We introduce temporal working memory (TWM), which aims to enhance the temporal modeling capabilities of Multimodal foundation models (MFMs). This plug-and-play module can be easily integrated into existing MFMs. With our TWM, nine state-of-the-art models exhibit significant performance improvements across QA, captioning, and retrieval tasks.

Working memory tests on JavaScript (digit span, operation span, visual array, spatial span)

N-Back Task Games designed to improve working memory and cognitive abilities.

Personalized Training for the Sequence Learning task with the NAO robot and the MUSE EEG sensor

A simple Android app that helps you improve your working memory

Spiking neuronal network simulations (Python, NEST Simulator) for continuous attractor working memory networks with short-term plasticity.

Example code accompanying the sternberg concept cell data release for Kyzar et al. (2024)

The classic dual n-back task implemented in Matlab; includes position, sound, and color options.

A setup to evaluate working memory using EEG signals

Estimate the demand of working memory in Social Cognition Tasks using HCP-fMRI dataset

Code for "Interpersonal movement synchrony responds to high- and low-level conversational constraints" (Paxton & Dale, 2017, Frontiers in Psychology).

Implementation of the change detection task aimed at measuring working memory capacity

A flutter app that can generate Chinese lyrics, poems and prose based on your given words and photos using GPT-2 and Working Memory Model. Designed to polish your twitter or moments.

This is our Neuromatch Academy 2020 computational neuroscience summer school group project.

Visual Working Memory Game in Python which gives you the gameplay data

Analysis Code and Data of "Concept neurons in the human medial temporal lobe flexibly represent abstract relations between concepts" (Bausch et al., 2021). Also see https://www.nature.com/articles/s41467-021-26327-3

This repository contains all codes necessary to reproduce figures and results reported in Stein, Barbosa et al. (Nature Communications, 2020) from the raw data acquired in human behavioral experiments (data included in the repository), and from the relevant model simulations.

Large Scale Neural Modeling (LSNM) simulator in Python

Reading span (RSPAN) task.