This week we read some new work from Shaul Druckmann and Karel Svoboda’s groups (https://www.biorxiv.org/content/early/2018/07/25/376830). They analyzed simultaneously recorded activity from the anterior lateral motor cortex (ALM; perhaps homologous to a premotor area in primates) from mice performing a delayed discrimination task (either a somatosensory pole detection task or an auditory tone discrimination task). They analyzed 55 sessions with 6-31 units on each session. Given the strong task epoch dependent responsiveness of most cells in the population, they fit a switching linear dynamical system (sLDS) to the data using expectation-maximazation. However, in their model, the switch times were dictated by the task structure, making the model significantly easier to fit than a sLDS with unconstrained switch times. They called their model a epoch-dependent linear dynamical system (EDLDS).

They used leave-one-neuron-out cross validation (*i.e.* compute the posterior on test trials without including one neuron’s activity, and then predict that neuron’s activity from the posterior) to test the model fit and found that it often fit the data about as well a sLDS that could flexibly assign the timing of the same number of switch events and significantly outperformed Gaussian process factor analysis.

The model defines a low-dimensional latent space to which they apply several analyses. First, they applied linear discriminate analysis (LDA) to decode the animal’s choice on each trial and show that it outperforms LDA applied to the activity of the full neural population, even when regularization is included.

Next, they applied principal components analysis (PCA) to the latent activity and the full neural population activity to visualize the dominant temporal trajectories within each space. PC projections of the full population activity showed sharp temporal transitions between task epochs and a random spatial ordering across trials, while PC projections of the latent activity showed smooth temporal transitions and strongly ordered dynamics.

They quantified the “orderliness” of each representation by computing the consistency of the trial-ranked value of the LDA projection across time to confirm greater orderliness within the latent space than the full neural activity. They also found that decode analyses to previous trial outcome or choice on error trials using the latent activity outperformed the same analyses applied to the full neural activity.

In summary, the dynamical nature of the EDLDS provides a smooth, de-noised portrait of the temporal dynamics present in the data but that might not easily reveal itself with standard analyses.

THANKS! (Complaint noted – I will now proceed to hound Farhan more aggressively…)