Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Archi...

arxiv.org

A functional influence based circuit motif that constrains the set of plausible algorithms of cortical function

There are several plausible algorithms for cortical function that are specific enough to make testable predictions of the interactions between functionally identified cell types. Many of these algorithms are based on some variant of predictive processing. Here we set out to experimentally distinguish between two such predictive processing variants. A central point of variability between them lies in the proposed vertical communication between layer 2/3 and layer 5, which stems from the diverging assumptions about the computational role of layer 5. One assumes a hierarchically organized architecture and proposes that, within a given node of the network, layer 5 conveys unexplained bottom-up input to prediction error neurons of layer 2/3. The other proposes a non-hierarchical architecture in which internal representation neurons of layer 5 provide predictions for the local prediction error neurons of layer 2/3. We show that the functional influence of layer 2/3 cell types on layer 5 is incompatible with the hierarchical variant, while the functional influence of layer 5 cell types on prediction error neurons of layer 2/3 is incompatible with the non-hierarchical variant. Given these data, we can constrain the space of plausible algorithms of cortical function. We propose a model for cortical function based on a combination of a joint embedding predictive architecture (JEPA) and predictive processing that makes experimentally testable predictions. ### Competing Interest Statement The authors have declared no competing interest. Swiss National Science Foundation, https://ror.org/00yjd3n13 Novartis Foundation, https://ror.org/04f9t1x17 European Research Council, https://ror.org/0472cxd90, 865617

biorxiv.org

Publishing with eLife: “the future of science lies in greater transparency”

Neuroscientist Magdalena Solyga shares her latest study and her experience publishing with eLife.

buff.ly

Dynamic basal ganglia output signals license and suppress forelimb movements - Nature

Basal ganglia output neurons fire dynamically in bidirectional and movement-specific patterns to license forelimb movements. 

nature.com

Activity in serotonergic axons in visuomotor areas of cortex is modulated by the recent history of visuomotor coupling

Visuomotor experience is necessary for the development of normal function of visual cortex (Attinger et al., 2017) and likely establishes a balance between movement-related predictions and sensory sig...

biorxiv.org