scholarly journals Ventral striatum lesions do not affect reinforcement learning with deterministic outcomes on slow time scales.

2017 ◽  
Vol 131 (5) ◽  
pp. 385-391 ◽  
Author(s):  
Raquel Vicario-Feliciano ◽  
Elisabeth A. Murray ◽  
Bruno B. Averbeck
Keyword(s):  
Reinforcement Learning ◽  
Time Scales ◽  
Ventral Striatum ◽  
Slow Time

Nonlinear acoustic forces acting on inhomogeneous fluids at slow time-scales

2016 ◽  
Vol 139 (4) ◽  
pp. 2152-2152 ◽  
Author(s):  
Jonas T. Karlsen ◽  
Per Augustsson ◽  
Henrik Bruus
Keyword(s):  
Time Scales ◽  
Slow Time ◽  
Inhomogeneous Fluids ◽  
Nonlinear Acoustic

scholarly journals The involvement of model-based but not model-free learning signals during observational reward learning in the absence of choice

2016 ◽  
Vol 115 (6) ◽  
pp. 3195-3203 ◽  
Author(s):  
Simon Dunne ◽  
Arun D'Souza ◽  
John P. O'Doherty
Keyword(s):  
Reinforcement Learning ◽  
Experiential Learning ◽  
Observational Learning ◽  
Ventral Striatum ◽  
Learning System ◽  
Model Based ◽  
Model Free ◽  
Human Participants ◽  
Open Question ◽  
Insight Into

A major open question is whether computational strategies thought to be used during experiential learning, specifically model-based and model-free reinforcement learning, also support observational learning. Furthermore, the question of how observational learning occurs when observers must learn about the value of options from observing outcomes in the absence of choice has not been addressed. In the present study we used a multi-armed bandit task that encouraged human participants to employ both experiential and observational learning while they underwent functional magnetic resonance imaging (fMRI). We found evidence for the presence of model-based learning signals during both observational and experiential learning in the intraparietal sulcus. However, unlike during experiential learning, model-free learning signals in the ventral striatum were not detectable during this form of observational learning. These results provide insight into the flexibility of the model-based learning system, implicating this system in learning during observation as well as from direct experience, and further suggest that the model-free reinforcement learning system may be less flexible with regard to its involvement in observational learning.

scholarly journals Local postsynaptic signalling on slow time scales in reciprocal olfactory bulb granule cell spines matches asynchronous release

2020 ◽  
Author(s):  
Tiffany Ona Jodar ◽  
Vanessa Lage-Rupprecht ◽  
Nixon M. Abraham ◽  
Christine R. Rose ◽  
Veronica Egger
Keyword(s):  
Olfactory Bulb ◽  
Time Scales ◽  
Time Course ◽  
Granule Cells ◽  
Gaba Release ◽  
Fluorescence Signal ◽  
Slow Time ◽  
Triggered Release ◽  
Time To Peak ◽  
Adult Mice

AbstractIn the vertebrate olfactory bulb (OB), axonless granule cells (GC) mediate self- and lateral inhibitory interactions between mitral/tufted cells via reciprocal dendrodendritic synapses. Locally triggered release of GABA from the large reciprocal GC spines occurs on both fast and slow time scales, possibly enabling parallel processing during olfactory perception. Here we investigate local mechanisms for asynchronous spine output.To reveal the temporal and spatial characteristics of postsynaptic ion transients, we imaged spine and adjacent dendrite Ca2+- and Na+-signals with minimal exogenous buffering by the respective fluorescent indicator dyes upon two-photon uncaging of DNI-glutamate in OB slices from juvenile rats. Both postsynaptic fluorescence signals decayed slowly, with average half durations in the spine head of t1/2_Δ[Ca2+]i ~500 ms and t1/2_Δ[Na+]i ~1000 ms. We also analysed the kinetics of already existing data of postsynaptic spine Ca2+-signals in response to glomerular stimulation in OB slices from adult mice, either WT or animals with partial GC glutamate receptor deletions (NMDAR: GluN1 subunit; AMPAR: GluA2 subunit). In a large subset of spines the fluorescence signal had a protracted rise time (average time to peak ~400 ms, range 20 ms - >1000 ms). This slow rise was independent of Ca2+ entry via NMDARs, since similarly slow signals occurred in ΔGluN1 GCs. Additional Ca2+ entry in ΔGluA2 GCs (with AMPARs rendered Ca2+-permeable), however, resulted in larger ΔF/Fs that rose yet more slowly.Thus GC spines appear to dispose of several local mechanisms to promote asynchronous GABA release, which are reflected in the time course of mitral/tufted cell recurrent inhibition.

scholarly journals Local Postsynaptic Signaling on Slow Time Scales in Reciprocal Olfactory Bulb Granule Cell Spines Matches Asynchronous Release

2020 ◽  
Vol 12 ◽  
Author(s):  
Tiffany Ona Jodar ◽  
Vanessa Lage-Rupprecht ◽  
Nixon M. Abraham ◽  
Christine R. Rose ◽  
Veronica Egger
Keyword(s):  
Olfactory Bulb ◽  
Time Scales ◽  
Granule Cell ◽  
Slow Time ◽  
Asynchronous Release

A Review of Neurocognitive Mechanisms of Social Conformity

2014 ◽  
Vol 45 (6) ◽  
pp. 466-478 ◽  
Author(s):  
Robert Schnuerch ◽  
Henning Gibbons
Keyword(s):  
Reinforcement Learning ◽  
Frontal Cortex ◽  
Ventral Striatum ◽  
Behavioral Adjustment ◽  
Medial Frontal Cortex ◽  
Valuable Insight ◽  
Cognitive Mechanisms ◽  
Learning Mechanisms ◽  
Social Conformity ◽  
Insight Into

In groups, individuals often adjust their behavior to the majority’s. Here, we provide a brief introduction into the research on social conformity and review the first, very recent investigations elucidating the underlying neurocognitive mechanisms. Multiple studies suggest that conformity is a behavioral adjustment based on reinforcement-learning mechanisms in posterior medial frontal cortex and ventral striatum. It has also been suggested that the detection of cognitive inconsistency and the modulation of basic encoding processes are involved. Together, recent findings provide valuable insight into the neural and cognitive mechanisms underlying social conformity and clearly point up the need for further studies in this field.

scholarly journals Deep Reinforcement Learning with Hierarchical Structures

2021 ◽  
Author(s):  
Siyuan Li
Keyword(s):  
Reinforcement Learning ◽  
Time Scales ◽  
Hierarchical Structures ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Top Down ◽  
Bottom Up ◽  
Hierarchical Reinforcement Learning ◽  
Future Work

Hierarchical reinforcement learning (HRL), which enables control at multiple time scales, is a promising paradigm to solve challenging and long-horizon tasks. In this paper, we briefly introduce our work in bottom-up and top-down HRL and outline the directions for future work.

scholarly journals Slow time scales in a dense vibrofluidized granular material

2020 ◽  
Vol 102 (1) ◽  
Author(s):  
Andrea Plati ◽  
Andrea Puglisi
Keyword(s):  
Granular Material ◽  
Time Scales ◽  
Slow Time

scholarly journals Effects of Ventral Striatum Lesions on Stimulus-Based versus Action-Based Reinforcement Learning

2017 ◽  
Vol 37 (29) ◽  
pp. 6902-6914 ◽  
Author(s):  
Kathryn M. Rothenhoefer ◽  
Vincent D. Costa ◽  
Ramón Bartolo ◽  
Raquel Vicario-Feliciano ◽  
Elisabeth A. Murray ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Ventral Striatum
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