Generative Activity of Population: Concept Formation

Concept Formation 1 Concept Formation

Synthesizing realistic neural population activity patterns using Generative Adversarial Networks

The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons. We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from ...

Concept Formation and Dynamics of Repeated Inference in Deep Generative Models

Deep generative models are reported to be useful in broad applications including image generation. Repeated inference between data space and latent space in these models can denoise cluttered images and improve the quality of inferred results. However, previous studies only qualitatively evaluated image outputs in data space, and the mechanism behind the inference has not been investigated. The...

A generative model for scientific concept hierarchies

In many scientific disciplines, each new 'product' of research (method, finding, artifact, etc.) is often built upon previous findings-leading to extension and branching of scientific concepts over time. We aim to understand the evolution of scientific concepts by placing them in phylogenetic hierarchies where scientific keyphrases from a large, longitudinal academic corpora are used as a proxy...

Population Spatial Mobility: Monitoring, Methodology of Formation, Features of Regulation

Spatial mobility is a topical concept of analytical migration science, which makes it possible to assess the desires, readiness and capabilities of the population to move over certain distances and time. In the management of spatial mobility assessment requires the organization of systematic monitoring, which includes identifying the mobility potential in spatial and temporal interpretation, th...