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An important problem in neuroscience is to identify low-dimensional structure underlying noisy, high-dimensional spike trains. In this talk, I will discuss recent advances for tackling this problem in single and multi-region neural datasets. First, I will discuss the Gaussian Process Latent Variable Model with Poisson observations (Poisson-GPLVM), which seeks to identify a low-dimensional nonlinear manifold from spike train data. This model can successfully handle datasets that appear high-dimensional with linear dimensionality reduction methods like PCA, and we show that it can identify a 2D spatial map underlying hippocampal place cell responses from their spike trains alone. Second, I will discuss recent extensions to Poisson-spiking Gaussian Process Factor Analysis (Poisson-GPFA), which incorporates separate signal and noise dimensions as well as a multi-region model with coupling between latent variables governing activity in different regions. This model provides a powerful tool for characterizing the flow of signals between brain areas, and we illustrate its applicability using multi-region recordings from mouse visual cortex.

Processing of a visual stimulus depends on previous and surrounding stimulations. For example, how an orientation detail is perceived depends on previous and surrounding orientation content. The influence of such context, temporal and spatial, is postulated to be beneficial, but the involved mechanism(s) as well as the behavioral relevance are not fully understood. Here, using behavioral experiments that measure how context integrates in space and time, we argue that context changes how statistical decisions are made by the visual system. Most importantly, we find that several context-dependent perceptual biases, such as visual illusions and aftereffects, are much reduced with increasing reaction time. To account for this, we consider a simple yet general explanation: prior and noisy decision-related evidence are integrated serially, with evidence and noise accumulating over time (as in the standard drift diffusion model). With time, owing to noise accumulation, the prior effect is predicted to diminish. This theory suggests a single-process alternative to the intuitive notion of dual brain systems (the so-called System 1 and System 2), and quantitatively predicts several known properties of perceptual bias, such as the order-of-magnitude variation in measured bias magnitudes between individuals.

Functional MRI is used pervasively in human brain research, enabling characterization of distributed brain activity underlying complex perceptual and cognitive processes. However, heretofore this technique has been limited in utility in rodents. I will present whole-brain functional imaging of head-fixed mice performing go/no-go odor discrimination in a platform allowing precise odor-delivery system, non-invasive sniff recordings and lick detection, detailing the brain regions subserving this behavior from the naïve state to task proficiency including learning of rule reversal. I will briefly discuss efforts to expand the mouse fMRI platform to additional modalities and conclude by describing the prospects of this approach more broadly.