The Rubik Cube and GP Temporal Sequence Learning: An Initial Study

2010 ◽  
pp. 35-54 ◽  
Author(s):  
Peter Lichodzijewski ◽  
Malcolm Heywood
Keyword(s):  
Sequence Learning ◽  
Initial Study ◽  
Temporal Sequence

scholarly journals Theta oscillations promote temporal sequence learning

2018 ◽  
Vol 153 ◽  
pp. 92-103 ◽  
Author(s):  
Jordan Crivelli-Decker ◽  
Liang-Tien Hsieh ◽  
Alex Clarke ◽  
Charan Ranganath
Keyword(s):  
Sequence Learning ◽  
Temporal Sequence ◽  
Theta Oscillations

Spatial and Temporal Sequence Learning in Patients with Parkinson's Disease or Cerebellar Lesions

2003 ◽  
Vol 15 (8) ◽  
pp. 1232-1243 ◽  
Author(s):  
Jacqueline C. Shin ◽  
Richard B. Ivry
Keyword(s):  
Sequence Learning ◽  
Functional Role ◽  
Phase Relationship ◽  
Reaction Time Task ◽  
Training Sequence ◽  
Temporal Sequence ◽  
General Role ◽  
Cerebellar Damage ◽  
Patient Groups

The functional role of different subcortical areas in sequence learning is not clear. In the current study, Parkinson's patients, patients with cerebellar damage, and age-matched control participants performed a serial reaction time task in which a spatial sequence and a temporal sequence were presented simultaneously. The responses were based on the spatial sequence, and the temporal sequence was incidental to the task. The two sequences were of the same length, and the phase relationship between them was held constant throughout training. Sequence learning was assessed comparing performance when both sequences were present versus when the dimension of interest was randomized. In addition, sequence integration was assessed by introducing phase-shift blocks. A functional dissociation was found between the two patient groups. Whereas the Parkinson's patients learned the spatial and temporal sequences individually, they did not learn the relationship between the two sequences, suggesting the basal ganglia play a functional role in sequence integration. In contrast, the cerebellar patients did not show any evidence of sequence learning at all, suggesting the cerebellum might play a general role in forming sequential associations.

Hearing discrete elements of a temporal sequence: Learning and generalization

2000 ◽  
Vol 107 (5) ◽  
pp. 2882-2882
Author(s):  
Robert S. Schlauch ◽  
Jeffrey J. DiGiovanni ◽  
Dennis T. Ries
Keyword(s):  
Sequence Learning ◽  
Temporal Sequence ◽  
Discrete Elements

scholarly journals Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms

2005 ◽  
Vol 17 (2) ◽  
pp. 245-319 ◽  
Author(s):  
Florentin Wörgötter ◽  
Bernd Porr
Keyword(s):  
Sequence Learning ◽  
Closed Loop ◽  
Calcium Release ◽  
Hebbian Learning ◽  
Spike Timing ◽  
Temporal Sequence ◽  
Learning Approaches ◽  
Biological Mechanisms ◽  
Machine Control ◽  
Loop Condition

In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlation-based (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calcium-release mechanisms known to be involved in STDP.

scholarly journals A mechanism for temporal sequence learning and recognition in neural systems

2008 ◽  
Vol 9 (S1) ◽  
Author(s):  
Sean Byrnes ◽  
Anthony N Burkitt ◽  
Hamish Meffin ◽  
Chris Trengove ◽  
David B Grayden
Keyword(s):  
Sequence Learning ◽  
Temporal Sequence ◽  
Neural Systems

The formation of a temporal-sequence learning set by monkeys.

1959 ◽  
Vol 52 (2) ◽  
pp. 225-227 ◽  
Author(s):  
Richard S. Massar ◽  
Roger T. Davis
Keyword(s):  
Sequence Learning ◽  
Temporal Sequence ◽  
Learning Set

scholarly journals A COMBINATION OF TEMPORAL SEQUENCE LEARNING AND DATA DESCRIPTION FOR ANOMALYBASED NIDS

2019 ◽  
Vol 11 (03) ◽  
pp. 89-100
Author(s):  
Nguyen Thanh Van ◽  
Tran Ngoc Thinh ◽  
Le Thanh Sach
Keyword(s):  
Sequence Learning ◽  
Temporal Sequence ◽  
Data Description
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