jovo
commented
9 years ago weights. recurrent networks are all about setting the weights properly.
On Tuesday, February 3, 2015, whock notifications@github.com wrote:
In the neuroscience literature, the idea of recurrent networks are important as that architecture is thought to have important properties. One example is being able to instantiate content-addressable memory (Hopfield 1982, Hopfield and Tank, 1986) which is thought to occur in the recurrent CA3 region of the hippocampus. But when they talk of these nets I usually don't see them using the term all-to-all despite them having apparently the same connectivity matrix.
My question is that it seems like recurrent networks and all-to-all networks have the same connectivity matrix, is this right? Or is there a difference I'm missing? And if they're the same why does the neurosci literature talk a lot about recurrent nets and not all-to-all nets if they're same wiring?
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whock
In the neuroscience literature, the idea of recurrent networks are important as that architecture is thought to have important properties. One example is being able to instantiate content-addressable memory (Hopfield 1982, Hopfield and Tank, 1986) which is thought to occur in the recurrent CA3 region of the hippocampus. But when they talk of these nets I usually don't see them using the term all-to-all despite them having apparently the same connectivity matrix.
My question is that it seems like recurrent networks and all-to-all networks have the same connectivity matrix, is this right? Or is there a difference I'm missing? And if they're the same why does the neurosci literature talk a lot about recurrent nets and not all-to-all nets if they're same wiring?