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We stimulated the model neuron with 5 segments of convoluted external noise (with τ = 3  ms ), each 10000 ms long.

We consider the dynamics of an LIF model neuron with excitatory synaptic inputs as governed by the equations v ′ = I − v − g ( v − E ), (2).

The Poisson simulation creates surrogate traces by stimulating a single model neuron with synaptic inputs.

We verified that the behavior was not due to a numerical artifact: Simulating the model neuron with the same parameter set with different numerical integration methods (reducing the forward-Euler time step, using Runge-Kutta or Adams-Bashforth-Moulton) always produced an irregular spiking pattern.

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Another method can be found in [10] where a population density equation has been derived for a population of SRM (spike-response model) neurons with escape noise.

Numerical simulations with more realistic models, i.e. conductance-based model neurons with exponentially decaying synaptic conductances, yield similar results.

Simultaneous representation by colour neurons, complex model neurons (with oriented receptive fields), and hypercomplex model neurons (responding to corners) makes attention and recognition robust and reliable, in the framework of emergent abilities of optimized complex systems [139] [141].

In contrast, model neurons with gain control show longer-tailed distributions (brown and red curves), indicating that the stimulus information is shared by neurons with a wide variety of preferred spatial frequencies.

We sampled the 50 model neurons with different preferred spatial frequencies that are considered in the above, and used the distributions of cell numbers estimated from the experimental data (Fig. 4d).

The parameters have been chosen to model neurons with relatively steep synaptic onset and short duty cycle (i.e. short fraction of the cycle when the cell is depolarised above threshold and may exert synaptic action).

In this model, neurons with fewer neighbors, as often seen dorsally and especially ventrally, target the medulla with higher probability.

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