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Temporal dynamics

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Robust encoding of sub-sniff temporal information in the mouse olfactory bulb

Cecilia Della Casa, Tom Warner, Sina Tootoonian, Andreas Schaefer · COSYNE 2025

The sensory world is highly dynamic, and the temporal structure of stimuli contains rich information about the environment. Odour plumes are shaped by complex airflow that imprint information about the nature and spatial organisation of the olfactory environment onto their temporal dynamics. Whilst insects and mammals alike can discern high-frequency information, how temporal properties of the olfactory environment are represented in the brain remains largely unknown. Here, we presented temporally rich and systematically varying odour stimuli whilst electrically recording from the output neuro

ePoster · Neuroscience

Next-interval dynamics shape cortical processing of touch

Wenyu Wan, Arko Ghosh · FENS Forum 2024

…If and how the recent temporal dynamics, as in the preceding intervals, shape cortical processing is unclear. Here we recorded the cortical signals (EEG) in response to a dynamic train of tactile stimulations applied at the fingertips ( n = 64). We separated the cortical signals according to the next interval dynamics by using a joint-interval distribution (JID) spanning 100ms to 10s. This yielded a two-dimensional event-related potential (JID-ERP) at all the examined time points of the EEG signal (-200 to 300 ms latency fro

ePoster · Neuroscience

Individualized representation learning of resting-state fMRI

Kuan Han, Minkyu Choi, Xiaokai Wang, Zhongming Liu · COSYNE 2023

Modest success has been made in using resting state fMRI (rs-fMRI) to predict individualized behaviors. Here, we describe a scalable and modular system for individualized representation learning of rs-fMRI. The system includes a “deep” base with “shallow” adds-on. The “deep” base is trained through multiple stages of self-supervised learning with unlabeled rs-fMRI. After the base is trained and fixed, the “shallow” adds-on are trained with supervised learning for behavior prediction. The base includes a variational auto-encoder (VAE) for unsupervised learning of spatial representations, a Bi-d

ePoster · Machine Learning

Bridging clinical and cognitive neuroscience together to investigate semantics, above and beyond language

Valentina Borghesani · University of Geneva, Switzerland & NCCR Evolving Language

…theories using the case of frontotemporal dementias affecting the language network. Specifically, we will focus on pathological, neuroimaging, and cognitive data from primary progressive aphasia. We will see how they can help us investigate the reading network, semantic knowledge organisation, and grammatical categories processing. Time permitting, the end of the talk will cover the temporal dynamics of semantic dimensions recovery and the role played by the task.

Fri, Jan 20, 2023 · 11:00 UTC

Seminar · Semantics

…and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing

Wed, Jul 6, 2022 · 15:00 UTC

Seminar · Machine Learning

Light-induced moderations in vitality and sleep in the field

Karin C. H. J. Smolders · Eindhoven University of Technology

…and sleep, with a strong focus on the temporal dynamics in these effects, and the bi-directional relation between persons' light profiles and their behavior.

Thu, May 19, 2022 · 15:00 UTC

Seminar · Chronobiology

Children's relational noun generalization strategies

Eleanor Stansbury · Université de Bourgogne

…Understanding the temporal dynamics of children’s solving strategies may help assess which processes underlie generalization. We tested children in noun and relational noun generalization tasks and collected eye tracking data. To analyze and interpret the data we followed predictions made by existing models of analogical reasoning and generalizatio

Thu, Oct 14, 2021 · 16:00 UTC

Seminar · Cognition