Shape your liquid with plasticity
Andreea Lazăr, Gordon Pipa, Jochen Triesch · 2007
Cortical networks involved in coding and processing of information are constantly shaped by a large variety of plasticity mechanisms. But how do different forms of plasticity interact to shape the structure, dynamics and computational properties of recurrent spiking networks? We use a simple recurrent spiking neural network made of threshold units that allows us to look at the network structure and dynamics in a detailed fashion. Two forms of neuronal plasticity are considered: spike timing dependent plasticity (STDP) that changes synaptic strength and intrinsic plasticity (IP) that changes the excitability of individual neurons to maintain homeostasis of their activity. In analogy to liquid state machines[1] we analyze the ability of such networks to exhibit a fading memory of external inputs and study the extent in which they may discover structure in non-random, predictable time series. We find that STDP and IP interact in non-trivial ways such that the effect of one of them on network behavior can be substantially altered by the presence of the other (also see [2,3]). Specifically, autonomous networks without input shaped by a combination of STDP and IP lead to many limit cycles with stable network behavior in the presence of small perturbations. When we study input driven networks, the causal nature of the STDP rule allows the reservoir to learn structure in the time sequences corresponding to likely sequences of external inputs. The intrinsic plasticity enforces balanced dynamics that utilizes all resources in the network. Together, these mechanisms allow recall (t 0) with a performance that was similar to that of randomly structured reservoirs, while networks trained with just STDP or IP separately perform on average significantly worse (see figure). These differences may be explained by our finding that the two forms of plasticity keep the dynamics of the network at the edge of chaos. Our results underscore the importance of studying the interaction of different forms of plasticity on network behaviour.