Topic: population recording

Modification of synaptic connections is thought to be the core mechanism of long-term memory formation. Despite the recent experimental development, there are still challenges inferring the synaptic plasticity rule from the experimental data. Here, we considered a biologically plausible synaptic plasticity model, and explored its key properties that enable efficient inference. As a recent theoretical work suggested the firing rates to be the most critical under in vivo-like conditions, we considered firing-rate dependent plasticity and its matrix representation. To enable efficient recovery

A change in pairwise spike train correlations is a well-established neural correlate of a shift in brain state, and as such has been the topic of intense study. However, while the explosion in population recording has provided a wealth of data to measure spike train correlations and their state dependence, there continues to be ambiguity in the biological mechanisms that mediate changes in correlated activity. One reason for this lack of understanding is the vast set of possible circuit and cellular realities that can support correlated spiking activity. To provide insight into how circuit and

Population recording offer a window into the underlying dynamics of brain regions. Latent Variable Models (LVMs) infer these dynamics treating the observations as projections from the latents. Evaluating the quality of these models remains challenging, as we don't have access to the true underlying dynamics. A common approach, "co-smoothing", involves jointly estimating latent variables from neural data and predicting the firing rates of held-out neurons. However, we find that optimizing this single objective often yields models that include extraneous dynamics not relevant to the underlying n