Probabilistic inference

Divisive normalization (DN), the division of a neuron’s activity by a pool of connected neurons, has been recognized as a general computational motif in the cortex. Arguments support that in the visual cortex, DN acts as a canonical computation in generative models, which delivers contextual effects and contributes to shaping response variability. Deep generative models (DGMs) have recently been adopted by neuroscience from machine vision to embrace nonlinear computations in the visual cortex. Yet, it remains unclear whether DGMs effectively integrate DN as a core computational element. We inv

Hippocampal remapping describes the phenomenon of substantial reshuffling of the spatial preferences of hippocampal place cells in response to environmental changes. Disparate neural representations in different environments could reduce interference between competing memories, hence functionally supporting robust context-dependent spatial cognition. Experimental data on existing remapping studies exhibit a diverse range of physiological properties, sometimes with seemingly conflicting results. In particular, subtle changes in experimental conditions lead to qualitatively different results. De

…use of the arising uncertainty by probabilistic inference [1]. Probabilistic inference in complex dynamic environments is challenging as it requires tracking not only individual latent variables but also the interactions among them. As a result, a neural circuit that optimally encodes these variables would need to be recurrently wired to relay information between them via feedback connections [2]. Given the additional cost of evolving and maintaining these feedback connections, we sought the circumstances under which t

…in the brain is implemented via probabilistic inference. The neural sampling hypothesis (NSH) posits that neuronal responses to a stimulus represent samples from the posterior distribution over latent world state variables (e.g., object identity) that underlie the stimulus. Existing work on NSH commonly evaluates qualitative agreement of experimental data with simple generative models of the stimulus and does not fit NSH models to experimentally observed sensory population responses. We propose a novel formulation for NSH that

…a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in biologically-plausible spiking networks. Here, we propose to leverage the population geometry, controlled by the neural code and the neural dynamics, to implement fast samplers in spiking neural networks. We first show that two classes of spiking samplers---efficient balanced spi

…oscillations) is shaped by probabilistic inference. However, progress has been hampered by the limited capabilities of generative models to learn both nonlinear and hierarchical representations of natural images. Here we harness the inspirations coming from neuroscience to

A hallmark of natural intelligence is the ability to sustain stable memories while flexibly learning new associations, allowing animals to adapt to dynamic environments while executing precise behaviors years after they were first learned. Yet, recent experiments have revealed that neural representations of fixed stimuli change continuously over time, contravening the classical assumption that learned features should remain static to maintain task proficiency. The phenomenon of representational drift can be reconciled with normative principles for neural computation within the framework of pro

…hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization, as well as stimulus-modulated noise variability, inhibition-dominated transients at stimu