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The problem of deciphering how low-level patterns (action potentials in thebrain, amino acids in a protein, etc.) drive high-level biological features(sensorimotor behavior, enzymatic function) represents the central challenge ofquantitative biology. The lack of general methods for doing so from the size ofdatasets that can be collected experimentally severely limits our understandingof the biological world. For example, in neuroscience, some sensory and motorcodes have been shown to consist of precisely timed multi-spike patterns.However, the combinatorial complexity of such pattern codes have precludeddevelopment of methods for their comprehensive analysis. Thus, just as it ishard to predict a protein's function based on its sequence, we still do notunderstand how to accurately predict an organism's behavior based on neuralactivity. Here we derive a method for solving this class of problems. Wedemonstrate its utility in an application to neural data, detecting preciselytimed spike patterns that code for specific motor behaviors in a songbird vocalsystem. Our method detects such codewords with an arbitrary number of spikes,does so from small data sets, and accounts for dependencies in occurrences ofcodewords. Detecting such dictionaries of important spike patterns --- ratherthan merely identifying the timescale on which such patterns exist, as in someprior approaches --- opens the door for understanding fine motor control andthe neural bases of sensorimotor learning in animals. For example, for thefirst time, we identify differences in encoding motor exploration versustypical behavior. Crucially, our method can be used not only for analysis ofneural systems, but also for understanding the structure of correlations inother biological and nonbiological datasets.

Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds