Application of a unifying reward-prediction error (RPE)-based framework to explain underlying dynamic dopaminergic activity in timing tasks

SUMMARYThis manuscript is intended as a theoretical companion to Hamiloset al., 20201, in which we examined the role of dopaminergic neurons (DANs) in self-timed movements. In that study, we recorded DAN signals in mice trained to initiate a licking movement after a self-timed delay following a start-timing cue. DAN signals both before the start-timing cue and during the timing interval predicted the timing of movement onset, up to seconds before the movement itself. In particular, dopaminergic signals “ramped up” from the time of the cue to the time of the movement. On a given trial, the slope of the ramping was predictive of when the movement would occur, with steep slope associated with early movement and shallow slope with late movement, reminiscent of a ramp-to-threshold process.Ramping dopaminergic signals were recently proposed in a theoretical framework that examined temporal-difference learning under resolved state uncertainty (Mikhael et al., 20192; Mikhael & Gershman, 20193; Gershman, 20144). Here, we show that an adapted version of Mikhael et al.’s model recapitulates the ramping dopaminergic signaling observed in our self-timed movement task. We also applied the model to results reported in a recent temporal bisection study, in which mice categorized time intervals as relatively short or long compared to a criterion interval (Soares et al., 20165). The model successfully predicted the relative amplitude of dynamic DAN signals observed in the bisection task. These combined results suggest a common neural mechanism that broadly underlies timing behavior: trial-by-trial variation in the rate of the internal “pacemaker,” manifested in DAN signals that reflect stretching or compression of the derivative of the subjective value function relative to veridical time. In this view, faster pacemaking is associated with relatively high amplitude dopaminergic signaling, whereas slower pacemaking is associated with relatively low levels of dopaminergic signaling.

Dynamic dopaminergic activity controls the timing of self-timed movement

SUMMARYDeciding when to initiate action is essential to survival. Insights from movement disorders and pharmacological studies implicate the neurotransmitter dopamine as a regulator of movement timing, but the underlying neural mechanisms are not understood. Here we show dynamic dopaminergic signaling over seconds-long timescales controls movement timing in mice. Animals were trained to initiate licking after a self-timed interval following a start-timing cue. Surprisingly, dopaminergic signals ramped-up slowly between the start-timing cue and the self-timed movement, with the slope predicting the movement time on single trials. Steeply rising signals preceded early lick-initiation, whereas slowly rising signals preceded later initiation, reminiscent of a ramp-to-threshold process. Higher baseline activity also predicted earlier self-timed movements. Optogenetic activation of dopamine neurons during self-timing caused systematic early-shifting of movement initiation, whereas inhibition caused late-shifting. These results reveal a causal role for dynamic dopaminergic signaling unfolding over seconds in controlling the moment-by-moment decision of when to move.

Design of Artificial Neural Networks to Recognize Fingerprint Patterns

Image or pattern recognition system is one of the branches in computer science, this system can help the processing of fingerprint patterns, especially in the banking, police and users of other institutions who really feel the importance of using fingerprints. Several stages in fingerprint pattern image recognition are through the process of scanning, then the resulting digital fingerprint image is converted to a certain value, among others, the threshold process, the division of images, and representation of input values. The training process is carried out using two treatments: the first with a different level of understanding and the second training with different unit numbers, the best training is obtained with a level of understanding of 0.3 and the number of hidden units 10 by producing a short training time and relatively small errors. Fingerprint pattern recognition is done by two trials, based on 1 number of training patterns and 5 number of training patterns. From the research data, the ability of the system to recognize output patterns is greater if the number of training patterns increases, with a number of 1 training patterns, the system is able to recognize 50% external patterns while the 5 system training patterns are able to recognize 70% output patterns.

