Theoretical Principles of Multiscale Spatiotemporal Control of Neuronal Networks: A Complex Systems Perspective

Does the brain behave like a (complex) network? I. Dynamics

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.

Multilevel Mesoscale Complexities in Mesoregimes: Challenges in Chemical and Biochemical Engineering

This review discusses the complex behaviors in diverse chemical and biochemical systems to elucidate their commonalities and thus help develop a mesoscience methodology to address the complexities in even broader topics. This could possibly build a new scientific paradigm for different disciplines and could meanwhile provide effective tools to tackle the big challenges in various fields, thus paving a path toward combining the paradigm shift in science with the breakthrough in technique developments. Starting with our relatively fruitful understanding of chemical systems, the discussion focuses on the relatively pristine but very intriguing biochemical systems. It is recognized that diverse complexities are multilevel in nature, with each level being multiscale and the complexity emerging always at mesoscales in mesoregimes. Relevant advances in theoretical understandings and mathematical tools are summarized as well based on case studies, and the convergence between physics and mathematics is highlighted. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A Computational Perspective of the Role of the Thalamus in Cognition

The thalamus has traditionally been considered as only a relay source of cortical inputs, with hierarchically organized cortical circuits serially transforming thalamic signals to cognitively relevant representations. Given the absence of local excitatory connections within the thalamus, the notion of thalamic relay seemed like a reasonable description over the past several decades. Recent advances in experimental approaches and theory provide a broader perspective on the role of the thalamus in cognitively relevant cortical computations and suggest that only a subset of thalamic circuit motifs fits the relay description. Here, we discuss this perspective and highlight the potential role for the thalamus, and specifically the mediodorsal (MD) nucleus, in the dynamic selection of cortical representations through a combination of intrinsic thalamic computations and output signals that change cortical network functional parameters. We suggest that through the contextual modulation of cortical computation, the thalamus and cortex jointly optimize the information and cost trade-off in an emergent fashion. We emphasize that coordinated experimental and theoretical efforts will provide a path to understanding the role of the thalamus in cognition, along with an understanding to augment cognitive capacity in health and disease.