Control of synaptic plasticity in deep cortical networks

By Pieter R. Roelfsema, Anthony Holtmaat. 2018

233 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

Theories of Error Back-Propagation in the Brain (2019, background): “Such bursts of spikes may subsequently trigger synaptic plasticity [48,49].”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, methodology): “…in layered networks, M3rdi is calculated by a weighted sum over the errors in the next layer closer to the output; a calculation which needs a well-tuned feedback circuit from the output back to previous layers (Roelfsema and van Ooyen, 2005; Lillicrap et al., 2016; Roelfsema and Holtmaat, 2018).”
Dendritic cortical microcircuits approximate the backpropagation algorithm (2018, background): “This is often referred to as the synaptic credit assignment problem (Rumelhart et al., 1986; Sutton and Barto, 1998; Roelfsema and van Ooyen, 2005; Friedrich et al., 2011; Bengio, 2014; Lee et al., 2015; Roelfsema and Holtmaat, 2018), for which the backprop algorithm provides an elegant solution.”
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020, methodology): “synapses lower — whether to engage in LTP or LTD in order to improve behavior [2]? This question is 42 sometimes referred to as the “credit assignment problem”: essentially, how can we assign credit for any 43 errors or successes to neurons that are multiple synapses away from the output [26]? 44 In”
Synaptic Plasticity Forms and Functions. (2020, background): “In addition, regulation of dendritic voltage signals by inhibitory and disinhibitory microcircuit elements as well as neuromodulatory input could provide these components a permissive control over learning,or even a direct role in credit assignment (Roelfsema & Holtmaat 2018,Sacramento et al.2018).”
Predictive Processing: A Canonical Cortical Computation (2018, unknown): “It is therefore plausible that long-range cortical communication conveys specific predictions of input to the target areas that are associated by experience with signals in the source area (Larkum, 2013; Roelfsema and Holtmaat, 2018).”
Artificial Neural Networks for Neuroscientists: A Primer (2020, methodology): “Efforts have been devoted to approximating gradient descent with algorithms more compatible with the brain’s hardware [Lillicrap et al., 2016, Guerguiev et al., 2017, Roelfsema and Holtmaat, 2018, Lillicrap et al., 2020].”