Related Experiment Video
Updated: Sep 2, 2025
09:34
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
4.1K
Dynamic branching in a neural network model for probabilistic prediction of sequences
Elif Köksal Ersöz1,2, Pascal Chossat3,4, Martin Krupa3,4
1Univ Rennes, INSERM, LTSI - UMR 1099, Campus Beaulieu, Rennes, F-35000, France. elif.koksal-ersoz@inserm.fr.
Journal of Computational Neuroscience
|August 10, 2022
Summary
This study explores how brain networks predict stimuli using computational models. It reveals that neuronal gain variations optimize prediction accuracy without altering synaptic efficacy.
Area of Science:
- Computational neuroscience
- Neural network modeling
- Brain function
Background:
- The brain's predictive capabilities are crucial for stimulus processing.
- Understanding neural mechanisms of prediction is key to neuroscience.
Purpose of the Study:
- To investigate the branching behavior of a computational neural network model.
- To analyze how synaptic properties influence predictive sequence selection.
Main Methods:
- Analytical modeling of neural networks.
- Simulations of excitatory and inhibitory neuron populations.
- Examination of synaptic efficacy, retroactive inhibition, and short-term synaptic depression.
Main Results:
- Synaptic factors determine the selection dynamics between predictive branches.
- Neuronal gain variations enable adaptation to changing stimulus probabilities.
- Networks optimize predictions without modifying synaptic efficacy.
Conclusions:
- Neuronal gain is a critical mechanism for adaptive prediction in neural networks.
- Computational models provide insights into the brain's predictive processing.
- This research highlights the interplay between network dynamics and predictive accuracy.
Related Concept Videos
01:26Per-Unit Sequence Models
113
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
113
01:24Sequence Networks of Rotating Machines
135
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
135
01:17Radical Chain-Growth Polymerization: Chain Branching
2.0K
The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
2.0K
01:22Multi-input and Multi-variable systems
145
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
145
02:24Predicting Reaction Outcomes
8.6K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.6K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K