Using numerical simulations, it is shown that a random, athermal pack of soft frictional grains will store an arbitrary waveform that is applied as a small time-dependent shear while the system is slowly compressed. When the system is decompressed at a later time, an approximation of the input waveform is recalled in time-reversed order as shear stresses on the system boundaries. It is shown that this effect depends on friction between the grains, and is independent of some aspects of the friction model. By systematically increasing the complexity of the stored waveform, it is found that a pack of ${10}^4$ grains can recall any one of 128 different waveforms with 100% classification accuracy and 512 different waveforms with over 90% classification accuracy, as measured by a neural net trained only on the inputs. This type of waveform memory might be observable in other types of athermal random media that form internal contacts when compressed, such as crumpled sheets and nestlike fiber assemblies.

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• Collective behavior
• Internal friction
• Rheology
• Granular materials
• Molecular dynamics