arXiv (Cornell University) · 2015 · 30 citations · 12 references
Life SciencesNeuroscienceComputational NeuroscienceSynaptic PlasticitySystems NeuroscienceActivity-dependent PlasticityNeural PlasticityNeuronal DynamicsSpike-timing-dependent PlasticitySpiking Neural NetworksSynaptic TransmissionGradient DescentNeural DynamicsNeural Network DynamicsSpike-timing PlasticityStochastic Gradient DescentStochastic Neural DynamicsSynaptic Weight DynamicsTiming-dependent Synaptic Plasticity
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.
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