Overall, XDream is an efficient, general, and robust algorithm for uncovering neuronal tuning preferences using a vast and diverse stimulus space. These results establish expectations and provide practical recommendations for using XDream to investigate neural coding in biological preparations. Lastly, we found no significant advantage to problem-specific parameter tuning. Furthermore, XDream is robust to choices of multiple image generators, optimization algorithms, and hyperparameters, suggesting that its performance is locally near-optimal.
XDream extrapolates to different layers, architectures, and developmental regimes, performing better than brute-force search, and often better than exhaustive sampling of >1 million images. XDream can efficiently find preferred features for visual units without any prior knowledge about them. We also explored design and parameter choices. We evaluated how the method compares to brute-force search, and how well the method generalizes to different neurons and processing stages. We use ConvNet units as in silico models of neurons, enabling experiments that would be prohibitive with biological neurons. Here we extensively and systematically evaluate the performance of XDream. A new method termed XDream (E Xtending DeepDream with real-time evolution for activation maximization) combined a generative neural network and a genetic algorithm in a closed loop to create strong stimuli for neurons in the macaque visual cortex. The characterization of effective stimuli has traditionally been based on a combination of intuition, insights from previous studies, and luck. A longstanding question in sensory neuroscience is what types of stimuli drive neurons to fire.
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