No subject

> One of the fundamental beliefs in neuroscience, cognitive science
> and artificial neural networks is that the brain learns in
> real-time. That is, it learns instantaneously from each and every
> learning example provided to it by adjusting the synaptic 
>strengths or connection weights in a network of neurons.  The 
>learning is generally thought to be accomplished using a 
>Hebbian-style mechanism or some other variation of the idea (a 
>local learning law). In these scientific fields, real-time 
>learning also implies memoryless learning. 

That is a non sequitur.

Why should real-time learning imply that there is not also
memory  based learning?

Both types of 'learning' are surely desirable for higher
cognitive behaviour in a real-time environment.

By 'learning', I just mean 'connection weight adjustment'.

In memory-based learning in our brains, it would be
impossible to store all parameters of an event or training
sample as they occur - but obviously we store some
transformed, compacted version of events,(if we didn't, 
then we would not have long term memories) and equally
obviously, this is available for reference for learning at 
a later time (if not, then we would be unable to learn from
our long-term memories).

>In memoryless learning, no training examples are stored explicitly 
>in the memory of the learning system, such as

what does explicitly mean in this context?

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