Neural coding of space by time.
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| Title: | Neural coding of space by time. |
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| Authors: | Löffler, Hubert1 (AUTHOR) loeffler.hubert@outlook.com, Gupta, Daya Shankar2 (AUTHOR), Bahmer, Andreas3 (AUTHOR) |
| Source: | Biological Cybernetics. Aug2024, Vol. 118 Issue 3/4, p215-227. 13p. |
| Subjects: | Space-time codes, Phase coding, Membrane potential, Oscillations, Physics |
| Abstract: | The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 03401200 |
| DOI: | 10.1007/s00422-024-00992-1 |
