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Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons
Maria Masoliver1, Cristina Masoller2
1Departament de Fisica, Universitat Politecnica de Catalunya, Rambla de Sant Nebridi 22, 08222, Terrassa, Barcelona, Spain.
Scientific Reports
|May 31, 2018
Summary
Researchers found that even a single neuron can encode weak signals using specific spike patterns. A second neuron, even without sensing the signal, can influence this encoding process, offering insights into neural information processing.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Theoretical Neuroscience
Background:
- Understanding the neural code, how neurons process information, remains a significant challenge in neuroscience.
- Previous studies explored single neuron responses to weak signals, focusing on spike timing patterns.
Purpose of the Study:
- To investigate how a second neuron influences signal encoding by a first neuron, particularly when only the first neuron detects the signal.
- To determine if symbolic spike patterns can encode information about a sub-threshold signal in a two-neuron system.
Main Methods:
- Simulated the stochastic FitzHugh-Nagumo (FHN) model for two coupled neurons.
- Analyzed neuronal firing activity using a symbolic method to identify preferred and infrequent spike patterns.
- Quantified information encoding based on the probabilities of these spike patterns.
Main Results:
- Confirmed that a single neuron can encode information about a sub-threshold periodic signal through preferred and infrequent spike patterns.
- Demonstrated that the presence of a second neuron, even one not directly sensing the signal, affects the encoding process.
- Showed that spike trains, despite appearing random, contain encoded signal information.
Conclusions:
- Encoding of weak signals via symbolic spike patterns is a plausible mechanism in neuronal systems.
- This mechanism is relevant for sensory systems involving multiple noisy neurons where only one detects an external input.
- Findings contribute to understanding information processing in simple neuronal circuits.
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