A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity

Activity-dependent synaptic plasticity is a fundamental learning mechanism that shapes connectivity and activity of neural circuits. Existing computational models of Spike-Time-Dependent Plasticity (STDP) model long-term synaptic changes with varying degree of biological details. A common approach is to neglect the influence of short-term dynamics on long-term plasticity, which may represent an oversimplification for certain neuron types. Thus, there is a need for new models to investigate how short-term dynamics influence long-term plasticity. To this end, we introduce a novel phenomenological model, the Short-Long-Term STDP (SL-STDP) rule, which directly integrates short-term dynamics with postsynaptic long-term plasticity. We fit the new model to layer 5 visual cortex recordings and study how the short-term plasticity affects the firing rate frequency dependence of long-term plasticity in a single synapse. Our analysis reveals that the pre- and postsynaptic frequency dependence of the long-term plasticity plays a crucial role in shaping the self-organization of recurrent neural networks (RNNs) and their information processing through the emergence of sinks and source nodes. We applied the SL-STDP rule to RNNs and found that the neurons of SL-STDP network self-organized into distinct firing rate clusters, stabilizing the dynamics and preventing connection weights from exploding. We extended the experimentation by including homeostatic balancing, namely weight normalization and excitatory-to-inhibitory plasticity and found differences in degree correlations between the SL-STDP network and a network without the direct coupling between short-term and long-term plasticity. Finally, we evaluated how the modified connectivity affects networks’ information capacities in reservoir computing tasks. The SL-STDP rule outperformed the uncoupled system in majority of the tasks and including excitatory-to-inhibitory facilitating synapses further improved information capacities. Our study demonstrates that short-term dynamics-induced changes in the frequency dependence of long-term plasticity play a pivotal role in shaping network dynamics and link synaptic mechanisms to information processing in RNNs.