Information Content in Neuronal Calcium Spike Trains: Entropy Rate Estimation based on Empirical Probabilities

Ande, Sathish; Avasarala, Srinivas; Regatti, Jayanth Reddy; Pandey, Neha; Swain, Sarpras; Karunarathne, Ajith; Giri, Lopamudra; Jana, Soumya

doi:10.1109/iecbes48179.2021.9398798

Cited by 4 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 for time course of calcium responses for aforementioned neurons). We inferred spike train for each neuron from its fluorescent time signal using a deconvolution algorithm and those were binarized by adaptive threshold [8], [11]. Such binary spike trains were used for quantification analysis.…”

Section: A Data Collection and Spike Train Inferencementioning

confidence: 99%

“…In the preceding, H(U |V ) denotes the conditional entropy of U given V . similarly, the entropy rate estimators for X n and Y n are as follows [8].…”

Section: Proposed Estimator Based On Empirical Probabilitiesmentioning

confidence: 99%

“…However, these methods have slow convergence i.e., they need large length spike trains for estimation, with out identifying the fact that the neuronal information changes in short time windows [7]. In this backdrop, we propose a fast yet accurate empirical method to estimate the mutual information rate by exploiting memory structures in calcium 1 Indian Institute of Technology Hyderabad, Telangana, India 2 The University of Toledo, Ohio, USA spike trains [8], [9]. Our method has shown superiority over existing LZ algorithms with example Markov processes and with heterogeneous calcium responses, recorded from hippocampal region of the brain, where memory and learning tasks performed.…”

Section: Introductionmentioning

confidence: 99%

“…1.Imaging of neuronal calcium spiking: illustrative image of hippocampal neuron population with eight neurons indexed 1-8, as explained in Section II-A. Scale bar = 20 µm[8].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Correlation in Neuronal Calcium Spiking: Quantification based on Empirical Mutual Information Rate

Ande,

Avasarala,

Karunarathne

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Quantification of neuronal correlations in neuron population helps us to understand neural coding rules. Such quantification could also reveal how neurons encode information in normal and disease conditions like Alzheimer's and Parkinson's. While neurons communicate with each other by transmitting spikes, there would be change in calcium concentration within the neurons inherently. Accordingly, there would be correlations in calcium spike trains and they could have heterogeneous memory structures. In this context, estimation of mutual information rate in calcium spike trains assumes primary significance. However, such estimation is difficult with available methods which would consider longer blocks for convergence without noticing that neuronal information changes in short time windows. Against this backdrop, we propose a faster method which exploits the memory structures in pair of calcium spike trains to quantify mutual information shared between them. Our method has shown superior performance with example Markov processes as well as experimental spike trains. Such mutual information rate analysis could be used to identify signatures of neuronal behaviour in large populations in normal and abnormal conditions.

show abstract

Section: A Data Collection and Spike Train Inferencementioning

confidence: 99%

“…In the preceding, H(U |V ) denotes the conditional entropy of U given V . similarly, the entropy rate estimators for X n and Y n are as follows [8].…”

Section: Proposed Estimator Based On Empirical Probabilitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…1.Imaging of neuronal calcium spiking: illustrative image of hippocampal neuron population with eight neurons indexed 1-8, as explained in Section II-A. Scale bar = 20 µm[8].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Correlation in Neuronal Calcium Spiking: Quantification based on Empirical Mutual Information Rate

Ande,

Avasarala,

Karunarathne

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the quest for a method that can be applied to short sequences, we consider LZ-76, a LZ-based fast method, but find it to be inaccurate. Against this backdrop, we propose a Hellinger distance measure based on empirical probabilities of patterns in each pair of spike trains [5]. Our method converges faster than LZ-78, and hence may be used on short sequences, while being comparably accurate.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance

Ande¹,

Regatti²,

Pandey³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Statistical similarities between neuronal spike trains could reveal significant information on complex underlying processing. In general, the similarity between synchronous spike trains is somewhat easy to identify. However, the similar patterns also potentially appear in an asynchronous manner. However, existing methods for their identification tend to converge slowly, and cannot be applied to short sequences.In response, we propose Hellinger distance measure based on empirical probabilities, which we show to be as accurate as existing techniques, yet faster to converge for synthetic as well as experimental spike trains. Further, we cluster pairs of neuronal spike trains based on statistical similarities and found two non-overlapping classes, which could indicate functional similarities in neurons. Significantly, our technique detected functional heterogeneity in pairs of neuronal responses with the same performance as existing techniques, while exhibiting faster convergence. We expect the proposed method to facilitate large-scale studies of functional clustering, especially involving short sequences, which would in turn identify signatures of various diseases in terms of clustering patterns.

show abstract

Statistical Dependence between Neuronal Spike Train Pairs: Quantification based on Empirical Mutual Information Rate

Ande

Avasarala

Karunarathne³

et al. 2021

2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

View full text Add to dashboard Cite

Information Content in Neuronal Calcium Spike Trains: Entropy Rate Estimation based on Empirical Probabilities

Cited by 4 publications

References 20 publications

Correlation in Neuronal Calcium Spiking: Quantification based on Empirical Mutual Information Rate

Correlation in Neuronal Calcium Spiking: Quantification based on Empirical Mutual Information Rate

Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance

Statistical Dependence between Neuronal Spike Train Pairs: Quantification based on Empirical Mutual Information Rate

Contact Info

Product

Resources

About