INFLUENCE OF THE METHOD OF FASTENING OF THE STRESSLESS TAGS TO THE THRESHOLD PROCESS

Introducing chipless taps into production it is necessary to take into account factors arising from the specifics of the method and factors affecting economic efficiency. Significant effect from the introduction of the machine method of manufacturing threaded holes in stainless steels with brushless taps can be obtained only if the complex of measures is observed, related to the choice of the optimum hole diameter and the quality of preparation of the hole for the thread, the choice of mounting the tap on the machine, the speed of thread extrusion, coolant equipment and others. A new contour design has been proposed in the paper to prevent damage to the instrument for the manufacture of internal threaded holes. In addition, the design allows to replace quickly the instrument to another size. The designed construction of the cartridge ensures the centering of the tool over the hole being machined and ensures the transmission of torque of the required size. Analytical dependencies and computational schemes that ensure the operability of the device are derived.

An online de-noising method for oil ultrasonic wear debris signal: fuzzy morphology component analysis

Purpose In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection. Design/methodology/approach For completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function. Findings According to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor. Research limitations/implications The rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further. Practical implications This paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection. Originality value This paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.

Motor selection dynamics in FEF explain the reaction time variance of saccades to single targets

In studies of voluntary movement, a most elemental quantity is the reaction time (RT) between the onset of a visual stimulus and a saccade toward it. However, this RT demonstrates extremely high variability which, in spite of extensive research, remains unexplained. It is well established that, when a visual target appears, oculomotor activity gradually builds up until a critical level is reached, at which point a saccade is triggered. Here, based on computational work and single-neuron recordings from monkey frontal eye field (FEF), we show that this rise-to-threshold process starts from a dynamic initial state that already contains other incipient, internally driven motor plans, which compete with the target-driven activity to varying degrees. The ensuing conflict resolution process, which manifests in subtle covariations between baseline activity, build-up rate, and threshold, consists of fundamentally deterministic interactions, and explains the observed RT distributions while invoking only a small amount of intrinsic randomness.

Motor selection dynamics in FEF explain the reaction time variance of saccades to single targets

In studies of voluntary movement, a most elemental quantity is the reaction time (RT) between the onset of a visual stimulus and a saccade toward it. However, this RT demonstrates extremely high variability, which in spite of extensive research remains unexplained. It is well established that, when a visual target appears, oculomotor activity gradually builds up until a critical level is reached, at which point a saccade is triggered. Here, we further characterize the dynamics of this rise-to-threshold process based on computational work and single-neuron recordings from the frontal eye field (FEF) of behaving monkeys. We find that the baseline activity, build-up rate, and threshold level show strong, nonlinear co-dependencies that explain the distinct RT distributions observed experimentally. The results indicate that intrinsic randomness contributes little to saccade variance, which results mainly from an intricate, fundamentally deterministic mechanism of motor conflict resolution that has subtle yet highly characteristic manifestations.

MODEL MONITORING DAN IDENTIFIKASI PELANGGAR DI JALUR TRANSJAKARTA MENGGUNAKAN LIBRARY TESSERACT OCR PADA RASPBERRY PI 3 MODEL B

TransJakarta Bus Lane is a special lane that is only passed by TransJakarta bus and is not allowed for other riders entering and passing the lane. But by reason of avoiding congestion, some riders break through the Transjakarta bus lane. This has distrubed Transjakarta bus travel schedule. Several attempts have been made by the government to prevent riders from entering TransJakarta route by installing 50 cm separator in several corridors, making Moveable concrete barriere and since Monday, 25 Noveber 2013 has been applied a maximum fine of Rp 500,000 for both the two- and four-wheeled vehicles or more, entering into a special line of TransJakarta bus or busway lane refers to article 287, Law No. 22 of 2009. The purpose of this study is to monitor in real time the rider who commits a violation or breaks through TransJakarta bus lane. The monitoring system is built in the form of hardware model consisting of Raspberry Pi 3 Model B, ultrasonic sensor HC-SR04, webcamera, and software in the form of website applications. Data processing vehicle license plate number using tesseract ocr library on raspberry. When the hardware model is turned on the ultrasonic sensor HC-SR04 detects the distance of the vehicle object, at a distance of ≤ 10 cm webcamera shoots the vehicle license plate model. Images taken by webcamera processed by raspberry using tesseract ocr library through threshold process so it can be read. The results is displayed on the website application so that it can known the identity of the violator.