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The role of single neurons in information processing

Christof Koch, Idan Segev
2000 Nature Neuroscience  
In a 'leaky' integrate-and-fire unit, an ohmic resistance is added in parallel to the capacitance, accounting for the loss of synaptic charge via the resistor and, consequently, the decay of the synaptic  ...  Networks containing hundreds or thousands of such units that utterly neglect the geometry of real neurons are commonly used in pattern recognition (for example, to predict credit card fraud) and at most  ...  ACKNOWLEDGEMENTS Work in the laboratories of the authors is supported by the NSF/ERC program, NIMH, ONR, Israeli Science Foundation and the BSF. We thank R. Nitzan for Fig. 2a , Y.  ... 
doi:10.1038/81444 pmid:11127834 fatcat:wpsetsmslfbf7cfyorjzv2qww4

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

Werner Van Geit, Michael Gevaert, Giuseppe Chindemi, Christian Rössert, Jean-Denis Courcol, Eilif B. Muller, Felix Schürmann, Idan Segev, Henry Markram
2016 Frontiers in Neuroinformatics  
Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its  ...  Parametrising such models to conform to the multitude of available experimental constraints is a global nonlinear optimisation problem with a complex fitness landscape, requiring numerical techniques to  ...  Typically, an inhibitory axon contacts a particular dendritic subdomain of its target neuron, where it often makes 10-20 synapses, sometimes on very distal branches.  ... 
doi:10.3389/fninf.2016.00017 pmid:27375471 pmcid:PMC4896051 fatcat:n5ejvfz4crhxtbszkjvzydxg5e

Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex

Jeff Hawkins, Subutai Ahmad
2016 Frontiers in Neural Circuits  
It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns.  ...  by a neuron act as predictions by slightly depolarizing the neuron without immediately generating an action potential.  ...  The sequence memory model learns continuously, uses variable amounts of temporal context to make predictions, can make multiple simultaneous predictions, uses only local learning rules, and is robust to  ... 
doi:10.3389/fncir.2016.00023 pmid:27065813 pmcid:PMC4811948 fatcat:kmyuakg4r5echksomnrinpjhri

Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks

Spyridon Chavlis, Panayiota Poirazi
2021 Zenodo  
We discuss the computational benefits provided by these features in biological neurons and suggest ways to adapt them in artificial neurons in order to exploit the respective benefits in machine learning  ...  This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications.  ...  Conflict of interest statement The authors declare no conflict of interest.  ... 
doi:10.5281/zenodo.4955396 fatcat:p6dqlc64vzd6dkaf3hkkyeudae

Porting HTM Models to the Heidelberg Neuromorphic Computing Platform [article]

Sebastian Billaudelle, Subutai Ahmad
2016 arXiv   pre-print
More generally the exercise of porting high level HTM algorithms to biophysical neuron models promises to be a fruitful area of investigation for future studies.  ...  The Heidelberg Neuromorphic Computing Platform, developed as part of the Human Brain Project (HBP), is a mixed-signal (analog and digital) large-scale platform for modeling networks of spiking neurons.  ...  ACKNOWLEDGEMENTS Special thanks to Je Hawkins, Prof. Dr. Karlheinz Meier, Paxon Frady, and the Numenta Team.  ... 
arXiv:1505.02142v2 fatcat:ws23vnxfy5av3ao36f65fyj4bq

Neurons as Canonical Correlation Analyzers

Cengiz Pehlevan, Xinyuan Zhao, Anirvan M. Sengupta, Dmitri Chklovskii
2020 Frontiers in Computational Neuroscience  
To model networks of pyramidal neurons, we introduce a novel multi-channel CCA objective function, and derive from it an online gradient-based optimization algorithm whose steps can be interpreted as the  ...  Finally, we confirm the functionality of our networks via numerical simulations.  ...  to the model of a single pyramidal neuron and extend it to neurons with more than two dendritic compartments using a multiview single-channel CCA, i.e., CCA on multiple sets of variables (Kettenring,  ... 
doi:10.3389/fncom.2020.00055 pmid:32694989 pmcid:PMC7338892 fatcat:dwptlxzxmrfpjilcuf5t2u2isi

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

Michele Giugliano, Giancarlo La Camera, Stefano Fusi, Walter Senn
2008 Biological cybernetics  
The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane's inherent time constants.  ...  For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually.  ...  As a consequence, some of the neurons are ready to generate an action potential quickly (order of 1 ms) in response to a stimulus.  ... 
doi:10.1007/s00422-008-0270-9 pmid:19011920 fatcat:ndapx5ezbzhtli6sfpa2o7aloe

Passive Normalization of Synaptic Integration Influenced by Dendritic Architecture

David B. Jaffe, Nicholas T. Carnevale
1999 Journal of Neurophysiology  
We examined how biophysical properties and neuronal morphology affect the propagation of individual postsynaptic potentials (PSPs) from synaptic inputs to the soma.  ...  Therefore the spread of PSPs throughout a dendritic tree can be described in terms of transfer impedance (Z c ), which reflects how a current applied at one location affects membrane potential at other  ...  Hubbard for the electrophysiology, histology, and 3-D reconstructions that constituted a major portion of the neuron morphology used in this study. We also wish to thank Dr. Brenda J.  ... 
doi:10.1152/jn.1999.82.6.3268 pmid:10601459 fatcat:7frt6srf5rervjmhhqt7i4dndi

