, Spike-timing dynamics of neuronal groups, Cereb Cortex, 14, 933, 10.1093/cercor/bhh053 Klampfl, 2013, Emergence of dynamic memory traces in cortical microcircuit models through STDP, J Neurosci, 33, 11515, 10.1523/JNEUROSCI.5044-12.2013 Pokorny, 2016 Litwin-Kumar, 2014, Formation and maintenance of neuronal assemblies through synaptic plasticity, Nat Commun, 5 Hebb, 1949 Buzsaki, 2010, Neural syntax: cell assemblies, synapsembles, and readers, Neuron, 68, 362, 10.1016/j.neuron.2010.09.023 Branco, 2009, The probability of neurotransmitter release: variability and feedback control at single synapes, Nat Rev Neurosci, 10, 373, 10.1038/nrn2634 Kavalali, 2015, The mechanisms and functions of spontaneous neurotransmitter release, Nat Rev Neurosci, 16, 5, 10.1038/nrn3875 Maass, 2014, Noise as a resource for computation and learning in networks of spiking neurons, Special Issue of the Proc of the IEEE on ‘Engineering Intelligent Electronic Systems based on Computational Neuroscience’, 102, 860 Leopold, 1999, Multistable phenomena: changing views in perception, Trends Cogn Sci, 3, 254, 10.1016/S1364-6613(99)01332-7 Jezek, 2011, Theta-paced flickering between place-cell maps in the hippocampus, Nature, 478, 246, 10.1038/nature10439 Rich, 2016, Decoding subjective decisions from orbitofrontal cortex, Nat Neurosci, 10.1038/nn.4320 Berkes, 2011, Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment, Science, 331, 83, 10.1126/science.1195870 Buesing, 2011, Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons, PLoS Comput Biol, 7, e1002211, 10.1371/journal.pcbi.1002211 Pecevski, 2011, Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons, PLoS Comput Biol, 7, e1002294, 10.1371/journal.pcbi.1002294 Pecevski D, Maass W: Learning probabilistic inference through STDP. eNeuro 2016, in press. Knill, 1991, Apparent surface curvature affects lightness perception, Nature, 351, 228, 10.1038/351228a0 Habenschuss, 2013, Stochastic computations in cortical microcircuit models, PLoS Comput Biol, 9, e1003311, 10.1371/journal.pcbi.1003311 Savin, 2014, Spatio-temporal representations of uncertainty in spiking neural networks, Adv Neural Inform Process Syst, 2024 Legenstein, 2014, Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment, PLoS Comput Biol, 10, e1003859, 10.1371/journal.pcbi.1003859 Aarts, 1988 Tversky, 1974, Judgment under uncertainty: heuristics and biases, Science, 185, 1124, 10.1126/science.185.4157.1124 Gigerenzer, 1991, Probabilistic mental models: a Brunswikian theory of confidence, Psychol Rev, 98, 506, 10.1037/0033-295X.98.4.506 Tenenbaum, 2011, How to grow a mind: statistics, structure, and abstraction, Science, 331, 1279, 10.1126/science.1192788 Pouget, 2013, Probabilistic brains: knowns and unknowns, Nat Neurosci, 16, 1170, 10.1038/nn.3495 Meyniel, 2015, Confidence as Bayesian probability: from neural origins to behavior, Neuron, 88, 78, 10.1016/j.neuron.2015.09.039 Holtmaat, 2009, Experience-dependent structural synaptic plasticity in the mammalian brain, Nat Rev Neurosci, 10, 647, 10.1038/nrn2699 Stettler, 2006, Axons and synaptic boutons are highly dynamic in adult visual cortex, Neuron, 49, 877, 10.1016/j.neuron.2006.02.018 Loewenstein, 2015, Predicting the dynamics of network connectivity in the neocortex, J Neurosci, 35, 12535, 10.1523/JNEUROSCI.2917-14.2015 Spitzer, 2015, Neurotransmitter switching? No surprise, Neuron, 86, 1131, 10.1016/j.neuron.2015.05.028 Ziv, 2013, Long-term dynamics of CA1 hippocampal place codes, Nat Neurosci, 16, 264, 10.1038/nn.3329 Kappel, 2015, Network plasticity as Bayesian inference, PLoS Comput Biol, 11, e1004485, 10.1371/journal.pcbi.1004485 MacKay, 1992, Bayesian interpolation, Neural Comput, 4, 415, 10.1162/neco.1992.4.3.415 Marder, 2006, Variability, compensation and homeostasis in neuron and network function, Nat Rev Neurosci, 7, 563, 10.1038/nrn1949 Marr, 1976 Maass, 2006, Theory of the computational function of microcircuit dynamics