scholarly article | Q13442814 |
P818 | arXiv ID | 1502.04993 |
P8978 | DBLP publication ID | journals/jcns/ZaytsevMD15 |
P356 | DOI | 10.1007/S10827-015-0565-5 |
P932 | PMC publication ID | 4493949 |
P698 | PubMed publication ID | 26041729 |
P5875 | ResearchGate publication ID | 272521830 |
P894 | zbMATH Open document ID | 1382.92102 |
P50 | author | Moritz Deger | Q41046459 |
P2093 | author name string | Abigail Morrison | |
Yury V Zaytsev | |||
P2860 | cites work | Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex | Q21145311 |
Normalized auto- and cross-covariance functions for neuronal spike train analysis | Q43439460 | ||
Whole-brain functional imaging at cellular resolution using light-sheet microscopy | Q44984064 | ||
Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. | Q46818698 | ||
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation | Q47735868 | ||
Exact subthreshold integration with continuous spike times in discrete-time neural network simulations | Q47846164 | ||
Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons | Q47899491 | ||
Likelihood methods for point processes with refractoriness | Q47952594 | ||
Predicting spike timing of neocortical pyramidal neurons by simple threshold models | Q48570869 | ||
Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity | Q48826398 | ||
Learning precisely timed spikes. | Q50667723 | ||
Maximum likelihood estimation of cascade point-process neural encoding models | Q51634878 | ||
Modeling compositionality by dynamic binding of synfire chains | Q51635933 | ||
Stable propagation of synchronous spiking in cortical neural networks | Q52133631 | ||
Dynamics of neuronal firing correlation: modulation of "effective connectivity". | Q52533697 | ||
A Limited Memory Algorithm for Bound Constrained Optimization | Q56560278 | ||
On the limited memory BFGS method for large scale optimization | Q57311064 | ||
Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation | Q72345644 | ||
Symbolic transfer entropy | Q81376767 | ||
On quadrature methods for refractory point process likelihoods | Q85363349 | ||
A Granger causality measure for point process models of ensemble neural spiking activity | Q21563486 | ||
Network anatomy and in vivo physiology of visual cortical neurons | Q24633731 | ||
Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone | Q28534619 | ||
Structural and molecular interrogation of intact biological systems | Q29616195 | ||
State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data | Q30000858 | ||
Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround | Q30475797 | ||
A synaptic organizing principle for cortical neuronal groups | Q30499294 | ||
Inference of neuronal network spike dynamics and topology from calcium imaging data | Q30560538 | ||
Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models | Q30571267 | ||
Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model | Q30587225 | ||
The elusive concept of brain connectivity | Q30804916 | ||
Common-input models for multiple neural spike-train data | Q31133418 | ||
Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes | Q33646280 | ||
A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro | Q33656914 | ||
Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data | Q33715208 | ||
Wiring specificity in the direction-selectivity circuit of the retina | Q34169590 | ||
A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects | Q34346784 | ||
A mesoscale connectome of the mouse brain | Q34413358 | ||
From electrical to chemical transmission in the central nervous system | Q34525896 | ||
Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect? | Q34565655 | ||
Modeling the impact of common noise inputs on the network activity of retinal ganglion cells | Q36577782 | ||
Spatio-temporal correlations and visual signalling in a complete neuronal population | Q37197936 | ||
Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions | Q37249035 | ||
Inferring functional connections between neurons. | Q37350285 | ||
The science of neural interface systems | Q37463140 | ||
Optical probing of neuronal ensemble activity | Q37620903 | ||
In vitro microelectrode array technology and neural recordings | Q37864658 | ||
Multi-electrode array technologies for neuroscience and cardiology | Q38079118 | ||
Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms | Q39677048 | ||
A general and efficient method for incorporating precise spike times in globally time-driven simulations | Q39822985 | ||
Bayesian inference for generalized linear models for spiking neurons | Q41276496 | ||
Maximum likelihood analysis of spike trains of interacting nerve cells | Q41400005 | ||
Bayesian inference of functional connectivity and network structure from spikes | Q41824474 | ||
Inferring synaptic connectivity from spatio-temporal spike patterns | Q41877412 | ||
Structural plasticity of circuits in cortical neuropil | Q42145044 | ||
Fast inference in generalized linear models via expected log-likelihoods | Q42263616 | ||
P4510 | describes a project that uses | scikit-learn | Q1026367 |
P433 | issue | 1 | |
P6104 | maintained by WikiProject | WikiProject Mathematics | Q8487137 |
P304 | page(s) | 77-103 | |
P577 | publication date | 2015-06-04 | |
P1433 | published in | Journal of Computational Neuroscience | Q15753630 |
P1476 | title | Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity | |
P478 | volume | 39 |
Q93045886 | Computational Neuroscience: Mathematical and Statistical Perspectives |
Q39249429 | Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses? |
Q49806244 | Dynamics of Evolving Feed-Forward Neural Networks and Their Topological Invariants |
Q30000049 | Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data |
Q55348451 | Ensemble stacking mitigates biases in inference of synaptic connectivity. |
Q35959303 | Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome |
Q39197260 | From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining |
Q47222395 | Functional network stability and average minimal distance - A framework to rapidly assess dynamics of functional network representations. |
Q64110456 | High-resolution directed human connectomes and the Consensus Connectome Dynamics |
Q58700489 | Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings |
Q91011780 | Inferring and validating mechanistic models of neural microcircuits based on spike-train data |
Q49721923 | Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology |
Q36289471 | On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs. |
Q90440823 | Reconstructing neuronal circuitry from parallel spike trains |
Q33361580 | Seizure Prediction: Science Fiction or Soon to Become Reality? |
Q55365396 | Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation. |
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