Memory Nearly on a Spring: A Mean First Passage Time Approach to Memory Lifetimes

DOI10.1162/NECO_a_00622zbMath1417.91421OpenAlexW2143889306WikidataQ50659085 ScholiaQ50659085MaRDI QIDQ5383794

Terry Elliott

Publication date: 20 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00622

zbMATH Keywords

binary synapsesmemory lifetimesperceptron-based framework

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Memory and learning in psychology (91E40)

Related Items (9)

The impact of sparse coding on memory lifetimes in simple and complex models of synaptic plasticity ⋮ First Passage Time Memory Lifetimes for Multistate, Filter-Based Synapses ⋮ A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory ⋮ The Enhanced Rise and Delayed Fall of Memory in a Model of Synaptic Integration: Extension to Discrete State Synapses ⋮ Variations on the Theme of Synaptic Filtering: A Comparison of Integrate-and-Express Models of Synaptic Plasticity for Memory Lifetimes ⋮ Mean First Passage Memory Lifetimes by Reducing Complex Synapses to Simple Synapses ⋮ First Passage Time Memory Lifetimes for Simple, Multistate Synapses ⋮ Dynamic Integrative Synaptic Plasticity Explains the Spacing Effect in the Transition from Short- to Long-Term Memory ⋮ First Passage Time Memory Lifetimes for Simple, Multistate Synapses: Beyond the Eigenvector Requirement

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Rise and Fall of Memory in a Model of Synaptic Integration
• The Mean Time to Express Synaptic Plasticity in Integrate-and-Express, Stochastic Models of Synaptic Plasticity Induction
• Stability Against Fluctuations: Scaling, Bifurcations, and Spontaneous Symmetry Breaking in Stochastic Models of Synaptic Plasticity
• Precise Capacity Analysis in Binary Networks with Multiple Coding Level Inputs
• A Non-Markovian Random Walk Underlies a Stochastic Model of Spike-Timing-Dependent Plasticity
• Introduction to Theoretical Neurobiology
• Spiking Neuron Models
• Neural networks and physical systems with emergent collective computational abilities.
• Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics
• Memory Capacity for Sequences in a Recurrent Network with Biological Constraints
• The Final Problem : An Account of the Mock Theta Functions

This page was built for publication: Memory Nearly on a Spring: A Mean First Passage Time Approach to Memory Lifetimes