  To learn useful dynamics on long time scales, neurons must use plasticity
rules that account for long-term, circuit-wide effects of synaptic changes. In
other words, neural circuits must solve a credit assignment problem to
appropriately assign responsibility for global network behavior to individual
circuit components. Furthermore, biological constraints demand that plasticity
rules are spatially and temporally local; that is, synaptic changes can depend
only on variables accessible to the pre- and postsynaptic neurons. While
artificial intelligence offers a computational solution for credit assignment,
namely backpropagation through time (BPTT), this solution is wildly
biologically implausible. It requires both nonlocal computations and unlimited
memory capacity, as any synaptic change is a complicated function of the entire
history of network activity. Similar nonlocality issues plague other approaches
such as FORCE (Sussillo et al. 2009). Overall, we are still missing a model for
learning in recurrent circuits that both works computationally and uses only
local updates. Leveraging recent advances in machine learning on approximating
gradients for BPTT, we derive biologically plausible plasticity rules that
enable recurrent networks to accurately learn long-term dependencies in
sequential data. The solution takes the form of neurons with segregated voltage
compartments, with several synaptic sub-populations that have different
functional properties. The network operates in distinct phases during which
each synaptic sub-population is updated by its own local plasticity rule. Our
results provide new insights into the potential roles of segregated dendritic
compartments, branch-specific inhibition, and global circuit phases in
learning.
