24 papers · ranked by Valyu relevance
Mufeng Tang, Helen Barron, Rafal Bogacz
Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent successes in applying predictive coding (PC) to static memory tasks, in this work we…
Ramy Mounir, Sudeep Sarkar
Sequential memory, the ability to form and accurately recall a sequence of events or stimuli in the correct order, is a fundamental prerequisite for biological and artificial intelligence as it underpins numerous cognitive functions (e.g., language comprehension, planning, episodic memory formation, etc.) However…
Songwei Dong, Zihan Chen, Chengshuai Shi, Peng Wang + 2 more
Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time. However, existing evaluations of LLM memory mostly rely on aggregate metrics such as final hold-out accuracy or cumulative online performance, which can obscure…
Julia Steinberg, Haim Sompolinsky
A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically…
James C.R. Whittington, William Dorrell, Timothy E.J. Behrens, Surya Ganguli + 1 more
Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a cognitive map, or model of how sensations, actions, and latent environmental or task states are all related to one another. Two key brain systems are implicated in this process: the…
Charles D. Holmes, ShiNung Ching, Lawrence H. Snyder
Though much research has characterized both the behavior and electrophysiology of spatial memory for single targets in non-human primates, we know much less about how multiple memoranda are handled. Multiple memoranda may interact in the brain, affecting the underlying representations. Mnemonic resources are famously…
Moufan Li, Kristopher T. Jensen, Qiong Zhang, Qihong Lu + 1 more
Humans exhibit structured patterns of memory recall, including a tendency to recall more recent information and to recall events in the same order they were experienced. Classic computational models explain these patterns by positing that memories incorporate the ongoing “temporal context”, formed by smoothly…
Moufan Li, Kristopher T. Jensen, Qiong Zhang, Qihong Lu + 1 more
Humans exhibit structured patterns of memory recall, including a tendency to recall more recent information and to recall events in the same order they were experienced. Classic computational models explain these patterns by positing that memories incorporate the ongoing “temporal context”, formed by smoothly…
Adam N. Hornsby, Bradley C. Love
Whether adding songs to a playlist or groceries during an online shop, how do we decide what to choose next? We develop a model that predicts such open-ended, sequential choices using a process of cued retrieval from long-term memory. Using the past choice to cue subsequent retrievals, this model predicts the…
Tomoki Kurikawa, Kunihiko Kaneko
Sequential transitions between metastable states are ubiquitously observed in the neural system and underlying various cognitive functions such as perception and decision making. Although a number of studies with asymmetric Hebbian connectivity have investigated how such sequences are generated, the focused sequences…
Qiaoli Huang, Christian F. Doeller
Human cognition is capacity-limited, requiring strategies to actively structure information. Eye movements offer a natural mechanism for sequential sampling, but whether such sequences organize mnemonic representations is unknown. We developed a working-memory task where color-frequency pairings created a consistent…
Takuya Ideriha, Junichi Ushiyama
Sequential working memory, the ability to actively maintain sequential information, is essential for human cognition. The neural representation of each item in sequential working memory is thought to be activated rhythmically within the theta (3-7 Hz) range of human electrophysiology. In the current study, we predicted…
Marike Christiane Maack, Jan Ostrowski, Michael Rose
The ability of the human brain to encode and recognize sequential information from different sensory modalities is key to memory formation. The sequence in which these modalities are presented during encoding critically affects recognition. This study investigates the encoding of sensory modality sequences and its…
Xiongbo Wu, Lluís Fuentemilla
In episodic encoding, an unfolding experience is rapidly transformed into a memory representation that binds separate episodic elements into a memory form to be later recollected. However, it is unclear how brain activity changes over time to accommodate the encoding of incoming information. This study aimed to…
Shujia Chen, Don Straney, Damiano Pasini
Recent advances in mechanical computing have harnessed bistable mechanisms with intrinsic memory to extend the scope of physically embodied intelligence, enabling history-dependent behavior. However, existing mechanical computing architectures largely fail to integrate mechanically encoded memory with the logic…
Qi Zhang
Declarative memory, or explicit memory, can be fractionated into episodic memory and semantic memory (Tulving, 1972). Episodic memory refers to personally based memories and semantic memory refers to the memory of factual knowledge. The loss of the capacity in retaining episodic memory leads to various amnesias, e.g.…
Sangjun Park, JinYeong Bak
Making neural networks remember over the long term has been a longstanding issue. Although several external memory techniques have been introduced, most focus on retaining recent information in the short term. Regardless of its importance, information tends to be fatefully forgotten over time. We present Memoria, a…
Savya Khosla, Zhen Zhu, Yifie He
This paper explores Memory-Augmented Neural Networks (MANNs), delving into how they blend human-like memory processes into AI. It covers different memory types, like sensory, short-term, and long-term memory, linking psychological theories with AI applications. The study investigates advanced architectures such as…
Fabian Peller-Konrad, Rainer Kartmann, Christian Dreher, Andre Meixner + 3 more
'Andre Meixner' 'Fabian Reister' 'Markus Grotz' 'Tamim Asfour'] Cognitive agents such as humans and robots perceive their environment through an abundance of sensors producing streams of data that need to be processed to generate intelligent behavior. A key question of cognition-enabled and AI-driven robotics is how to…
Pierfrancesco Ombrini, Qidi Wang, Alexandros Vasileiadis, Fangting Wu + 6 more
Effective optimization and control of lithium-ion batteries cannot neglect the relation between fundamental physicochemical phenomena and performance. In this work, we apply a multi-step charging protocol to commercially relevant electrodes, such as LiNi0.8Mn0.1Co0.1O2 (NMC811), LiFePO4 (LFP), LiMn1.5Ni0.5O4 (LMNO)…
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Ishir Garg, Neel Kolhe, Dawn Song, Xuandong Zhao
Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empirical work has been done to understand the specific failure modes and design choices that these systems present. Existing benchmarks report aggregate question-answering…