The likelihood function evaluates how well different parameter values account for the measurements under a specified probability model. Maximum Likelihood compares these candidate values and selects the parameter set associated with the greatest likelihood. This makes the estimate dependent on both the observed data and the model used to describe how those data could arise.
Optimizing the logarithm preserves the parameter values that maximize the original likelihood while providing a more convenient objective for estimation. The transformation allows researchers to work with a log-likelihood rather than the raw likelihood when identifying the best-fitting parameters. In practice, this supports the optimization step without changing which parameter set is preferred.
A Maximum Likelihood estimate is meaningful only relative to the specified probability model. That model determines how the observed measurements are evaluated, while its unknown parameters control the hypotheses being fitted to the data. Consequently, changing the model or the parameterization can change the selected values and the scientific interpretation of how well the data support a hypothesis.
The fitted parameter values summarize the model configuration that best explains the observations, but they do not by themselves describe every aspect of the evidence. Maximum Likelihood analyses can also help quantify uncertainty and compare models. These additional results let researchers assess how strongly the data support parameter estimates and distinguish competing explanations for the same measurements.
In neural decoding, researchers use recorded neural measurements together with a probability model to estimate the parameters linking activity to sensory information or behavior. The likelihood-based fit identifies the parameter values that best explain the observed measurements. This provides a principled way to evaluate how neural activity represents information and to connect recordings with decoding hypotheses.
For spike-train analysis, Maximum Likelihood provides a framework for fitting parameters that describe observed neural activity. The same approach can estimate firing-rate relationships or stimulus-response relationships, depending on the scientific question and specified model. These fitted relationships help researchers examine how neural responses vary with measured stimuli and how activity may contribute to behavior.