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An input of size γ c is sufficient to push the voltage across the threshold for single spike initiation.
The average case for an input of size n is defined by the expected value or average over all inputs of size n.
The best case and the worst case for an input of size n are defined by the minimum and the maximum running time of computing over all inputs of the size n, respectively.
The LIF model was also considered by Börgers and Kopell, who proved (1) if a collection of phase-shifted, identical time-periodic inputs cause a spike in an LIF neuron, then the same inputs will also cause a spike if they are synchronized, and (2) if a constant input of size A causes a spike, then any time-periodic input with time average A will also cause a spike [13].
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Notice that for Divide and Conquer algorithms with running time satisfying the recurrence equation (1), it is typically sufficient to obtain the complexity on inputs of size n, where n ranges over the set N b [45, 46].
The worst case space complexity of \(M\) – denoted \(s_{M}(n)\) – is defined similarly – i.e. the maximum number of tape cells (or other form of memory locations) visited or written to in the course of \(M\)'s computation for all inputs of size \(n\).
For every algorithm (mathcal {A}) computing some partial function (phi :Srightarrow {mathbb {N}}), by (T_{mathcal A}) we denote the time function of (mathcal {A}) which is defined as the maximal running time of (mathcal A) on inputs of size at most n from the domain of (phi ) (thus we simply ignore inputs not from the domain of (phi )).
where a, b ∈ N with a, b > 1, N b = { b k : k ∈ N }, c > 0 and h ∈ C with h ( n ) < ∞ for all n ∈ N. Observe that for Divide and Conquer algorithms, it is sufficient to obtain the complexityon inputs of size n with n ranges over the set N b (for a fuller treatment we refer the reader to [5]).
So we only need to show that contains at least points in each input disk of size at least.
We are considering input disks of size at least, and this implies that there are at least points in each disk.
For experiments using the Lyon cochleagram, the optimal configuration for the network topology had an input layer of size 64, a hidden layer containing 350 memory blocks (with one cell in each block), and an output layer of size 5.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com