Abstract A model for visual adaptation to spatial grating is developed based on the assumption that inhibitory synapses within the visual system may be temporarily modified as a function of recent usage. Specifically, it is hypothesized that inhibitory synaptic weights are altered as a function of the correlation between recent presynaptic and postsynaptic activity. When such modifiable synapses are incorporated into a simple neural network model having the spatial filtering properties of the human visual system, two coupled equations are obtained which may be solved analytically. The model accounts for experimental data on adaptation to sinusoidal gratings, square wave gratings, single bars, and tilted gratings. The relationship of the model to single and multiple channel models of the human visual system is discussed.