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Neural pathway prediction based on multi-neuron spike train data

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The finding of Neural Pathway is typically based on brain slicing and reconstruction with nanoscale imaging... Two strategies are proposed for neural pathway prediction: (1) The time-ordered strategy: Synapses exist between neurons that generate spikes at the two neighborhood time points (the time point N and N-1). (2) The spike co-occurrence strategy: Synapses exist between neurons that fire together at the same time point... Data of the rat hippocampus CA3 pyramidal cell layer based on functional Multineuron Calcium Imaging (fMCI) is used for neural pathway prediction (Including 8 datasets, and each of them records spike activities for 62 to 226 neurons... Select the first 80% of the sub datasets (S1,…,S16) for neural pathway construction, and validate the pathway using the rest of the 20% sub dataset (Here we assume that if the predicted pathway is correct, it should cover the neuronal connections based on the rest of the 20% sub dataset). (2) Select another 80% of the sub datasets for neural pathway construction, and the rest for validation, and repeat this step until all the sub datasets have been selected for validation. (3) The prediction accuracy is the average value of the 20 predictions... There are several important observations and indications based on the prediction results. (1) Although the two prediction strategies seem entirely different, the neural pathways based on the two different strategies are very relevant (The correlation is significant with the Pearson correlation value 0.958)... It indicates that although the proposed strategies are different, the results from the different two strategies do not have major conflicts, instead they are very consistent, and support each other. (2) The neural pathway prediction accuracy for the time-ordered strategy is 83%, and the accuracy for the spike co-occurrence strategy is 80%... This result indicates that better prediction can be made when the predicted pathways from the two strategies are combined together. (3) 27% of the possible connections among neurons are selected for the time-ordered strategy, while 25% of the connections are selected by the spike co-occurrence strategy... If the two results are grouped together, 32% of the possible connections are included... The results seem good, since the coverage is not high (and is consistent with the observation by using electro-microscopy techniques ), while the predicted accuracy for possible neural pathway reaches 89%... Whether the proposal is applicable when the distance goes further needs to be validated... In addition, our current result is based on fMCI data from rat brain slices.

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Neural Pathway Prediction Accuracy based on fMCI Multi-neuron Spike Train Dataset
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Figure 1: Neural Pathway Prediction Accuracy based on fMCI Multi-neuron Spike Train Dataset

Mentions: There are several important observations and indications based on the prediction results. (1) Although the two prediction strategies seem entirely different, the neural pathways based on the two different strategies are very relevant (The correlation is significant with the Pearson correlation value 0.958). It indicates that although the proposed strategies are different, the results from the different two strategies do not have major conflicts, instead they are very consistent, and support each other. (2) The neural pathway prediction accuracy for the time-ordered strategy is 83%, and the accuracy for the spike co-occurrence strategy is 80%. When we group the two neural pathways together (denoted as the merged strategy), the prediction accuracy reaches 89% (Figure 1 shows the prediction accuracy for each of the dataset using the proposed strategies). This result indicates that better prediction can be made when the predicted pathways from the two strategies are combined together. (3) 27% of the possible connections among neurons are selected for the time-ordered strategy, while 25% of the connections are selected by the spike co-occurrence strategy. If the two results are grouped together, 32% of the possible connections are included. The results seem good, since the coverage is not high (and is consistent with the observation by using electro-microscopy techniques [3,4]), while the predicted accuracy for possible neural pathway reaches 89%.


Neural pathway prediction based on multi-neuron spike train data
Neural Pathway Prediction Accuracy based on fMCI Multi-neuron Spike Train Dataset
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4126519&req=5

Figure 1: Neural Pathway Prediction Accuracy based on fMCI Multi-neuron Spike Train Dataset
Mentions: There are several important observations and indications based on the prediction results. (1) Although the two prediction strategies seem entirely different, the neural pathways based on the two different strategies are very relevant (The correlation is significant with the Pearson correlation value 0.958). It indicates that although the proposed strategies are different, the results from the different two strategies do not have major conflicts, instead they are very consistent, and support each other. (2) The neural pathway prediction accuracy for the time-ordered strategy is 83%, and the accuracy for the spike co-occurrence strategy is 80%. When we group the two neural pathways together (denoted as the merged strategy), the prediction accuracy reaches 89% (Figure 1 shows the prediction accuracy for each of the dataset using the proposed strategies). This result indicates that better prediction can be made when the predicted pathways from the two strategies are combined together. (3) 27% of the possible connections among neurons are selected for the time-ordered strategy, while 25% of the connections are selected by the spike co-occurrence strategy. If the two results are grouped together, 32% of the possible connections are included. The results seem good, since the coverage is not high (and is consistent with the observation by using electro-microscopy techniques [3,4]), while the predicted accuracy for possible neural pathway reaches 89%.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

The finding of Neural Pathway is typically based on brain slicing and reconstruction with nanoscale imaging... Two strategies are proposed for neural pathway prediction: (1) The time-ordered strategy: Synapses exist between neurons that generate spikes at the two neighborhood time points (the time point N and N-1). (2) The spike co-occurrence strategy: Synapses exist between neurons that fire together at the same time point... Data of the rat hippocampus CA3 pyramidal cell layer based on functional Multineuron Calcium Imaging (fMCI) is used for neural pathway prediction (Including 8 datasets, and each of them records spike activities for 62 to 226 neurons... Select the first 80% of the sub datasets (S1,…,S16) for neural pathway construction, and validate the pathway using the rest of the 20% sub dataset (Here we assume that if the predicted pathway is correct, it should cover the neuronal connections based on the rest of the 20% sub dataset). (2) Select another 80% of the sub datasets for neural pathway construction, and the rest for validation, and repeat this step until all the sub datasets have been selected for validation. (3) The prediction accuracy is the average value of the 20 predictions... There are several important observations and indications based on the prediction results. (1) Although the two prediction strategies seem entirely different, the neural pathways based on the two different strategies are very relevant (The correlation is significant with the Pearson correlation value 0.958)... It indicates that although the proposed strategies are different, the results from the different two strategies do not have major conflicts, instead they are very consistent, and support each other. (2) The neural pathway prediction accuracy for the time-ordered strategy is 83%, and the accuracy for the spike co-occurrence strategy is 80%... This result indicates that better prediction can be made when the predicted pathways from the two strategies are combined together. (3) 27% of the possible connections among neurons are selected for the time-ordered strategy, while 25% of the connections are selected by the spike co-occurrence strategy... If the two results are grouped together, 32% of the possible connections are included... The results seem good, since the coverage is not high (and is consistent with the observation by using electro-microscopy techniques ), while the predicted accuracy for possible neural pathway reaches 89%... Whether the proposal is applicable when the distance goes further needs to be validated... In addition, our current result is based on fMCI data from rat brain slices.

No MeSH data available.