Topic: latent representations

Eric Shea-Brown joins Paul Middlebrooks to examine what dynamical systems and dimensionality reveal about neural computation. They compare task-related activity with less constrained behaviour, consider how network connection motifs shape collective dynamics, and discuss high-dimensional activity embedded within lower-dimensional descriptions. Research examples connect predictive learning, latent representations and rich versus lazy learning regimes. The conversation also examines how theoretical neuroscientists choose useful abstractions and which assumptions about network structure and funct

The understanding of the stroke lesions’ consequences is limited, and it relies mostly on behavioral reports and mere descriptive correlational information from neuroimaging techniques. Using functional magnetic resonance imaging, it has been shown that the functional synchronization between distinct regions of the brain, referred to as functional connectivity, is disrupted by stroke. Nevertheless, this kind of data is usually large and high-dimensional. Extracting useful information from the vast amount of information afforded by brain networks remains a great challenge so, in order to make s

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

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