| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2013PNAS..110E3468N |
| P356 | DOI | 10.1073/PNAS.1212083110 |
| P8608 | Fatcat ID | release_rup6b4bqjnh3lkccfyg5etwrnu |
| P932 | PMC publication ID | 3773754 |
| P698 | PubMed publication ID | 23878215 |
| P5875 | ResearchGate publication ID | 251235277 |
| P50 | author | Ueli Rutishauser | Q41047593 |
| Jonathan Binas | Q59555549 | ||
| P2093 | author name string | Rodney J Douglas | |
| Giacomo Indiveri | |||
| Emre Neftci | |||
| Elisabetta Chicca | |||
| P2860 | cites work | Frontiers in neuromorphic engineering | Q21129367 |
| NEURONAL CIRCUITS OF THE NEOCORTEX | Q22337027 | ||
| Grid cells generate an analog error-correcting code for singularly precise neural computation | Q28247736 | ||
| Neurotech for neuroscience: unifying concepts, organizing principles, and emerging tools | Q28255758 | ||
| Neural mechanisms of selective visual attention | Q28292891 | ||
| Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit | Q28487312 | ||
| Bilinearity, rules, and prefrontal cortex | Q28757307 | ||
| Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles | Q30480856 | ||
| A fluctuation-driven mechanism for slow decision processes in reverberant networks | Q33348619 | ||
| Vision egg: an open-source library for realtime visual stimulus generation | Q33388943 | ||
| Bistable perception modeled as competing stochastic integrations at two levels | Q33480951 | ||
| Collective behavior of networks with linear (VLSI) integrate-and-fire neurons | Q33543274 | ||
| A large-scale model of the functioning brain | Q34314574 | ||
| Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. | Q34479655 | ||
| Neuromorphic silicon neuron circuits. | Q35088642 | ||
| Simple substrates for complex cognition | Q37395808 | ||
| A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. | Q39432970 | ||
| Analog versus digital: extrapolating from electronics to neurobiology | Q39475843 | ||
| Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses | Q39785061 | ||
| Silicon auditory processors as computer peripherals | Q39834056 | ||
| A systematic method for configuring VLSI networks of spiking neurons | Q39990085 | ||
| Selective attention in multi-chip address-event systems | Q40908605 | ||
| Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses | Q42161080 | ||
| A quantitative map of the circuit of cat primary visual cortex. | Q45084921 | ||
| State-dependent computation using coupled recurrent networks | Q46051154 | ||
| Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses | Q47813588 | ||
| A microcircuit model of the frontal eye fields. | Q47837763 | ||
| Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons | Q47899491 | ||
| The role of the anterior prefrontal cortex in human cognition | Q48204765 | ||
| Neural activity in the primate prefrontal cortex during associative learning | Q48308891 | ||
| Interactions between frontal cortex and basal ganglia in working memory: a computational model | Q48432343 | ||
| Prefrontal cell activities related to monkeys' success and failure in adapting to rule changes in a Wisconsin Card Sorting Test analog. | Q48624290 | ||
| Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control | Q48696267 | ||
| Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI. | Q48777095 | ||
| Collective stability of networks of winner-take-all circuits. | Q51624406 | ||
| Synaptic dynamics in analog VLSI. | Q51625662 | ||
| Task-specific neural activity in the primate prefrontal cortex. | Q52166788 | ||
| Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. | Q52173663 | ||
| Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. | Q52175540 | ||
| An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors | Q59309440 | ||
| P433 | issue | 37 | |
| P407 | language of work or name | English | Q1860 |
| P304 | page(s) | E3468-76 | |
| P577 | publication date | 2013-07-22 | |
| P1433 | published in | Proceedings of the National Academy of Sciences of the United States of America | Q1146531 |
| P1476 | title | Synthesizing cognition in neuromorphic electronic systems | |
| P478 | volume | 110 |
| Q64059327 | A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision |
| Q30412113 | A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses |
| Q30443858 | A robust sound perception model suitable for neuromorphic implementation. |
| Q30834703 | A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems |
| Q30570736 | An efficient automated parameter tuning framework for spiking neural networks |
| Q47123085 | Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population |
| Q59808732 | CABots and Other Neural Agents |
| Q35548494 | Computation in dynamically bounded asymmetric systems |
| Q47836278 | Computational Foundations of Natural Intelligence |
| Q40995046 | Connecting the Brain to Itself through an Emulation |
| Q91626091 | Data and Power Efficient Intelligence with Neuromorphic Learning Machines |
| Q41810787 | Dynamic neural fields as a step toward cognitive neuromorphic architectures. |
| Q36099719 | Electrochemical Bioelectronic Device Consisting of Metalloprotein for Analog Decision Making. |
| Q96303115 | Event-Based Computation for Touch Localization Based on Precise Spike Timing |
| Q33819262 | Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines |
| Q37579640 | Event-driven contrastive divergence for spiking neuromorphic systems. |
| Q45943707 | Feedforward Approximations to Dynamic Recurrent Network Architectures. |
| Q91011780 | Inferring and validating mechanistic models of neural microcircuits based on spike-train data |
| Q60047587 | Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain |
| Q33861505 | Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity |
| Q45963377 | Local structure supports learning of deterministic behavior in recurrent neural networks. |
| Q90226746 | Nanosystems, Edge Computing, and the Next Generation Computing Systems |
| Q58694129 | Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning |
| Q37627105 | Neuromorphic Implementation of Attractor Dynamics in a Two-Variable Winner-Take-All Circuit with NMDARs: A Simulation Study |
| Q59808624 | Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields |
| Q28656038 | PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems |
| Q36158706 | Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems |
| Q37203873 | Reverse engineering the cognitive brain |
| Q51556530 | Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks. |
| Q47916111 | Specific excitatory connectivity for feature integration in mouse primary visual cortex. |
| Q39763572 | Stochastic phase-change neurons |
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