A Model of Temporal Scaling Correctly Predicts that Weber′s Law is Speed-dependent

2017 ◽  
Author(s):  
Nicholas F Hardy ◽  
Vishwa Goudar ◽  
Juan L Romero-Sosa ◽  
Dean Buonomano
Keyword(s):  
Neural Network ◽  
General Feature ◽  
Motor Timing ◽  
Motor Patterns ◽  
Temporal Scaling ◽  
Temporal Precision ◽  
Motor Behaviors ◽  
Morse Code ◽  
Complex Motor ◽  
Speed Effect

Timing is fundamental to complex motor behaviors: from tying a knot to playing the piano. A general feature of motor timing is temporal scaling: the ability to produce motor patterns at different speeds. Here we report that temporal scaling is not automatic. After learning to produce a Morse code pattern at one speed, subjects did not accurately generalize to novel speeds. Temporal scaling was also not a general property of a recurrent neural network (RNN) model, however after training across different speeds the model produced robust temporal scaling. The model captured a signature of motor timing-Weber′s law-but predicted that temporal precision increases at faster speeds. A human psychophysics study confirmed this prediction: the standard deviation of responses in absolute time were lower at faster speeds. These results establish that RNNs can account for temporal scaling, and suggest a novel psychophysical principle: the Weber-speed effect.

scholarly journals Musical expertise generalizes to superior temporal scaling in a Morse code tapping task

2019 ◽  
Author(s):  
Matthew A. Slayton ◽  
Juan L. Romero-Sosa ◽  
Katrina Shore ◽  
Dean V. Buonomano ◽  
Indre V. Viskontas
Keyword(s):  
Temporal Patterns ◽  
Motor Patterns ◽  
Temporal Scaling ◽  
Temporal Precision ◽  
Morse Code ◽  
Extensive Training ◽  
Timing Precision ◽  
Significant Difference ◽  
Speed Effect ◽  
High Level

ABSTRACTA key feature of the brain’s ability to tell time and generate complex temporal patterns is its capacity to produce similar temporal patterns at different speeds. For example, humans can tie a shoe, type, or play an instrument at different speeds or tempi—a phenomenon referred to as temporal scaling. While it is well established that training improves timing precision and accuracy, it is not known whether expertise improves temporal scaling. We quantified temporal scaling and timing precision in musicians and non-musicians as they learned to tap a Morse code sequence. We found that controls improved significantly over the course of days of training at the standard speed. In contrast, musicians exhibited a high level of temporal precision on the first day, which did not improve significantly with training. Although there was no significant difference in performance at the end of training at the standard speed, musicians were significantly better at temporal scaling—i.e., at reproducing the learned Morse code pattern at faster and slower speeds. Interestingly, both musicians and non-musicians exhibited a Weber-speed effect, where absolute temporal precision sharpened when producing patterns at the faster speed. These results are the first to establish that the ability to generate the same motor patterns at different speeds improves with extensive training and generalizes to non-musical domains.

Temporal Sparseness of the Premotor Drive Is Important for Rapid Learning in a Neural Network Model of Birdsong

2004 ◽  
Vol 92 (4) ◽  
pp. 2274-2282 ◽  
Author(s):  
Ila R. Fiete ◽  
Richard H.R. Hahnloser ◽  
Michale S. Fee ◽  
H. Sebastian Seung
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Motor Neurons ◽  
Neural Network Model ◽  
Motor Behavior ◽  
Analytical Techniques ◽  
Temporal Coding ◽  
Neural Codes ◽  
Learning Time ◽  
Complex Motor

Sparse neural codes have been widely observed in cortical sensory and motor areas. A striking example of sparse temporal coding is in the song-related premotor area high vocal center (HVC) of songbirds: The motor neurons innervating avian vocal muscles are driven by premotor nucleus robustus archistriatalis (RA), which is in turn driven by nucleus HVC. Recent experiments reveal that RA-projecting HVC neurons fire just one burst per song motif. However, the function of this remarkable temporal sparseness has remained unclear. Because birdsong is a clear example of a learned complex motor behavior, we explore in a neural network model with the help of numerical and analytical techniques the possible role of sparse premotor neural codes in song-related motor learning. In numerical simulations with nonlinear neurons, as HVC activity is made progressively less sparse, the minimum learning time increases significantly. Heuristically, this slowdown arises from increasing interference in the weight updates for different synapses. If activity in HVC is sparse, synaptic interference is reduced, and is minimized if each synapse from HVC to RA is used only once in the motif, which is the situation observed experimentally. Our numerical results are corroborated by a theoretical analysis of learning in linear networks, for which we derive a relationship between sparse activity, synaptic interference, and learning time. If songbirds acquire their songs under significant pressure to learn quickly, this study predicts that HVC activity, currently measured only in adults, should also be sparse during the sensorimotor phase in the juvenile bird. We discuss the relevance of these results, linking sparse codes and learning speed, to other multilayered sensory and motor systems.

