Hybrid Neural Learning
Hybrid Neural Learning - Related Videos
21:47
Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology
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Cited by 3 •
2010
Here, we describe how to produce, expand, and immunolabel postnatal hippocampal neural progenitor cells (NPCs) in three-dimensional (3D) culture. Next, using hybrid visualization technologies, we demonstrate how digital images of immunolabelled cryosections can be used to reconstruct and map the spatial position of immunopositive cells throughout the entire 3D neurosphere.
09:22
A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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Cited by 9 •
2015
This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while rats are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different components of learning.
10:32
Enumeration of Neural Stem Cells Using Clonal Assays
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Cited by 5 •
2016
Neural stem cells (NSCs) refer to cells which can self-renew and differentiate into the three neural lineages. Here, we describe a protocol to determine NSC frequency in a given cell population using neurosphere formation and differentiation under clonal conditions.
15:42
Double Whole Mount in situ Hybridization of Early Chick Embryos
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Cited by 4 •
2008
This video demonstrates 2-color whole mount in situ hybridization, a method by which the spatial and temporal expression pattern of 2 different genes can be visualized in young chick embryos. This method was originally introduced by David Wilkinson, Domingos Henrique, Phil Ingham and David Ish -Horowicz.
10:06
An Introduction to Learning and Memory
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2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
FAQs
The neural component learns patterns or unknown relationships from measured data by adjusting weighted connections through backpropagation. The complementary component contributes known system behavior through physical laws, established models, symbolic rules, or optimization methods. This division allows learning to address what is uncertain while domain knowledge constrains or structures predictions, which is valuable when engineering systems are only partly understood.
These components provide constraints or representations that purely data-driven learning may lack. Physical laws and established models express known system behavior, symbolic rules encode structured knowledge, and optimization methods can guide solutions. Their inclusion can improve data efficiency, interpretability, robustness, and generalization, especially when measured data are limited but reliable engineering knowledge is available.
A purely neural network relies on patterns available in data, whereas a hybrid approach combines that learning capacity with established models, physical laws, symbolic rules, or optimization methods. The added structure can reduce dependence on large amounts of measured data and make predictions more interpretable or robust. This distinction matters for engineering systems that are difficult to describe from data alone.
Generalization depends on the relationship between the available measurements and the domain knowledge incorporated into the model. When data are limited, established system behavior can provide additional structure rather than leaving every relationship to be learned. Hybrid neural learning is therefore suited to settings where physical or engineering knowledge can complement incomplete measurements and support more reliable predictions.
The approach requires measured data for learning patterns or unknown relationships, together with some established understanding of the target system. That understanding may take the form of a model, physical law, symbolic rule, or optimization method. Combining these inputs helps represent known behavior while allowing the neural component to address relationships that are not already specified.
Applications identified for this approach include system identification, process control, fault diagnosis, robotics, digital twins, and engineering design. These problems often involve complex behavior that is difficult to capture entirely with equations or entirely with data. A hybrid model can use measurements to learn unresolved relationships while retaining relevant engineering structure for the application.
Engineers can use these models to obtain system representations, predictions, or decisions that draw on both measured behavior and domain knowledge. In the listed applications, this supports modeling, control, diagnosis, simulation through digital twins, and design. The intended benefits include improved data efficiency, interpretability, robustness, and generalization compared with relying on purely data-driven modeling.