- Poster presentation
- Open Access
Optimization of weak signal propagation in a feedforward network
© Uzuntarla et al; licensee BioMed Central Ltd. 2011
- Published: 18 July 2011
- Output Layer
- Synaptic Transmission
- Weak Signal
- Gain Factor
- Feedforward Network
The ability of weak signal detection and transduction of neurons is of great importance. Although the subject hotly debated in single neuron and complex networks , it has been partly addressed in feedforward networks [2, 3]. In our previous work , we determined the conditions for weak rhythmic signal propagation through a feedforward network where the neurons are constructed with detailed biophysical modeling approaches. It is shown that the optimal propagation of weak rhythmic signals through feedforward neuronal networks depends significantly on the level of intrinsic noise, the forcing frequency and the inter-layer link density. Here, we used the same network structure in , which involves 10 layers with N=200 neurons in each one and 10% inter-layer link density (Fig1a). For synaptic transmission, alpha-synapse type is used to model the conductance variations in the post-synaptic neuron resulting from the binding of neurotransmitter released from the pre-synaptic neuron. Release time of neurotransmitters τsyn and coupling constant gsyn are critical parameters of this type of synapses. A measure Qi is chosen to quantify the signal transmission efficiency, which gives the existence of the input signal frequency at any layers’ output, similar to Signal to Noise Ratio (SNR). Model details and methods can be found in .
To extend our findings, we first investigated the role of fraction of forced neurons forming the first layer on the propagation of weak rhythmic activity. We consider different fractions of periodically forced neurons (chosen randomly) and compute Q10 (Fourier coefficients of output layer) over a broad frequency range (Fig 1b). Evidently, the larger the fraction of neurons subject to the weak forcing, the better the outreach of the signal through the network. This can be appreciated most clearly for the optimal angular forcing frequency, ω≈0.4ms–1. Interestingly, the depicted curves show only marginal improvement in Q10 if more than 50% of the neurons forming the first layer are forced. This finding suggests that the propagation of weak signals across feedforward neuronal networks is rather robust to variations in the coverage of the initial input.
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