[go: up one dir, main page]

Hadjiivanov, 2021 - Google Patents

Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis

Hadjiivanov, 2021

View PDF
Document ID
15576647208176603146
Author
Hadjiivanov A
Publication year
Publication venue
arXiv preprint arXiv:2104.10851

External Links

Snippet

Most classical (non-spiking) neural network models disregard internal neuron dynamics and treat neurons as simple input integrators. However, biological neurons have an internal state governed by complex dynamics that plays a crucial role in learning, adaptation and the …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/049Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0472Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0454Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/067Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/10Simulation on general purpose computers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/004Artificial life, i.e. computers simulating life
    • G06N3/008Artificial life, i.e. computers simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior

Similar Documents

Publication Publication Date Title
US8504502B2 (en) Prediction by single neurons
Suri TD models of reward predictive responses in dopamine neurons
Del Giudice et al. Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses
Rahman et al. Modulated spike-time dependent plasticity (STDP)-based learning for spiking neural network (SNN): A review
Zhou et al. Surrogate-assisted evolutionary search of spiking neural architectures in liquid state machines
Chrol-Cannon et al. Computational modeling of neural plasticity for self-organization of neural networks
Isomura Active inference leads to Bayesian neurophysiology
Weidel et al. Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks
Florian A reinforcement learning algorithm for spiking neural networks
McClelland et al. Consciousness and connectionist models
Gerstner Hebbian learning and plasticity
Christophe et al. Pattern recognition with spiking neural networks: a simple training method.
Hadjiivanov Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis
Wu et al. Enhancing orderly signal propagation between layers of neuronal networks through spike timing-dependent plasticity
KR101122158B1 (en) Prediction by single neurons and networks
Verschure et al. A real-time model of the cerebellar circuitry underlying classical conditioning: a combined simulation and robotics study
Farajidavar et al. Incorporating synaptic time-dependent plasticity and dynamic synapse into a computational model of wind-up
Recio et al. Emergence of low noise frustrated states in E/I balanced neural networks
Senn et al. Spike-Timing-Dependent Plasticity, Learning Rules.
Kannan et al. Neural Models of Task Adaptation: A Tutorial on Spiking Networks for Executive Control
Qian Condition Integration Memory Network: An Interpretation of the Meaning of the Neuronal Design
Vico et al. Stable neural attractors formation: Learning rules and network dynamics
Chandhok et al. Adaptation of spiking neural networks for image clustering
Belavkin et al. Conflict resolution and learning probability matching in a neural cell-assembly architecture
Meunier et al. Neural networks for computational neuroscience.