Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

By Robert Gütig, Ranit Aharonov, Stefan Rotter, Haim Sompolinsky. 2003

424 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “Such a stabilizing weight dependence is advantageous because it can lead to more plausible unimodal weight distributions as observed empirically [72, 74].”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, background): “On the other hand, in “multiplicative STDP” or mSTDP (van Rossum et al., 2000; Rubin et al., 2001; Gütig et al., 2003) the learning function is also a function of the actual weight value ξ m ( w , ∆ T ).”
Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell. (2004, background): “Rossum et al., 2000; Song et al., 2000; Rubin et al., 2001; Cateau and Fukai, 2003; Gütig et al., 2003).”
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity (2016, background): “One solution is interpolating between hard and soft bounds to obtain a middle ground that can harbor both synaptic competition and stability, which is obtained over a limited parameter range [12].”
Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity (2010, methodology): “In our simulations, we used f (w) (1 w) and f (w) w (Gütig et al., 2003), and considered to equal either 0 or 1.”
Stable Learning in Stochastic Network States (2012, methodology): “A weight-dependent version of mSTDP could be obtained by adding this dependency in Equation 7, as described by van Rossum et al. (2000), Gu¨tig et al. (2003), and Morrison et al. (2007), so that each LTP and LTD synaptic weight update is modulated by the current weight value.”
Synaptic consolidation: an approach to long-term learning (2011, neutral): “Numerous models of synaptic plasticity have been developed , first rate-based (Hebb 1949; Oja 1982; Bienenstock et al. 1982) then spike-based (Gerstner and Abbott 1997; Song et al. 2000; Gütig et al. 2003; Pfister and Gerstner 2006; Clopath and Gerstner 2010).”
Pyramidal Neuron Conductance State Gates Spike-Timing-Dependent Plasticity (2010, background): “Other types of stable STDP implementations have been proposed (van Rossum et al., 2000; Gütig et al., 2003; Morrison et al., 2007).”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, methodology): “Multiplicative/interpolating doublet STDP An alternative non-linear STDP model proposed by Gütig et al. (2003) interpolates between additive and multiplicative rules”
Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding (2010, background): “Moreover, firing rate and correlation (or synchrony) are attractive candidate neural codes because both can affect network function by influencing synaptic plasticit”
Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity (2012, methodology): “Theoretical studies have also explored the development of neuronal response properties [28,29,36,39,42].”
Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity (2007, result): “In the following simulations we used networks composed of integrate-and-fire neurons with resting potential ur =−70 mV, firing threshold θ =−54 mV, reset potential equal to the resting potential, and decay time constant τ= 20 ms (parameters from (Gütig et al., 2003) and similar to those in (Song et ”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways (2009, methodology): “The positive within-pool correlation is generated so that, for any input, a given portion of its spikes occur at the same time as some other spikes within its pool, while the remainder occur at independent times (Gütig et al. 2003; Meffin et al. 2006).”