Representation and reasoning about strategic abilities has been an active research area in AI and multi-agent systems. Many variations and extensions of alternating-time temporal logic ATL have been proposed. However, most of the logical frameworks ignore the issue of coordination within a coalition, and are unable to specify the internal structure of strategies. In this paper, we propose JAADL, a modal logic for joint abilities under strategy commitments, which is an extension of ATL. Firstly, we introduce an operator of elimination of (strictly) dominated strategies, with which we can represent joint abilities of coalitions. Secondly, our logic is based on linear dynamic logic (LDL), an extension of linear temporal logic (LTL), so that we can use regular expressions to represent commitments to structured strategies. We analyze valid formulas in JAADL, give sufficient/necessary conditions for joint abilities, and show that model checking memoryless JAADL is in EXPTIME.

Neural information coding is helpful in understanding the working mechanism of the nervous system. Currently, most of the studies are based on the neural network which is based on excitatory synaptic plasticity. However, the inhibitory synaptic plasticity also plays an important role in the regulation of neural network. For presenting better biological authenticity, a spiking neural network was constructed based on the synaptic plasticity regulation mechanism in this study. The synaptic plasticity regulation mechanism contains excitatory and inhibitory synapses. The characteristics of neural information coding under AC electric field stimulation were studied from the perspective of time coding (inter-spike interval coding) and rate coding (average rate coding). The experimental results indicate that inter-spike intervals decrease and the firing rate of neurons increases under AC electric field stimulation. With the increase of the stimulation intensity, inter-spike intervals are decreased and the firing rate of neurons is increased. The neurons whose average firing rate increases can be raised as a neuron cluster to express the information. The results of this paper help us to understand the mechanism of information processing of the brain, and bring new ideas to the engineering applications such as neural computation and artificial intelligence.