| Year |
Citation |
Score |
| 2020 |
Paprocki B, Pregowska A, Szczepanski J. Optimizing information processing in brain-inspired neural networks Bulletin of the Polish Academy of Sciences-Technical Sciences. 225-233. DOI: 10.24425/Bpasts.2020.131844 |
0.705 |
|
| 2019 |
Pregowska A, Proniewska K, van Dam P, Szczepanski J. Using Lempel-Ziv complexity as effective classification tool of the sleep-related breathing disorders. Computer Methods and Programs in Biomedicine. 182: 105052. PMID 31476448 DOI: 10.1016/J.Cmpb.2019.105052 |
0.7 |
|
| 2019 |
Pregowska A, Casti A, Kaplan E, Wajnryb E, Szczepanski J. Information processing in the LGN: a comparison of neural codes and cell types. Biological Cybernetics. PMID 31243531 DOI: 10.1007/S00422-019-00801-0 |
0.8 |
|
| 2019 |
Pregowska A, Kaplan E, Szczepanski J. How Far can Neural Correlations Reduce Uncertainty? Comparison of Information Transmission Rates for Markov and Bernoulli Processes. International Journal of Neural Systems. 1950003. PMID 30841769 DOI: 10.1142/S0129065719500035 |
0.814 |
|
| 2016 |
Pregowska A, Szczepanski J, Wajnryb E. Temporal code versus rate code for binary Information Sources Neurocomputing. 216: 756-762. DOI: 10.1016/J.Neucom.2016.08.034 |
0.809 |
|
| 2015 |
Pregowska A, Szczepanski J, Wajnryb E. Mutual information against correlations in binary communication channels. Bmc Neuroscience. 16: 32. PMID 25986973 DOI: 10.1186/S12868-015-0168-0 |
0.817 |
|
| 2013 |
Paprocki B, Szczepanski J. Transmission efficiency in ring, brain inspired neuronal networks. Information and energetic aspects. Brain Research. 1536: 135-43. PMID 23891793 DOI: 10.1016/J.Brainres.2013.07.024 |
0.774 |
|
| 2013 |
Arnold MM, Szczepanski J, Montejo N, Amigó JM, Wajnryb E, Sanchez-Vives MV. Information content in cortical spike trains during brain state transitions. Journal of Sleep Research. 22: 13-21. PMID 22737985 DOI: 10.1111/J.1365-2869.2012.01031.X |
0.766 |
|
| 2013 |
Paprocki B, Szczepanski J, Kolbuk D. Information transmission efficiency in neuronal communication systems Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P217 |
0.776 |
|
| 2013 |
Paprocki B, Szczepanski J. How do the amplitude fluctuations affect the neuronal transmission efficiency Neurocomputing. 104: 50-56. DOI: 10.1016/J.Neucom.2012.11.001 |
0.759 |
|
| 2011 |
Paprocki B, Szczepanski J. Efficiency of neural transmission as a function of synaptic noise, threshold, and source characteristics. Bio Systems. 105: 62-72. PMID 21439348 DOI: 10.1016/J.Biosystems.2011.03.005 |
0.76 |
|
| 2011 |
Szczepanski J, Arnold M, Wajnryb E, Amigó JM, Sanchez-Vives MV. Mutual information and redundancy in spontaneous communication between cortical neurons. Biological Cybernetics. 104: 161-74. PMID 21340601 DOI: 10.1007/S00422-011-0425-Y |
0.811 |
|
| 2008 |
Nagarajan R, Szczepanski J, Wajnryb E. Interpreting non-random signatures in biomedical signals with Lempel–Ziv complexity Physica D: Nonlinear Phenomena. 237: 359-364. DOI: 10.1016/J.Physd.2007.09.007 |
0.61 |
|
| 2004 |
Szczepanski J, Wajnryb E, Amigo JM, Sanchez-Vives MV, Slater M. Biometric random number generators Computers and Security. 23: 77-84. DOI: 10.1016/S0167-4048(04)00064-1 |
0.74 |
|
| 2004 |
Szczepański J, Amigó JM, Wajnryb E, Sanchez-Vives MV. Characterizing spike trains with Lempel-Ziv complexity Neurocomputing. 58: 79-84. DOI: 10.1016/J.Neucom.2004.01.026 |
0.596 |
|
| 1995 |
Szczepański J, Wajnryb E. Do ergodic or chaotic properties of the reflection law imply ergodicity or chaotic behavior of a particle's motion? Chaos, Solitons & Fractals. 5: 77-89. DOI: 10.1016/0960-0779(95)91375-8 |
0.585 |
|
| 1991 |
Szczepański J, Wajnryb E. Long-time behavior of the one-particle distribution function for the Knudsen gas in a convex domain Physical Review A. 44: 3615-3621. PMID 9906378 DOI: 10.1103/Physreva.44.3615 |
0.565 |
|
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