While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions. This ability is particularly important for applications in cognitive neuroscience and neuroinformatics .
<clarity> While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions. This ability is particularly important for applications in cognitive neuroscience and neuroinformatics .
While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used by the DNN in the process, something important in both cognitive neuroscience and neuroinformatics .