Cortical neural networks in vivo are able to retain their activity evoked by a short stimulus. We compare and analyze two mathematical models of ring-structured networks of an orientational hypercolumn of the visual cortex in order to distinguish the contributions of recurrent connections, synaptic depression and slow synaptic kinetics into the retention effect. Comparison of a more elaborated model with the classical ring-model has helped to translate the mathematical analysis of the later model to the former one. As shown, the network with developed recurrent connections reproduces the retention effect compared to that in experiments. The synaptic depression prevents the effect, however the long-lasting excitatory synaptic current recovers the property. Accounting of the slow kinetics of the NMDA receptors, a characteristic postpeak plateau of activity is reproduced. The models show an invariance to contrast of visual stimuli. Simulations reveal a major role of strong excitatory recurrent connections in the retention effect.

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