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In experimental designs of animal models, memory is often assessed by the time for a performance measure to occur (latency).
In unsupervised distributed models, memory systems extract information from inputs, becoming attuned, in context-dependent fashion, to what the environment affords.
Several new issues in our architecture, including scalable cache coherence protocols, relaxed memory consistency models, memory optimization techniques and several types of processors are considered.
We demonstrate that for both object manipulation and force-field adaptation, contrary to previous models, memory decay is highly context dependent.
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According to this model, memory performance is based on associative and strategic components.
Moreover, some examinations including the Volterra model memory size and algorithm initial condition are further considered.
Modelling memory and especially how can memory be manipulated and how the memory influences choices and thus transitions between states is not easy.
where x(n), y(n), and z(n) denote the input signal of the memoryless nonlinear PA model, memory linear PA and Hammerstein, respectively.
In our model, memory cells are maintained until newly generated memory cells replace them.
In the first model, memory decay was assumed to be context independent, consistent with previous studies (Equation 1).
The proposed methodology is based on the fast instruction analysis using instruction level power models, cache memory and memory power models.
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