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In general scenarios, we conjecture that the same effect holds as long as the network is kept fully loaded, as shown by numerical examples.
While several studies indicate that nonlinear methods can extract valuable information from neuronal dynamics, others doubt their necessity and conjecture that the same information can be obtained using classical linear techniques.
Although this remains to be checked in detail, we conjecture that the same holds true for the case of a massive particle in an external potential, because in this case the Dirac equation is still linear.
Thus, we conjecture that the same implications could be applicable to other countries that have or are developing a system of universal coverage to all the people resided in each country.
Since the transfer of PDE techniques to neural fields has been very successful, either directly [51] or indirectly [14, 21], one may conjecture that the same strategy also works for SPDEs and stochastic neural fields.
We conjecture that the same patterns will occur along all types of stress gradients: the harsher the conditions, the more colonizers will be limited to gap edges.
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Singer and Wermer [20] proved that if is a commutative Banach algebra and is a continuous linear derivation, then They also conjectured that the same result holds even is a discontinuous linear derivation.
On the basis of the commonalities between the functional architectures of reward and novelty, we conjectured that the same two forms of neural adaptation seen for reward may also characterize novelty.
He also conjectured in [1] that the same result holds for n = 3 and 4.
Similar to (4.7), we state the following conjecture that would also follow from Petty's conjectured projection inequality (see [75]).
Analogous remarks apply to 'I conjecture that' and the like.
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