Topic: variational autoencoder

Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memory (WM) representations are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items’ attributes to tokenized representations. The simulation r

In recent years, artificial neural networks have demonstrated human-like or super-human performance in many tasks including image or speech recognition, natural language processing (NLP), playing Go, chess, poker and video-games. One remarkable feature of the resulting models is that they can develop very intuitive latent representations of their inputs. In these latent spaces, simple linear operations tend to give meaningful results, as in the well-known analogy QUEEN-WOMAN+MAN=KING. We postulate that human brain representations share essential properties with these deep learning latent space

Motor and premotor cortical activities reflect motor planning and execution. For stereotyped movements, the internal cortical state should be predictive of subsequent motor commands. We developed a statistical method to infer a nonlinear state-space model that extracts an effective systems-level description of neural dynamics from population recordings. Without the knowledge of the task, target, or motor behavior, the inferred model can forecast neural activity at a single-trial level. Our novel inference algorithm performs approximate Bayesian filtering and smoothing within a structured varia

Juvenile male zebra finches learn to produce a single, highly stereotyped song and maintain this song over the course of their adult lives using auditory feedback. Much of what we know of the underlying learning process comes from studies of adult male zebra finches adapting to white noise feedback triggered by either high or low-pitch variants of harmonic stack syllables --- a single static perturbation of a single, simple syllable of a crystallized song. However, zebra finch song is spectrally and temporally rich, with numerous degrees of freedom, and the dynamic learning process must tackle