Abstract
Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy).
Highlights
Variability is a prominent feature of the neuronal activities
The firing rate of individual neurons fluctuate strongly (Figure 6A, lower panels); and the coefficient of variance (CV) is larger than 1 (Figure 6B), reflecting the burstiness of the spike patterns. These features suggest that the network works in chaotic asynchronous states, which may be caused by the unstability of the network dynamics to heterogeneous perturbations (Ostojic, 2014)
In the following two subsections, we will explain in details how we investigate the influence of spike pattern structure onto efficacy variability by implementing a variety of spike shuffling methods onto the spike patterns generated by the leaky integrate-and-fire (LIF) network
Summary
Variability is a prominent feature of the neuronal activities. The neurons in the same population may respond quite differently to the same stimulus (structural variability), and the responses of a neuron to the same stimulus can differ in different trials (trial variability). Trial variability partly comes from biomolecular noises such as the open and close of ion channels and the release of synaptic vesicles (see Faisal et al, 2008 for review). Such noises may enter any stage of information processing in the brain, from perception and decision making to motion generation (Faisal et al, 2008), influencing the reliability and timing of action potentials (Allen and Stevens, 1994; Zador, 1998; Dorval and White, 2005; Faisal and Laughlin, 2007), especially in neurons with thin axons (Faisal et al, 2005). If the dynamics of the network exhibits deterministic chaos, such as theoretically suggested for the networks under excitatoryinhibitory balanced state (van Vreeswijk and Sompolinsky, 1998; Monteforte and Wolf, 2010; Ostojic, 2014), the responsive variability will be exacerbated due to the high sensitivity to noises and initial conditions
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