Neuromodulation and time-dependent plasticity in a model of foraging behavior

2008 ◽  
Author(s):  
Jeffrey L. Krichmar
Keyword(s):  
Foraging Behavior ◽  
Time Dependent ◽  
Dependent Plasticity

scholarly journals Is the vagus nerve our neural connectome?

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
V Reggie Edgerton ◽  
Parag Gad
Keyword(s):  
Vagus Nerve ◽  
Time Dependent ◽  
Dependent Plasticity

What are the implications of the vagus nerve being able to mediate the time-dependent plasticity of an array of sensorimotor networks?

Work Bounding Functions for Plastic Materials

1976 ◽  
Vol 43 (3) ◽  
pp. 434-438 ◽  
Author(s):  
P. Carter ◽  
J. B. Martin
Keyword(s):  
Internal Variable ◽  
Time Dependent ◽  
Dependent Plasticity ◽  
Plastic Materials ◽  
Minimum Work

The minimum work and maximum complementary work potentials for both time-independent and time-dependent plasticity are reconsidered from the viewpoint of internal variable theories. It is shown that the minimum work and maximum complementary work can be bounded in a simple and direct manner. The bounds provide the minimum work and maximum complementary work under certain limitations.

scholarly journals Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
M. Prezioso ◽  
F. Merrikh Bayat ◽  
B. Hoskins ◽  
K. Likharev ◽  
D. Strukov
Keyword(s):  
Metal Oxide ◽  
Time Dependent ◽  
Spike Time ◽  
Dependent Plasticity ◽  
Spike Time Dependent Plasticity ◽  
Self Adaptive

scholarly journals The effect of spike time dependent plasticity on activity patterns in the basal ganglia

2011 ◽  
Vol 12 (S1) ◽  
Author(s):  
Marcel AJ Lourens ◽  
Jasmine A Nirody ◽  
Hil GE Meijer ◽  
Tjitske Heida ◽  
Stephan A van Gils
Keyword(s):  
Basal Ganglia ◽  
Activity Patterns ◽  
Time Dependent ◽  
Spike Time ◽  
Dependent Plasticity ◽  
Spike Time Dependent Plasticity

Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions

2013 ◽  
Vol 25 (12) ◽  
pp. 3113-3130 ◽  
Author(s):  
Jan-Moritz P. Franosch ◽  
Sebastian Urban ◽  
J. Leo van Hemmen
Keyword(s):  
Function Approximation ◽  
Sensory Input ◽  
Learning Rule ◽  
Animal Learn ◽  
Spike Timing ◽  
Time Dependent ◽  
Stable Configuration ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Tactile System

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

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