Distributed memory models (DMMs) describe the process of encoding and retrieval of information as operations taking place on vectors in a high-dimensional space. A physical model of memory would not only provide a quantitatively acceptable model of behavior, but also an accurate model of the actual computations taking place in the brain that support this behavior. Although it is difficult to specify what DMMs predict for the activity of single neurons, existing technologies make it possible to measure multivariate responses from multiple neurons (tetrode or silicon arrays) or patches of cortex (fMRI or optical imaging). DMMs can make natural predictions about the relationship of these ensemble responses corresponding to different study and/or encoding events. A recent study (Manns, Howard, & Eichenbaum, SfN 2006) applied this strategy to examining predictions of the temporal context model in a judgment of recency task in behaving rats implanted with tetrode arrays. This work suggests that the hippocampus has access to a representation of spatio-temporal context during performance of this task. More broadly, it illustrates the ability of DMMs to contribute to our understanding of the neural substrates of memory, and the potential for ensemble recordings to constrain DMMs. |