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Neural spike prediction based on spreading activation

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For each neuron in a neural network, its behavior does not only be decided by its own property, but also very relevant to its contexts (e.g. other neurons in the same network)... Hence, effective prediction of neural spike activities in a network context requires at least the following three major efforts: (1) Response prediction of a single neuron towards a stimulus, (2) Obtaining the detailed network structure, with synapse information among neurons, (3) Modeling signal transmission based on the neural network... In addition, the followings should be considered in the modeling process: (1) when reaching the soma, the voltages will be reduced during the signal transmission process from synapses. (2) The voltage at the soma is sometimes a collective contribution from multiple neurons... Hence, iterations of the spreading activation process are needed... The spike prediction accuracy for each of the dataset is shown in Figure 1... As a comparative study, we introduce two alternative strategies, namely the shortest distance strategy (the neuron which owns the shortest distance compared to other post synaptic neurons will be fired), and the synapse based random selection strategy (randomly select a neuron from the set of post synaptic neurons)... As shown in Figure 1, the spreading activation strategy outperforms other two strategies and the average prediction accuracy on 8 datasets is around 13.2% (the average prediction accuracy for shortest distance strategy is 3.8%, while the synapse based random selection strategy is 5.0%)... The validation shows that the proposed spreading activation strategy is potentially effective for predicting neural spikes in the neural network.

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Neural Spike Prediction Accuracy based on Different Strategies
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Figure 1: Neural Spike Prediction Accuracy based on Different Strategies

Mentions: In order to validate the proposed method, the data from the rat hippocampus CA3 pyramidal cell layer using functional Multineuron Calcium Imaging (fMCI) is used (Including 8 datasets, and each of them records spike activities for 62 to 226 neurons. The datasets were pictured with the frequency of 10Hz [4,5]). Since the time slot during two neighborhood pictures is 100ms, signal transmissions may have done for several rounds. Hence, iterations of the spreading activation process are needed. The spike prediction accuracy for each of the dataset is shown in Figure 1.


Neural spike prediction based on spreading activation
Neural Spike Prediction Accuracy based on Different Strategies
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4126541&req=5

Figure 1: Neural Spike Prediction Accuracy based on Different Strategies
Mentions: In order to validate the proposed method, the data from the rat hippocampus CA3 pyramidal cell layer using functional Multineuron Calcium Imaging (fMCI) is used (Including 8 datasets, and each of them records spike activities for 62 to 226 neurons. The datasets were pictured with the frequency of 10Hz [4,5]). Since the time slot during two neighborhood pictures is 100ms, signal transmissions may have done for several rounds. Hence, iterations of the spreading activation process are needed. The spike prediction accuracy for each of the dataset is shown in Figure 1.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

For each neuron in a neural network, its behavior does not only be decided by its own property, but also very relevant to its contexts (e.g. other neurons in the same network)... Hence, effective prediction of neural spike activities in a network context requires at least the following three major efforts: (1) Response prediction of a single neuron towards a stimulus, (2) Obtaining the detailed network structure, with synapse information among neurons, (3) Modeling signal transmission based on the neural network... In addition, the followings should be considered in the modeling process: (1) when reaching the soma, the voltages will be reduced during the signal transmission process from synapses. (2) The voltage at the soma is sometimes a collective contribution from multiple neurons... Hence, iterations of the spreading activation process are needed... The spike prediction accuracy for each of the dataset is shown in Figure 1... As a comparative study, we introduce two alternative strategies, namely the shortest distance strategy (the neuron which owns the shortest distance compared to other post synaptic neurons will be fired), and the synapse based random selection strategy (randomly select a neuron from the set of post synaptic neurons)... As shown in Figure 1, the spreading activation strategy outperforms other two strategies and the average prediction accuracy on 8 datasets is around 13.2% (the average prediction accuracy for shortest distance strategy is 3.8%, while the synapse based random selection strategy is 5.0%)... The validation shows that the proposed spreading activation strategy is potentially effective for predicting neural spikes in the neural network.

No MeSH data available.