Nonlinear Dendritic Coincidence Detection for Supervised Learning [article]

Fabian Schubert, Claudius Gros
2021 arXiv   pre-print
Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field.  ...  In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals.  ...  Acknowledgments The authors acknowledge the financial support of the German Research Foundation (DFG) Data Availability Statement The simulation datasets for this study can be found under  ... 
arXiv:2107.05336v1 fatcat:ff6jtym3bbfnbip2siy2fxloaa

Regulation of AMPA and NMDA receptor-mediated EPSPs in dendritic trees of thalamocortical cells

Francis Lajeunesse, Helmut Kröger, Igor Timofeev
2013 Journal of Neurophysiology  
Two main excitatory synapses are formed at the dendritic arbor of first-order nuclei thalamocortical (TC) neurons.  ...  Ascending sensory axons primarily establish contacts at large proximal dendrites, whereas descending corticothalamic fibers form synapses on thin distal dendrites.  ...  Despite some variability in the amplitude of somatic responses, this variability is rather small compared with the variability of local responses in dendrites, except for neurons lacking an electrotonically  ... 
doi:10.1152/jn.01090.2011 pmid:23100131 pmcid:PMC3545156 fatcat:ac4uwjdjwfhpjlnq4o6bcpztqq

The HTM Learning Algorithm [chapter]

Kjell Jørgen Hole
2016 Anti-fragile ICT Systems  
Second, the chapter outlines why HTM is an improvement over these earlier approaches.  ...  To grasp HTM's novelty and importance, the current chapter first discusses the approach to learning taken by traditional artificial intelligence (AI) research, as well as efforts to "train" artificial  ...  The ability to predict using variable-length sequences of patterns is due to the variable order memory of HTM.  ... 
doi:10.1007/978-3-319-30070-2_11 fatcat:lgqacnmdc5dcxgz7itwj63zeam

Computational Neuroscience: Capturing the Essence [chapter]

Shaul Druckmann, Albert Gidon, Idan Segev
2013 Neurosciences - From Molecule to Behavior: a university textbook  
Ultimately, CN aims to understand, via mathematical theory, how do high-level phenomena such as cognition, emotions, creativity, A quantitative understanding of how neurons compute means to link, via theory  ...  The research agenda of computational neuroscience ( CN ) is to use theoretical tools in order to understand how the different elements composing the nervous system: membrane ion channels, synapses, neurons  ...  However, due to the intrinsic variability inherent to neurons, responses to the exact same stimulus in the same neuron can vary considerably, making the approach of trying to bring the model in agreement  ... 
doi:10.1007/978-3-642-10769-6_30 fatcat:elx5fqmkibdn7c2k2imy5lf6me

A Theoretical Framework for the Dynamics of Multiple Intrinsic Oscillators in Single Neurons [chapter]

Michiel W. H. Remme, Máté Lengyel, Boris S. Gutkin
2011 Phase Response Curves in Neuroscience  
The dendritic tree contributes significantly to the elementary computations a neuron performs while converting its synaptic inputs into action potential output.  ...  Under this account, neurons compute nearinstantaneous mappings from their current input to their current output, brought about by somatic summation of dendritic contributions that are generated in functionally  ...  The mathematical approach that we used, builds on several studies which focused on the interaction between two neurons with repetitively spiking somata that interact via inputs at the dendrites (Crook  ... 
doi:10.1007/978-1-4614-0739-3_3 fatcat:kcko5ovbtnbwxb733llxtrzm7u

Robustness, variability, phase dependence, and longevity of individual synaptic input effects on spike timing during fluctuating synaptic backgrounds: A modeling study of globus pallidus neuron phase response properties

N.W. Schultheiss, J.R. Edgerton, D. Jaeger
2012 Neuroscience  
roles of perisomatic and distal dendritic synapses in the control of patterned network activity.  ...  We found that the variability in responses to PRC stimuli and the incidence of stimulusevoked added or skipped spikes were stimulus-phase-dependent and reflected the profile of the average PRC, suggesting  ...  Acknowledgments The authors would like to thank Roberto Fernandez Galan for constructive and insightful feedback during the preparation of the manuscript.  ... 
doi:10.1016/j.neuroscience.2012.05.059 pmid:22659567 pmcid:PMC3402697 fatcat:p2fcwdadjfabdkzeslkeqmm4v4

The Spike-Timing Dependence of Plasticity

Daniel E. Feldman
2012 Neuron  
This review summarizes this broader view of plasticity, including the forms and cellular mechanisms for the spike-timing dependence of plasticity, and, the evidence that spike timing is an important determinant  ...  In spike-timing-dependent plasticity (STDP), the order and precise temporal interval between presynaptic and postsynaptic spikes determine the sign and magnitude of long-term potentiation (LTP) or depression  ...  In L5 pyramidal cell distal dendrites, EPSPs occurring <10 ms prior to the bAP enhance bAP amplitude 3-fold via recruitment of dendritic sodium channels (Stuart and Hä usser, 2001) .  ... 
doi:10.1016/j.neuron.2012.08.001 pmid:22920249 pmcid:PMC3431193 fatcat:e2swxi5oivdttao2tgfkt7ojnm
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