STDP as presynaptic activity times rate of change of postsynaptic activity
This provides a theoretical explanation for synaptic changes from a machine learning perspective, potentially linking neural dynamics to optimization, but it is incremental as it builds on existing STDP frameworks.
The authors tackled the problem of deriving a synaptic weight update rule consistent with spike-timing dependent plasticity (STDP) without needing explicit spike timing, resulting in a formula based on presynaptic firing rates and postsynaptic activity derivatives that aligns with biological observations and can be interpreted as stochastic gradient descent.
We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed. The new rule changes a synaptic weight in proportion to the product of the presynaptic firing rate and the temporal rate of change of activity on the postsynaptic side. These quantities are interesting for studying theoretical explanation for synaptic changes from a machine learning perspective. In particular, if neural dynamics moved neural activity towards reducing some objective function, then this STDP rule would correspond to stochastic gradient descent on that objective function.