computational-neuroscience

People learn from experience, but with considerable individual differences in the degree and type of behavioral adjustments resulting from a given experience. Error driven learning rules provide an elegant framework for explaining both learning behavior and its neural signatures; however, implementing them requires carving the world into so-called “latent states”, that serve as substrates for lea…

BackgroundNeurite orientation dispersion and density imaging (NODDI), an emerging diffusion MRI technique for estimating the microstructural pathology of brain tissue in vivo, has attracted significant research interest. However, a systematic bibliometric analysis of this field remains unexamined. This study aims to perform a bibliometric analysis of the NODDI literature to explore the current re…

Psychotherapy schools often describe overlapping process phenomena in non-interoperable vocabularies. This pluralism is clinically valuable but computationally costly: datasets become difficult to compare, clinically load-bearing distinctions are collapsed into convenience labels, and artificial intelligence (AI) systems inherit annotation schemes rather than a clinically interpretable intermedia…

IntroductionContralateral organization is a defining feature of vertebrate nervous systems, yet its functional origin remains incompletely understood. We examined whether contralateral routing can arise as an advantageous solution in delayed bilateral control systems using a minimal computational framework.MethodsWe constructed abstract bilateral sensorimotor networks composed of sensory, central…

While perceptual multistability arises from many types of stimuli across different sensory systems, there are common dynamical features that may be rooted in universal organizing principles underlying perception. We probe the fundamental mechanisms responsible for visual multistability using a neuronal network model framework in which a set of realistic images directly drives competing pools of n…

Version 4 (April 2026) We develop a mathematical framework for neural computation in which percepts are modeled as probabilistic structures on directed acyclic graphs of subthreshold oscillatory (STO) activity, and affective valence is defined in terms of entropy dynamics on these structures. A central problem in the study of consciousness is how structured subjective experience, including both p…

Representation similarity measures such as Centered Kernel Alignment (CKA) are widely used to compare neural networks by quantifying geometric alignment in representation space. However, their relationship to functional behavior under input transformations remains unclear. We study the relationship between representation similarity and transformation-dependent stability across a sequence of neura…

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions Tuckute, Greta; Feather, Jenelle; Boebinger, Dana; McDermott, Josh H Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neura…

IntroductionWhen trying to differentiate between hemodynamic cortical and extracerebral signals identified by devices used to detect cortical activity, statistical methods such as principal component analysis (PCA) are commonly employed as alternative approaches to using short separation measurements to reduce the influence of extracerebral hemodynamics. PCA requires a threshold value to separate…

A dynamical condition is presented under which a system forms an interior and sustains structured activity across time. Let λ_self denote the rate at which prior internal states contribute to subsequent state formation, and let λ_env denote the rate at which environmental input perturbs that evolution. When the ratio λ_self / λ_env exceeds a critical threshold R_star and is maintained across the …

A dynamical condition is presented under which a system forms an interior and sustains structured activity across time. Let λ_self denote the rate at which prior internal states contribute to subsequent state formation, and λ_env denote the rate at which environmental input perturbs that evolution. When the ratio λ_self / λ_env exceeds a critical threshold R_star and is maintained across the syst…

This February, the NeuroRSE group at CCN ran a software workshop training scientists how to use pynapple and nemos to analyze neuroscience data! We had just over 30 participants, ranging from postbacs to postdocs.

Come work with us on an open source neuroscience software package in Manhattan for a summer! The NeuroRSE team at the Flatiron Institute Center for Computational Neuroscience is looking for paid interns for Summer 2026. Interns will spend the summer living and working in New York City.

At the end of January and beginning of February, the NeuroRSE group at CCN ran two software workshops training scientists how to use pynapple and nemos to analyze neuroscience data! We had a total of about 55 participants across the two workshops, ranging from undergrads to postdocs.

Are you a neuroscience researcher with a PhD or equivalent industry experience? Neuromatch is seeking volunteer Professional Development Mentors for its July 2026 online summer courses in Computational Neuroscience, Deep Learning, and NeuroAI. The commitment is just three hours in total (three one-hour sessions), all delivered online. Mentors will share their career journeys with early-career […]…

Large neuronal networks demonstrate complex dynamics across multiple scales, ranging from single-neuron excitability and spike-train variability to mesoscopic rhythms and whole-brain activity. Different types of differential equation models have been developed to comprehend these phenomena, connecting deterministic, stochastic, and mean-field descriptions. At the deterministic level, ordinary dif…

Representation similarity metrics such as CKA are widely used to compare neural networks by quantifying geometric alignment in representation space. These measures are often interpreted as proxies for functional similarity across models, architectures, and training conditions. In this work, we investigate whether representation similarity is predictive of transformation-dependent stability in neu…

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique with promising application in the treatment of neurological and psychiatric disorders. However, its effectiveness is often limited by the high inter-subject variability of the induced effects, mainly attributable to individual anatomical differences, which are not considered in the design of the stimulation…