Automated Author ProfileGepshtein, Sergei
Gepshtein, Sergei
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Supplementary files for article "Artificial transneurons emulate neuronal activity in different areas of brain cortex"
Rapid development of memristive elements emulating biological neurons creates new opportunities for brain-like computation at low energy consumption. A first step toward mimicking complex neural computations is the analysis of single neurons and their characteristics. Here we measure and model spiking activity in artificial neurons built using diffusive memristors. We compare activity of these artificial neurons with the spiking activity of biological neurons measured in sensory, pre-motor, and motor cortical areas of the monkey (male) brain. We find that artificial neurons can operate in diverse self-sustained and noise-induced spiking regimes that correspond to the activity of different types of cortical neurons with distinct functions. We demonstrate that artificial neurons can function as trans-functional devices (transneurons) that reconfigure their behaviour to attain instantaneous computational needs, each capable of emulating several biological neurons.
© The Author(s), CC BY 4.0
Authors
- Midya, Rivu ;
- Pawar, Ambarish S. ;
- P. Pattanaik, Debi ;
- Mooshagian, Eric ;
- Borisov, Pavel ;
- D. Albright, Thomas ;
- H. Snyder, Lawrence ;
- Williams, R. Stanley ;
- Joshua Yang, J. ;
- Balanov, Alexander ;
- Gepshtein, Sergei ;
- Saveliev, Sergey
Supplementary files for article "Artificial transneurons emulate neuronal activity in different areas of brain cortex"
Rapid development of memristive elements emulating biological neurons creates new opportunities for brain-like computation at low energy consumption. A first step toward mimicking complex neural computations is the analysis of single neurons and their characteristics. Here we measure and model spiking activity in artificial neurons built using diffusive memristors. We compare activity of these artificial neurons with the spiking activity of biological neurons measured in sensory, pre-motor, and motor cortical areas of the monkey (male) brain. We find that artificial neurons can operate in diverse self-sustained and noise-induced spiking regimes that correspond to the activity of different types of cortical neurons with distinct functions. We demonstrate that artificial neurons can function as trans-functional devices (transneurons) that reconfigure their behaviour to attain instantaneous computational needs, each capable of emulating several biological neurons.
© The Author(s), CC BY 4.0
Authors
- Midya, Rivu ;
- Pawar, Ambarish S. ;
- P. Pattanaik, Debi ;
- Mooshagian, Eric ;
- Borisov, Pavel ;
- D. Albright, Thomas ;
- H. Snyder, Lawrence ;
- Williams, R. Stanley ;
- Joshua Yang, J. ;
- Balanov, Alexander ;
- Gepshtein, Sergei ;
- Saveliev, Sergey