scholarly journals Musical expertise generalizes to superior temporal scaling in a Morse code tapping task

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0221000 ◽  
Author(s):  
Matthew A. Slayton ◽  
Juan L. Romero-Sosa ◽  
Katrina Shore ◽  
Dean V. Buonomano ◽  
Indre V. Viskontas
Keyword(s):  
Temporal Scaling ◽  
Morse Code ◽  
Musical Expertise

scholarly journals Central pattern generators in the turtle spinal cord: selection among the forms of motor behaviors

2018 ◽  
Vol 119 (2) ◽  
pp. 422-440 ◽  
Author(s):  
Paul S. G. Stein
Keyword(s):  
Spinal Cord ◽  
Motor Pattern ◽  
Central Pattern Generators ◽  
Knee Extensor ◽  
Motor Patterns ◽  
Motor Behaviors ◽  
Multiple Behaviors ◽  
Pattern Generators ◽  
Electrical Stimuli

Neuronal networks in the turtle spinal cord have considerable computational complexity even in the absence of connections with supraspinal structures. These networks contain central pattern generators (CPGs) for each of several behaviors, including three forms of scratch, two forms of swim, and one form of flexion reflex. Each behavior is activated by a specific set of cutaneous or electrical stimuli. The process of selection among behaviors within the spinal cord has multisecond memories of specific motor patterns. Some spinal cord interneurons are partially shared among several CPGs, whereas other interneurons are active during only one type of behavior. Partial sharing is a proposed mechanism that contributes to the ability of the spinal cord to generate motor pattern blends with characteristics of multiple behaviors. Variations of motor patterns, termed deletions, assist in characterization of the organization of the pattern-generating components of CPGs. Single-neuron recordings during both normal and deletion motor patterns provide support for a CPG organizational structure with unit burst generators (UBGs) whose members serve a direction of a specific degree of freedom of the hindlimb, e.g., the hip-flexor UBG, the hip-extensor UBG, the knee-flexor UBG, the knee-extensor UBG, etc. The classic half-center hypothesis that includes all the hindlimb flexors in a single flexor half-center and all the hindlimb extensors in a single extensor half-center lacks the organizational complexity to account for the motor patterns produced by turtle spinal CPGs. Thus the turtle spinal cord is a valuable model system for studies of mechanisms responsible for selection and generation of motor behaviors. NEW & NOTEWORTHY The concept of the central pattern generator (CPG) is a major tenet in motor neuroethology that has influenced the design and interpretations of experiments for over a half century. This review concentrates on the turtle spinal cord and describes studies from the 1970s to the present responsible for key developments in understanding the CPG mechanisms responsible for the selection and production of coordinated motor patterns during turtle hindlimb motor behaviors.

scholarly journals Precision Timing with α–β Oscillatory Coupling: Stopwatch or Motor Control?

2020 ◽  
Vol 32 (9) ◽  
pp. 1624-1636
Author(s):  
Tadeusz W. Kononowicz ◽  
Tilmann Sander ◽  
Hedderik Van Rijn ◽  
Virginie van Wassenhove
Keyword(s):  
Motor Control ◽  
Relevant Information ◽  
Time Interval ◽  
Beta Activity ◽  
Motor Timing ◽  
Reproduction Task ◽  
Temporal Precision ◽  
Time Reproduction ◽  
Timing Precision ◽  
Oscillatory Coupling

Precise timing is crucial for many behaviors ranging from conversational speech to athletic performance. The precision of motor timing has been suggested to result from the strength of phase–amplitude coupling (PAC) between the phase of alpha oscillations (α, 8–12 Hz) and the power of beta activity (β, 14–30 Hz), herein referred to as α–β PAC. The amplitude of β oscillations has been proposed to code for temporally relevant information and the locking of β power to the phase of α oscillations to maintain timing precision. Motor timing precision has at least two sources of variability: variability of timekeeping mechanism and variability of motor control. It is ambiguous to which of these two factors α–β PAC should be ascribed: α–β PAC could index precision of stopwatch-like internal timekeeping mechanisms, or α–β PAC could index motor control precision. To disentangle these two hypotheses, we tested how oscillatory coupling at different stages of a time reproduction task related to temporal precision. Human participants encoded and subsequently reproduced a time interval while magnetoencephalography was recorded. The data show a robust α–β PAC during both the encoding and reproduction of a temporal interval, a pattern that cannot be predicted by motor control accounts. Specifically, we found that timing precision resulted from the trade-off between the strength of α–β PAC during the encoding and during the reproduction of intervals. These results support the hypothesis that α–β PAC codes for the precision of temporal representations in the human brain.

