Related Experiment Video
Updated: Apr 1, 2026
07:13
A Two-interval Forced-choice Task for Multisensory Comparisons
Published on: November 9, 2018
11.6K
A Neural Mechanism for Sensing and Reproducing a Time Interval
Mehrdad Jazayeri1, Michael N Shadlen2
1Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Current Biology : CB
|October 13, 2015
Summary
Neural mechanisms for time reproduction were explored in monkeys. Parietal cortex activity revealed a nonlinear response during interval measurement and a ramping activity during reproduction, encoding the measured time for sensorimotor control.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Sensorimotor Control
Background:
- Precise timing is essential for sensorimotor functions.
- Neural basis for flexible time interval measurement and reproduction remains unclear.
Purpose of the Study:
- Investigate neural mechanisms underlying time interval reproduction.
- Identify how the brain encodes time during measurement and production phases.
Main Methods:
- Recorded neural activity in the parietal cortex of monkeys performing a time reproduction task.
- Monkeys measured and reproduced various time intervals.
- Analyzed neural response profiles and their correlation with interval duration and reproduction accuracy.
Main Results:
- Neural responses during interval measurement showed a nonlinear profile increasing with duration.
- Activity reset post-measurement, followed by a ramp encoding the sample interval.
- Firing rates correlated with ramp slope and reproduction interval on a trial-by-trial basis.
Conclusions:
- Parietal cortex plays a key role in encoding time intervals for sensorimotor tasks.
- Neural activity dynamically represents time during both measurement and reproduction.
- Interval timing may involve prospective encoding relative to motor planning.
Related Concept Videos
01:21Time and frequency -Domain Interpretation of Phase-lag Control
447
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
447
01:21Perceiving Loudness, Pitch, and Location
1.3K
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
1.3K
01:27Time and frequency -Domain Interpretation of PI Control
478
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
478
01:30Classification of Signals
1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
01:22Basic Continuous Time Signals
795
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
795
01:11Sampling Continuous Time Signal
838
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
838