  Recent experiments revealed that a certain class of inhibitory neurons in the
cerebral cortex make synapses not onto cell bodies but at distal parts of
dendrites of the target neurons, mediating highly nonlinear dendritic
inhibition. We propose a novel form of competitive neural network model that
realizes such dendritic inhibition. Contrary to the conventional lateral
inhibition in neural networks, our dendritic inhibition models don't always
show winner-take-all behaviors; instead, they converge to "I don't know" states
when unknown input patterns are presented. We derive reduced two-dimensional
dynamics for the network, showing that a drastic shift of the fixed point from
a winner-take-all state to an "I don't know" state occurs in accordance with
the increase in noise added to the stored patterns. By preventing
misrecognition in such a way, dendritic inhibition networks achieve fine
pattern discrimination, which could be one of the basic computations by
inhibitory connected recurrent neural networks in the brain.
