The spike triggered averaged (STA) technique has been widely used to estimate the receptive fields (RF) of sensory neurons (Chichilnisky, 2001). Theoretically, it has been shown that when the neurons are stimulated with a white noise stimulus the STA is an unbiased estimator of the neuron RF (up to a mul- tiplicative constant). The error decreases with the number of spikes at a rate proportional to the stimulus variance (Paninski, 2003). Experimentally, for visual neurons, the standard stimuli are checkerboards where block size is heuristically tuned. This raises difficulties when dealing with large neurons assemblies: When the block size is too small, neuron’s response might be too weak, and when it is too large, one may miss RFs. Previously online updating the stimulus in the direction of larger stimulus-neural response corre- lation (Foldiak, 2001) or mutual information (Machens, 2002; MacKay, 1992) has been proposed. However, these approaches can not be applied for an ensemble of cells recorded simultaneously since each neuron would update the stimulus in a different direction. We propose an improved checkerboard stimulus where blocks are shifted randomly in space at fixed time steps. Theoretically, we show that the STA remains an unbiased estimator of the RF. Additionally, we show two major properties of this new stimulus: (i) For a fixed block size, RF spatial resolution is improved as a function of the number of possible shifts; (ii) Targeting a given RF spatial resolution, our method converges faster than the standard one. Numerically, we perform an exhaustive analysis of the performance of the approach based on simulated spiked trains from LNP cascades neurons varying RF sizes and positions. Results show global improvements in the RF representation even after short stimulation times. This makes this approach a promising solution to improve RF estimation of large ensemble of neurons.