Volume 85, Number 3, February 2009
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||13 February 2009|
Signal propagation through feedforward neuronal networks with different operational modes
Nanjing National Laboratory of Microstructures and Department of Physics, Nanjing University Nanjing 210093, China
Corresponding author: firstname.lastname@example.org
Accepted: 19 January 2009
How neuronal activity is propagated across multiple layers of neurons is a fundamental issue in neuroscience. Using numerical simulations, we explored how the operational mode of neurons —coincidence detector or temporal integrator— could affect the propagation of rate signals through a 10-layer feedforward network with sparse connectivity. Our study was based on two kinds of neuron models. The Hodgkin-Huxley (HH) neuron can function as a coincidence detector, while the leaky integrate-and-fire (LIF) neuron can act as a temporal integrator. When white noise is afferent to the input layer, rate signals can be stably propagated through both networks, while neurons in deeper layers fire synchronously in the absence of background noise; but the underlying mechanism for the development of synchrony is different. When an aperiodic signal is presented, the network of HH neurons can represent the temporal structure of the signal in firing rate. Meanwhile, synchrony is well developed and is resistant to background noise. In contrast, rate signals are somewhat distorted during the propagation through the network of LIF neurons, and only weak synchrony occurs in deeper layers. That is, coincidence detectors have a performance advantage over temporal integrators in propagating rate signals. Therefore, given weak synaptic conductance and sparse connectivity between layers in both networks, synchrony does greatly subserve the propagation of rate signals with fidelity, and coincidence detection could be of considerable functional significance in cortical processing.
PACS: 87.18.Sn – Neural networks and synaptic communication / 05.45.Xt – Synchronization; coupled oscillators / 87.19.L- – Neuroscience
© EPLA, 2009
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