LEARNING, MEMORY AND PLASTICITY
Excitatory postsynaptic potential - Wikipedia, the free encyclopedia
Biological neural network - Wikipedia, the free encyclopedia
- Backpropagating APs are impossible because after an action potential travels down a given segment of the axon, the voltage gated sodium channels' (Na+ channels) m gate becomes closed, thus blocking any transient opening of the h gate from causing a change in the intracellular [Na+], and hence preventing the generation of an action potential back towards the cell body. In some cells, however, neural backpropagation does occur through the dendritic arbor and may have important effects on synaptic plasticity and computation.
Biological neural network - Wikipedia, the free encyclopedia
Connections display temporal and spatial characteristics. Temporal characteristics refer to the continuously modified activity-dependent efficacy of synaptic transmission, called spike dependent synaptic plasticity. It has been observed in several studies that the synaptic efficacy of this transmission can undergo shortterm increase (called facilitation) or decrease (depression) according to the activity of the presynaptic neuron. The induction of long-term changes in synaptic efficacy, by long-term potentiation (LTP) or depression (LTD), depends strongly on the relative timing of the onset of the EPSP generated by the pre-synaptic AP, and the post-synaptic action potential. LTP is induced by a series of action potentials which cause a variety of biochemical responses. Eventually the reactions cause the insertion of new receptors into the cellular membrane of the dendrites, or serve to increase the efficacy of the receptors through phosphorylation.
In the brain, memories are very likely represented by patterns of activation amongst networks of neurons. However, how these representations are formed, retrieved and reach conscious awareness is not completely understood. Cognitive processes that characterize human intelligence are mainly ascribed to the emergent properties of complex dynamic characteristics in the complex systems that constitute neural networks. Therefore, the study and modeling of these networks have attracted broad interest under different paradigms and many different theories have been formulated to explain various aspects of their behavior. One of these — and the subject of several theories — is considered a special property of a neural network: the ability to learn complex patterns.
Another issue, called the binding problem, relates to the question of how the activity of more or less distinct populations of neurons dealing with different aspects of perception are combined to form a unified perceptual experience and have qualia.
Neural Network Models in Excel
- If you happen to be interested in Self Organizing Maps (SOM), click
here to see a small
clustering tool in Excel
using SOMs. Also, if you are interested in tree based Classification
Model, here is a
tree based Classifier in Excel.
NeuroSolutions: What is a Neural Network?
A good way to introduce the topic is to take a look at a typical application of neural networks. Many of today's document scanners for the PC come with software that performs a task known as optical character recognition (OCR). OCR software allows you to scan in a printed document and then convert the scanned image into to an electronic text format such as a Word document, enabling you to manipulate the text. In order to perform this conversion the software must analyze each group of pixels (0's and 1's) that form a letter and produce a value that corresponds to that letter. Some of the OCR software on the market use a neural network as the classification engine.
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