Cognitive models developed in psychology are redefined as mechanisms in artificial intelligence (AI). Brain commands are abstracted in AI as if they were discrete in time and space. To acquire meaning and feel emotions, AI controllers must command a robot. Their symbolic neural networks (SNNs) accumulate invariant temporal rules associating a 'situation' and a 'result' with the 'action' performed. These rules are AI programs learned by experience. An inference engine embodied in these SNNs finds which 'action' produces a given 'intention'. The intentional repetition of brief movements does not require inverse dynamics. Categorization prevents combinatorial explosion. This paper describes a neuroanatomically plausible large-scale architecture integrating SNNs with other NNs. All-or-none irreversible storage is compatible with adaptive learning in this hybrid system. Biological mappings are suggested. Neuromodulators change the processing mode of whole SNNs to enable decisions, freeze states, chain procedure steps and learn temporal rules. Logical impulses are bursts generated by dendritic calcium transients. Synapses transformed are stabilized by self-regulations maintaining multi-stationary states. 'Winner-take-all' sparse coding preserves memory by storing no more than one rule condition per all-or-none synapse.