Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition

By Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski. 2014

1,694 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Plant Science

View Paper: https://www.semanticscholar.org/paper/ae597169ddc0100da30f7763fd79130a6ab2327f

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In Cellular and Molecular Neuroscience

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Citations in the corpus

Physics for neuromorphic computing (2020, background): “The brain as physical substrate of computing is also fundamentally different from our general purpose computers based on digital circuits 5,14 .”
Network Reconstruction From High-Dimensional Ordinary Differential Equations (2016, background): “As an alternative, neuronal populations can be studied (see e.g., Part Three of Gerstner et al., 2014).”
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks (2019, methodology): “We first introduce an LIF neuron model with current-based synapses, which has wide use in computational neuroscience [12].”
Training Spiking Neural Networks Using Lessons From Deep Learning (2021, background): “The input spike train undergoes two rounds of “smoothing,” which means that the membrane potential has a finite rise time rather than experiencing discontinuous jumps in response to incoming spikes [78], [79], [80].”
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks (2020, methodology): “Neuron model We used leaky integrate-and-fire neurons with current-based exponential synapses [52, 53].”
Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics (2019, background): “In addition, biological signals are processed in an event-driven manner, i.e., the synapses fire only when a membrane potential reaches a specific value, rather than at each propagation cycle, [189–191] which shows high energy efficiency.”
Asymptotic behavior of random fitzhugh-nagumo systems driven by colored noise (2017, background): “The O-U process Oδ is also called a colored noise because its power spectrum is not flat compared to the white noise, see for instance [4, 25, 42, 43, 55].”
Deep Learning in Spiking Neural Networks (2018, methodology): “connecting synapses that are modeled by adjustable scalar weights. The ﬁrst step in implementing an SNN is to encode the analog input data into the spike trains using either a rate based method [64], [15], some form of temporal coding [66], [67], or population coding [68]. As stated earlier, a biolo”
Flexible statistical inference for mechanistic models of neural dynamics (2017, background): “However, identifying which models of neural dynamics can (or cannot) reproduce electrophysiological or imaging measurements of neural activity has been a major challenge [1].”
Long short-term memory and Learning-to-learn in networks of spiking neurons (2018, methodology): “Common ways for fitting models for adapting neurons to data are described in [18, 19, 20, 21].”
Neuromorphic computing using non-volatile memory (2017, background): “Emulation of these neuronal dynamics, including maintenance of the equilibrium potential, the transient dynamics and the process of neuro-transmission, is thought to be the key to the realization of biologically plausible neuromorphic computing systems [196].”
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks (2020, methodology): “The Leaky Integrate-and-Fire (LIF) model [16] is one of the simplest spiking neuron models used in SNNs.”
Unsupervised learning by competing hidden units (2018, background): “We now know that neurons make either excitatory or inhibitory outgoing synapses (but not both) [12], that for excitatory synapses the change in efficacy can either increase or decrease depending on whether the coordinated activity of the post-synaptic cell is strong or weak (LTP=long term potentiati”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, methodology): “For the neuron model (Gerstner et al., 2014), a plausible set of standard parameters with “round” values were chosen before starting any plasticitymodelingandkeptfixedthroughoutthemodelingprocess.”