Generative model

…of the mouse visual cortex with a generative model that predicts connectivity based on explicit biological and physical features: excitatory or inhibitory cell type, the distance between cell bodies, and cell's depth, which is c

Bayesian models have been successful in explaining sensorimotor behavior in humans and animals, yet it remains unclear whether they can also explain neural activity. Testing them using neural data is complicated by the fact that linking them to neural activity requires two key moving parts: (1) a hypothesis about which latent variable is represented by a neuron, or neural population (``generative model'') and (2) a hypothesis about how beliefs about a latent variable (``posterior'') are represented by neural responses. Here, we present two new methods that in conjunction allow us to, first, fa

…Therefore, we propose a generative model containing separate latent variables representing form and motion. The model is related to prior work on sparse coding and slow feature analysis, but goes further by positing multiplicative interactions between latent variables for invariant object representations and for equivariant transformations representi

Mental imagery refers to our remarkable capacity to generate percepts, emotions, and thoughts in the absence of external stimuli. This ability allows us to generate art, simulate actions and outcomes, remember previous experiences, and imagine new ones. Little is known about the mechanisms that enable mental imagery. While neuroimaging data shows that sensory areas are active during imagery (1–4), it remains unknown whether this is due to reactivation of the same neurons that support perception or separate circuitry located in the same regions. Here we examine the single-neuron mechanisms of v

…Based on the inversion of a generative model of raster plot synthesis, we present a novel detection model. This model derives an optimal detection procedure in the form of logistic regression combined with temporal convolution. Its differentiability allows for a supervised learning approach usin

…neural networks of rate coding models implicitly performs variational Bayesian inference under a well-known form of partially observed Markov decision process model (Isomura, Shimazaki, Friston, Commun Biol, 2022). Based on the proposed theory, w

A fundamental goal in neuroscience is to connect an animal's behavior to its neural activity. However, imaging neural activity in a behaving animal presents unique challenges because the animal's movements create motion artifacts that, in the worst cases, cannot be distinguished from neural signals of interest. One approach to mitigating motion artifacts is to image two channels simultaneously: one that captures a calcium sensitive fluorophore, such as GCaMP, and another that captures a calcium insensitive fluorophore such as RFP. In principle, because the calcium insensitive channel contains

…Specifically, we model the firing rates of single neurons using a set of kernels characterizing neurons' responses to time-sensitive sparse events/stimuli. The kernels can be either unique or shared across the population and are weighted by codes whose amplitude and timing are trial-spe

…that I am involved in, spanning from generative model to computer vision and natural language processing. There are many opportunities for fundamental innovation in this field, ad

…machine learning techniques to build a generative model of cytokine response. We discover that, surprisingly, immune activity is encoded into one global parameter, encoding ligand antigenic properties and to a less extent ligand quantity. Based on this we build a simple interpretable model which can fully explain the broad variability of cytokines dynamics. We