POSTER PRESENTATION Open Access On the influence of inhibitory STDP on balanced state random networks

The distribution of synaptic efficacies in neural networks takes fundamental influence on their dynamics and the modification of synaptic strengths forms the foundation of learning and memory. A prominent plasticity rule that has been observed in vitro is spike-timing-depen-dent plasticity (STDP). While first studied in glutama-tergic synapses, recently also STDP of GABAergic synapses came into the focus of experimental and theo-retical research [1]. We study random balanced state networks of leaky integrate-and-fire neurons in the asynchronous irregular (AI) regime [2] that is believed to be a good theoretical fit to the activity of cortical networks in vivo. We con-sider driven networks that receive Poisson input as well as networks in a self-sustained state of activity. In order to assess the influence of excitatory and inhibitory STDP on the network dynamics, we introduce these two plasticity rules independently, observing network dynamics and weight distributions after a transient phase. Note that both additive and multiplicative STDP rules yield the same network dynamics as described below. When introducing excitatory STDP alone, parameters involving the maximal weight have to be fine-tuned in order to keep the network activity stably in the AI regime [3]. For almost all parameter values the network activity becomes unstable, leaving the AI regime and settling in a pathological, highly synchronized state with saturated firing rates of most cells, see Figure 1A. We also observed that even without STDP, few strong exci-tatory connections can substantially destabilize network dynamics yielding pathological states. Interestingly, thi