To which extent can successful machine learning inform our understanding of biological learning? One popular avenue of inquiry in recent years has been to directly map such algorithms into a realistic circuit implementation. Here we focus on learning in recurrent networks and investigate a range of learning algorithms. Our approach decomposes them into their computational building blocks and discusses their abstract potential as biological operations. This alternative strategy provides a “lazy” but principled way of evaluating ML ideas in terms of their biological plausibility It is still unclear how neural circuits achieve sophisticated learning, in particular solving temporal credit assignment. Here we approached the problem by looking for biologically sensible approximations to RTRL and BPTT. Although we have empirical results to prove that our solutions can solve temporal credit assignment for simple tasks, the substance of our contribution is conceptual, in articulating what computations are abstractly feasible and which are not. In particular, we have shown that accessing the Jacobian for learning is possible by using a set of synapses trained to linearly approximate the network's own dynamics. Along the way, we have identified some key lessons. The main one is that neural circuits need additional infrastructure specifically to support learning. This could be extra neurons, extra compartments within neurons, separate coordinated phases of computation, input gating by inhibition, etc. While we all know that biology is a lot more complicated than traditional models of circuit learning would suggest, it has proved difficult to identify the functional role of these details in a bottom-up way. On the other hand, drawing a link between ML algorithms and biology can hint at precise computational roles for not well understood circuit features. Another lesson is that implementing even fairly simple learning equations in parallel to the forward pass is nontrivial, since it already uses up so much neural hardware. Even a simple matrix-vector product requires an entirely separate phase of network dynamics in order to not interfere with the forward pass of computation. While it may be tempting to outsource some of these update equations to separate neurons, the results would not be locally available to drive synaptic plasticity. Of course, we acknowledge that any particular solution, whether RFLO or DNI, is a highly contrived, specific, and likely incorrect guess at how neural circuits learn, but we believe the exercise has big-picture implications for how to think about biological learning. Beyond the particular topic of online learning in recurrent networks, our work provides a general blueprint for abstractly evaluating computational models as mechanistic explanations for biological neural networks. Knowing what computational building blocks are at our disposal and what biological details are needed to implement them is an important foundation for studying ML algorithms in a biological context.
We evaluate new ML learning algorithms' biological plausibility in the abstract based on mathematical operations needed
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