scholarly journals Neural Network of Predictive Motor Timing in the Context of Gender Differences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Pavel Filip ◽  
Jan Lošák ◽  
Tomáš Kašpárek ◽  
Jiří Vaníček ◽  
Martin Bareš
Keyword(s):  
Neural Network ◽  
Temporal Processing ◽  
Temporal Analysis ◽  
Behavioral Performance ◽  
Motor Timing ◽  
Short Term ◽  
Fmri Study ◽  
Movement Parameters ◽  
Timing Processes

Time perception is an essential part of our everyday lives, in both the prospective and the retrospective domains. However, our knowledge of temporal processing is mainly limited to the networks responsible for comparing or maintaining specific intervals or frequencies. In the presented fMRI study, we sought to characterize the neural nodes engaged specifically in predictive temporal analysis, the estimation of the future position of an object with varying movement parameters, and the contingent neuroanatomical signature of differences in behavioral performance between genders. The established dominant cerebellar engagement offers novel evidence in favor of a pivotal role of this structure in predictive short-term timing, overshadowing the basal ganglia reported together with the frontal cortex as dominant in retrospective temporal processing in the subsecond spectrum. Furthermore, we discovered lower performance in this task and massively increased cerebellar activity in women compared to men, indicative of strategy differences between the genders. This promotes the view that predictive temporal computing utilizes comparable structures in the retrospective timing processes, but with a definite dominance of the cerebellum.

scholarly journals UNSTABLE MORSE CODE RECOGNITION SYSTEM WITH EXPERT-GATING NEURAL NETWORK

2002 ◽  
Vol 14 (01) ◽  
pp. 12-19 ◽  
Author(s):  
DUU-TONG FUH ◽  
CHING-HSING LUO
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Disabled Persons ◽  
Single Chip ◽  
Morse Code ◽  
Average Recognition Rate ◽  
Rate Test

The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.

Does Trampolining and Anaerobic Physical Fitness Affect Sleep?

1991 ◽  
Vol 73 (1) ◽  
pp. 243-252 ◽  
Author(s):  
Joachim Buchegger ◽  
Reiner Fritsch ◽  
Alfred Meier-Koll ◽  
Hartmut Riehle
Keyword(s):  
Motor Skills ◽  
Rem Sleep ◽  
Motor Coordination ◽  
The Other ◽  
Control Group ◽  
Nocturnal Sleep ◽  
Motor Patterns ◽  
Complex Motor ◽  
Polygraphic Recordings ◽  
Experimental Group

The structure of nocturnal sleep of 16 volunteers, participating in the anaerobic sports of trampolining, dancing, and soccer, was monitored by means of polygraphic recordings. Since trampolining requires the acquisition of unfamiliar patterns of motor coordination, it can be considered as a special form of motor learning, whereas the acquisition of motor skills specific for dancing and soccer can be linked with motor patterns of normal biped locomotion. According to this view, an experimental group of 8 volunteers was formed; they participated in a training course of trampolining. In addition, a control group of 8 subjects was recruited, who engaged in one of the other two anaerobic sports. Subjects who had acquired new motor skills during a 13-wk. program in trampolining showed a statistically significant increase in REM-sleep. By contrast, the 8 subjects of the control group showed no considerable changes in REM-sleep, This suggests that efforts in acquiring new and complex motor patterns activate processes specifically involved in the generation of REM stage during nocturnal sleep.

scholarly journals Closed-loop automated reaching apparatus (CLARA) for interrogating complex motor behaviors

2021 ◽  
Author(s):  
Spencer Bowles ◽  
W. Ryan Williamson ◽  
Dailey Nettles ◽  
Jordan Hickman ◽  
Cristin G Welle
Keyword(s):  
Closed Loop ◽  
Motor Behaviors ◽  
Complex Motor

scholarly journals High order structure in mouse courtship vocalizations

2019 ◽  
Author(s):  
Stav Hertz ◽  
Benjamin Weiner ◽  
Nisim Perets ◽  
Michael London
Keyword(s):  
Neural Control ◽  
Temporal Structure ◽  
Order Structure ◽  
Sequence Structure ◽  
Motor Behaviors ◽  
Information Maximization ◽  
Complex Motor ◽  
Information Score ◽  
Complex Sequences

AbstractMany complex motor behaviors can be decomposed into sequences of simple individual elements. Mouse ultrasonic vocalizations (USVs) are naturally divided into distinct syllables and thus are useful for studying the neural control of complex sequences production. However, little is known about the rules governing their temporal order. We recorded USVs during male-female courtship (460,000 USVs grouped into 44,000 sequences) and classified them using three popular algorithms. Modeling the sequences as Markov processes revealed a significant temporal structure which was dependent on the specific classification algorithm. To quantify how syllable misclassification obscures the true underlying sequence structure, we used information theory. We developed the Syntax Information Score and ranked the syllable classifications of the three algorithms. Finally, we derived a novel algorithm (Syntax Information Maximization) that utilized sequence statistics to improve the classification of individual USVs with respect to the underlying sequence structure.

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