computational-neuroscience

Understanding how decision making changes across the lifespan is a central challenge for neuroscience, yet research on cognitive aging has remained largely disconnected from the theoretical and computational advances that now shape modern systems neuroscience. Over the past two decades, theoretical frameworks have transformed how we study cognition in young, healthy brains, providing principled t…

Connectomes are comprehensive maps of the wiring within nervous systems, representing connections from the cellular and synaptic level (on the nanometer scale) to large-scale brain areas (on the centimeter scale). Network neuroscience focuses on modeling and analyzing these connectomes. While the primary goal of network neuroscience—understanding the principles of connectome organization and its …

Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistics and the empirical neural data of neuroscience. Serving as an interdisciplinary cornerstone, computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models through modeling, simulation, and data analys…

How much of the brain's learned algorithms depend on the fact it is a brain? We argue: a lot, but surprisingly few details matter. We point to simple biological details -- e.g. nonnegative firing and energetic/space budgets in connectionist architectures -- which, when mixed with the requirements of solving a task, produce models that predict brain responses down to single-neuron tuning. We under…

Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling—also known as virtual brain modeling—combines computational models of brain dynamics (placed at each network node) with individual brain imaging data (to coordinate and connect the nodes), advancing our unders…

Non-stationary dynamical cortical states - neural activity changing on the topology of the cortex across time - and in particular traveling waves, is an emerging topic. In this article, we propose that similar spatio-temporal traveling wave patterns observed across cortical scales are underpinned by generative mechanisms that differ in nature, that we categorize as first- and second-order traveli…

Perceptual biases offer a glimpse into how the brain processes sensory stimuli. While psychophysics has uncovered systematic biases such as contraction (stored information shifts toward a central tendency) and repulsion (the current percept shifts away from recent percepts), a unifying neural network model for how such seemingly distinct biases emerge from learning is lacking. Here, we show that …

Recently, reservoir computing techniques have been employed to elucidate the functions of the large-scale network of structural brain connections known as the connectome. Researchers investigated the role of brain connectivity patterns in reservoir computing by comparing task performance when the reservoir layer nodes of the echo state network were connected according to the connectome versus whe…

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power-law scaling, self-similarity, and self-organized criticality. As regards criticali…

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning…