In this paper, a hybrid model of cellular neural network based on heterogeneous spiking neurons connected with a spatial model of terrain through hierarchically ordered context is proposed. Spatial cells are considered as elements of the neural network (neurons) and events are considered as spikes at the neuron output. A discrete automaton model with integrated likelihood model supplements a hybrid spiking neuron model to determine the neuron state at specified time points based on probability or possibility of state transitions. The structure of neuron connec-tions in the model resembles a cellular network model. The hierarchical context containing a set of transmitters allows organizing additional channels of commu-nication between neurons and provide remote sensing data to the neural network. Neurons have controlled sensitivity to transmitters with special receptors con-nected to the network context. The method of modeling spatial distributed de-structive processes using the proposed hybrid neural networks is presented. The proposed method and models is intended for modeling dynamic systems with dif-ferent types of simultaneously arising interacting processes with respect of their spatiotemporal aspects.