![]() Specifically, we use convolutional neural networks (ConvNets) pre-trained on visual recognition tasks as an approximation to the computations performed along ventral visual cortex. To overcome these challenges, here we test the performance of XDream using state-of-the-art in silico models of visual neurons in lieu of real neurons, in the same spirit of. The performance and design options of XDream have not been thoroughly evaluated, due to the time-intensiveness of neuronal recordings and the difficulty to fully control experimental variables. XDream can generate strong stimuli for neurons in macaque inferior temporal (IT) and primary visual cortex (V1). Named XDream (e Xtending DeepDream with real-time evolution for activation maximization), this method combines a genetic algorithm and a deep generative neural network -both inspired by previous work -to evolve images that trigger high activation in neurons. Such images could lead us to revisit our current descriptions of feature tuning in visual cortex.Ī recently introduced method shows promise to begin bridging the gap. In other words, there could be other images that drive visual neurons better than those found so far. ĭespite the progress made in understanding visual cortex by testing limited sets of hand-chosen stimuli, these experiments could be missing the true feature preferences of neurons. Stimuli selected in this way underlie our current understandings of how circular center-surround receptive fields give rise to orientation tuning, then to encoding of more complex shapes such as curvatures, and further to selective responses to complex objects such as faces. Instead, investigators have traditionally selected stimuli guided by natural image statistics, behavioral relevance, theoretical postulates about internal representations, intuitions from previous studies, and serendipitous findings. It is practically impossible to exhaustively evaluate neuronal responses to images, due to the combinatorially large number of possible images. What stimuli excite a neuron, and how can we find them? Consider vision as a paradigmatic example, the selection of stimuli to probe neural activity has shaped the understanding of how visual neurons represent information. XDream is implemented in Python, released under the MIT License, and works on Linux, Windows, and MacOS. 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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