layer need to be stored.

We discuss related work in section \[sec:related\_work\]. We describe the proposed learning mechanism in section \[sec:model\] and quantitatively assess the hardware-related computational and memory access benefits compared to standard learning with global objective functions in section \[sec:hw\]. We present the results of applying the proposed learning method to standard supervised learning benchmarks in section \[sec:results\] and compare our learning method’s performance to that of the feedback alignment technique [@Lillicrap_etal16] . We present our conclusions and further discussion on the biological plausibility of the proposed learning mechanism in section \[sec:conclusions\_discussion\].

Related Work {#sec:related_work}
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Training of deep convolutional networks is currently dominated by approaches where all weights are simultaneously trained to minimize a global objective. This is typically done in a purely supervised setting where the training objective is the classification loss at the top layer. To ameliorate the problem of exploding/vanishing errors in deep layers [@Hochreiter_etal01], auxiliary classifiers are sometimes added to provide additional error information to deep layers [@Szegedy_etal14; @Lee_etal15]. Unlike our training approach, however, training still involves backpropagating errors across the entire network and simultaneous adjustments of all weights. Several learning mechanisms have been traditionally used to pre-train a deep network layer-by-layer using 