AI Article Synopsis
- The study investigates how a recurrent neuronal network detects weak signals with adaptive synapses.
- It finds that the network becomes selective to the frequency of weak periodic stimuli and that synaptic depression helps optimize the response to nonperiodic signals.
- Additionally, introducing correlations in the signal structure decreases the information transfer between input and output, indicating that the way signals are processed in neural circuits depends on their content rather than just transmission.
Article Abstract
We study the characteristics of weak signal detection by a recurrent neuronal network with plastic synaptic coupling. It is shown that in the presence of an asynchronous component in synaptic transmission, the network acquires selectivity with respect to the frequency of weak periodic stimuli. For nonperiodic frequency-modulated stimuli, the response is quantified by the mutual information between input (signal) and output (network's activity) and is optimized by synaptic depression. Introducing correlations in signal structure resulted in the decrease in input-output mutual information. Our results suggest that in neural systems with plastic connectivity, information is not merely carried passively by the signal; rather, the information content of the signal itself might determine the mode of its processing by a local neuronal circuit.
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| http://dx.doi.org/10.1063/1.3184806 | DOI Listing |
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