Neural Networks
Neural Networks - Related Videos
03:33
Visualization of Neural and Vascular Networks in a Chicken Embryo
0 Views •
2025
Source: Delalande, J., et.al. Dual Labeling of Neural Crest Cells and Blood Vessels Within Chicken Embryos Using ChickGFP Neural Tube Grafting and Carbocyanine Dye DiI Injection. J. Vis. Exp. (2015)This video demonstrates the transplantation of a GFP-labeled donor neural tube from a stage-matched transgenic chicken embryo into a recipient embryo at the level of somites one to seven, followed by vascular labeling using a lipophilic fluorescent dye. The combined approach allows for direct...
02:53
Developing a Micro-Tissue-Engineered Neural Network Using a Hydrogel-Based Micro-column
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2025
Source: Struzyna, L. A. et. al., Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling. J. Vis. Exp. (2017)This video demonstrates the development of micro-tissue-engineered neural networks using a hydrogel micro-column with an extracellular matrix core. Seeded neuronal aggregates adhere, extend projections, and form structured neural networks, contributing to advancements in neurodevelopment and...
03:07
Assessing the Effects of Toxins on Chick Embryo Neural Network Development Using Multielectrode Arrays
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2025
This video demonstrates the use of multi-electrode arrays (MEA) to study the effects of toxins on early embryonic chick neuronal cultures. By monitoring the synchronous activity of the neurons in the neuronal network, the MEA captures the functional dynamics of the neuronal network in response to toxin exposure. Reduced synchrony and firing rates upon toxin exposure highlight its detrimental impact on neuronal network maturation and function.
10:45
Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
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Cited by 40 •
2017
This manuscript details the fabrication of micro-tissue engineered neural networks: three-dimensional micron-sized constructs comprised of long aligned axonal tracts spanning aggregated neuronal population(s) encased in a tubular hydrogel. These living scaffolds can serve as functional relays to reconstruct or modulate neural circuitry or as biofidelic test-beds mimicking gray-white matter neuroanatomy.
13:19
Deep Neural Networks for Image-Based Dietary Assessment
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Cited by 13 •
2021
The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.
FAQs
The network’s adjustable weights determine how strongly each incoming signal contributes to a unit’s result. The activation function then transforms the combined input before the signal moves forward. Changing either the weighting pattern or the activation response can alter later outputs, making these components important for explaining how models represent patterns and generate decisions.
Learning occurs when an algorithm compares the model’s predicted outcome with the observed outcome and adjusts the connection weights to reduce their difference. Repeated adjustment changes how the network responds to information, allowing researchers to examine how experience could produce improved performance in tasks such as concept acquisition, stimulus recognition, language production, or choice.
A model becomes theoretically useful when its behavior can be compared with human performance rather than judged only by its predictions. Agreement may support a proposed account of cognition, while systematic differences can reveal limitations or suggest missing mechanisms. This comparison helps researchers evaluate explanations of learning, recognition, language, and decision-making.
Researchers first identify a psychological task and provide information relevant to that task, such as examples associated with concepts, stimuli, language, or choices. They specify interconnected units, adjustable weights, and activation functions, then train the model by reducing differences between predicted and observed outcomes. Finally, they compare the model’s behavior with human data.
Neural networks can be applied to several broad areas of psychology, including acquiring concepts, recognizing stimuli, producing language, and making choices from experience. The same general modeling framework can therefore address different forms of cognition while preserving a common focus on how patterns of input and learned connections produce behavior.
Researchers can use neural network models to develop computational accounts of why people may differ in psychological performance. Differences in model behavior can be examined alongside differences in human data, helping connect variation in learning, recognition, language, or decision-making to the model’s information-processing account rather than describing individual differences only at a behavioral level.