Topic: controllability

Fatih Porikli examines why scaling image models alone does not guarantee visually correct results. He discusses separating scene planning from rendering, improving controllability and editing, and bringing high-resolution generation to edge devices, including the role of training objectives and reinforcement learning.

Neural circuits exhibit complex activity patterns, both spontaneously and in response to external stimuli. Information encoding and learning in neural circuits depend on the ability of time-varying stimuli to control spontaneous network activity. In particular, variability arising from the sensitivity to initial conditions of recurrent cortical circuits can limit the information conveyed about the sensory input. Spiking and firing rate network models can exhibit such sensitivity to initial conditions that are reflected in their dynamic entropy rate and attractor dimensionality computed from th

Recent meta-analyses indicate that non-neurobiological treatments for mental disorders, such as psychotherapy, demonstrate variable success rates ranging from 30% to 60% [Munder et al., 2019] . However, there is no consensus on the theoretical upper limit of these treatments [Cuijpers et al., 2024]. This study aims to estimate this upper bound by examining the alignment between network control properties of emotional and whole-brain fMRI responses to visual emotional stimuli. We hypothesize that the upper limit of non-neurobiological treatments (i.e., treatments that do not monitor brain resp