Cascade models of synaptically stored memories.

By Stefano Fusi, P. Drew, L. F. Abbott. 2005

581 citations recorded by the source corpus.

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

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Citations in the corpus

Overcoming catastrophic forgetting in neural networks (2016, background): “Together, these experimental ﬁndings—together with neurobiological models [Fusi et al., 2005, Benna and Fusi, 2016]—suggest that continual learning in the mammalian neocortex relies on a process of task-speciﬁc synaptic consolidation, whereby knowledge about how to perform a previously acquired…”
Continual Lifelong Learning with Neural Networks: A Review (2018, background): “there are shared structural properties, different brain areas operate at multiple timescales and learning rates, thus differing signiﬁcantly from each other in a functional way (Benna and Fusi, 2016; Fusi et al., 2005). A prominent example are the complementary contributions of the neocortex and the”
Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, background): “While previous models based on delayed synaptic consolidation induced by molecular signaling cascades [52,141] may account for short-term spacing effects on the time-scale of minutes, structural plasticity can also explain long-term spacing effects on the time scale of months to years [110,111].”
Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgetting (2019, methodology): “Synaptic models with a cascade of internal states of progressively longer retention times were shown to prevent an exponentially fast forgetting while being continuously exposed to stimuli (Fusi et al., 2005; Benna and Fusi, 2016; Kaplanis et al., 2018).”
On Learning the Geodesic Path for Incremental Learning (2021, methodology): “Inspired by the synaptic consolidation from neurobiological models [8], Kirkpatrick et al.”
Orthogonal Gradient Descent for Continual Learning (2019, methodology): “These approaches are well motivated by neuro-biological models of memory Fusi et al. (2005); Kaplanis et al. (2018) and are computationally fast and do not require storing data.”
Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (2018, background): “A third strategy also motivated by neurobiological considerations is the use of latent synaptic dynamics (Fusi et al., 2005; Lahiri & Ganguli, 2013).”
Improved multitask learning through synaptic intelligence (2017, background): “While this complexity has been surmised to serve memory consolidation (Fusi et al., 2005; Lahiri & Ganguli, 2013; Benna & Fusi, 2016), few studies have illustrated how the added synaptic complexity benefits learning in neural network models in a supervised setting across multiple tasks.”
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression (2008, background): “Hence, classical [20; 21; 34] and recent [22] theoretical models which studied memory maintenance in the presence of ongoing neuronal activity on the level of single synapses need to be reconsidered, since the assumption of independent synapses does not hold (Fig.”
On simplicity and complexity in the brave new world of large-scale neuroscience. (2015, background): “In the realm of long-term memory, seminal work revealed that it is essential to treat synapses as entire dynamical systems in their own right, exhibiting a particular synaptic model [85], while further theoretical work [86] analyzed the space of all possible synaptic dynamical systems, revealing gen”
Copying and Evolution of Neuronal Topology (2008, background): “copying alternatively from L0 to L1 and from L1 to L0. Abbott’s group propose that synapses have distinct states of stability and plasticity [ 71 ].”
How can memories last for days, years, or a lifetime? Proposed mechanisms for maintaining synaptic potentiation and memory (2019, methodology): “This model enables quick learning in combination with slow forgetting (Fusi et al. 2005; Benna and Fusi 2016).”
Stable Learning in Stochastic Network States (2012, result): “Previous models also included an activity-dependent induction threshold (Bienenstock et al., 1982), or metaplasticity mechanisms (Lisman, 1989; Fusi et al., 2005; Graupner and Brunel, 2007), but these models were not consistent with STDP or with priming experiments.”
Neural Associative Memory with Optimal Bayesian Learning (2011, background): “Another future direction will be to investigate more closely the biological relevance of Bayesian learning by implementing more realistic network models that include spikes, forgetful synapses, and inhibitory circuits (Sandberg et al, 2000 ; Fusi, Drew, & Abbott, 2005 ; Markram et al, 2004 ).”
Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules (2010, unknown): “Such a “lock-in” (O’Connor et al., 2005a) concept has been modeled using deeper levels of plasticity (Stäubli and Chun, 1996; Fusi et al., 2005; O’Connor et al., 2005b).”