outline two types of neural network that you may find in the visual system and state the advantage of each?
thank you very much. im really stuck with this question.
Are you talking about rods and cones?
Rods are more around the periphery of the retina, are more sensitive after dark adaptation (absolute limen) and see only variations of black and white. In contrast, cones can see color, have better visual acuity and are more in the center of the retina. In fact, the the fovea contains only cones.
In addition, I searched Google under the key words "human rods cones" to get these possible sources:
http://hyperphysics.phy-astr.gsu.edu/hbase/vision/rodcone.html
http://www.cis.rit.edu/people/faculty/montag/vandplite/pages/chap_9/ch9p1.html
http://www.innerbody.com/image/nerv07.html
http://faculty.washington.edu/chudler/retina.html
http://en.wikipedia.org/wiki/Eye
I hope this helps. Thanks for asking.
One might be tempted to bring up the debate as to whether or not psychology should even be included as one of the sciences.
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In the visual system, two types of neural networks that are commonly found are convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each network has distinct advantages in processing visual information.
1. Convolutional Neural Networks (CNNs):
CNNs are specifically designed for visual data processing and excel in tasks such as image classification, object detection, and image segmentation. They possess the following advantages:
- Feature Extraction: CNNs automatically learn hierarchical and meaningful features from visual data, allowing them to detect patterns at different levels of abstraction.
- Spatial Invariance: CNNs are invariant to translations, meaning that they can identify objects regardless of their position in an image.
- Parameter Sharing: CNNs utilize shared weights across the network, allowing them to efficiently handle large input data while reducing the number of parameters, making them computationally efficient.
- Local Connectivity: CNNs capture spatial dependencies by connecting neurons to a small region of the input, mimicking the arrangement of receptive fields found in the visual cortex.
2. Recurrent Neural Networks (RNNs):
RNNs are known for their ability to process sequential data, making them suitable for tasks like video analysis and natural language processing. Advantages of RNNs for visual processing include:
- Temporal Dependency: RNNs can capture the temporal dependencies in sequential visual data, enabling them to learn from previous parts of the sequence to predict the future.
- Flexibility: RNNs can handle inputs of variable lengths, making them suitable for processing videos with varying lengths or processing sequential visual data in real-time.
- Memory Capacity: RNNs have the ability to store and retrieve information from previous time steps, allowing them to maintain context and long-term dependencies.
It's important to note that CNNs are commonly used for initial visual processing, extracting features, and reducing the dimensionality of the data. RNNs are often employed afterward to process the extracted features and capture temporal dependencies. The choice of which network to use depends on the specific visual task at hand.