25 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…
Qian Liang, Yi Zeng, Bo Xu
Sequence learning is a fundamental cognitive function of the brain. However, the ways in which sequential information is represented and memorized are not dealt with satisfactorily by existing models. To overcome this deficiency, this paper introduces a spiking neural network based on psychological and neurobiological…
Qiaoli Huang, Jianrong Jia, Qiming Han, Huan Luo
Storing temporal sequences of events (i.e., sequence memory) is fundamental to many cognitive functions. However, how the sequence order information is maintained and represented in working memory and its behavioral significance, particularly in human subjects, remains unknown. Here, we recorded electroencephalography…
Sanjay G. Manohar, Yoni Pertzov, Masud Husain
Title: Highlights 1. • Analog report methods provide novel insights on STM for space and time. 2. • Space and time may be used to bind features in STM. 3. • The hippocampus is involved in object-location binding in STM.
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…
Puck C. Reeders, Amanda G. Hamm, Timothy A. Allen, Aaron T. Mattfeld
Remembering sequences of events defines episodic memory, but retrieval can be driven by both ordinality and temporal contexts. Whether these modes of retrieval operate at the same time or not remains unclear. Theoretically, medial prefrontal cortex (mPFC) confers ordinality, while the hippocampus (HC) associates events…
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…
Hyeonsu Lee, Woochul Choi, Youngjin Park, Se-Bum Paik
The serial-position effect in working memory is considered important for studying how a sequence of sensory information can be retained and manipulated simultaneously in neural memory circuits. Here, via a precise analysis of the primacy and recency effects in human psychophysical experiments, we propose that stable…
Yawen Lan, Xiaobin Wang, Yuchen Wang
Memory is an intricate process involving various faculties of the brain and is a central component in human cognition. However, the exact mechanism that brings about memory in our brain remains elusive and the performance of the existing memory models is not satisfactory. To overcome these problems, this paper puts…
Mufeng Tang, Helen C. Barron, Rafał 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…
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…
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…
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…
Stephen B. Fountain, Katherine H. Dyer, Claire C. Jackman
Lashley () rejected the notion that sequential behavior was accounted for by simple reflex chaining and argued instead for cognitive encoding of hierarchical plans. This notion contributed to the development of cognitive research in both human and non-human animals which continues to this day. Recently, Rosenbaum et…
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.…
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…
T. S. Jayram, Tomasz Kornuta, Ryan L. McAvoy, Ahmet S. Özcan
We propose a new architecture called Memory-Augmented Encoder-Solver (MAES) that enables transfer learning to solve complex working memory tasks adapted from cognitive psychology. It uses dual recurrent neural network controllers, inside the encoder and solver, respectively, that interface with a shared memory module…
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…
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
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…
Greg Yang, Alexander M. Rush
External neural memory structures have recently become a popular tool for algorithmic deep learning (Graves et al., 2014; Weston et al., 2014). These models generally utilize differentiable versions of traditional discrete memory-access structures (random access, stacks, tapes) to provide the storage necessary for…
Alex Graves, Greg Wayne, Ivo Danihelka
We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient…
Authors not listed
A memristor is a two-terminal electronic component that modify its conductance state depending on how much charge has passed through it previously. Halide perovskites are materials recently employed for neuromorphic computing in this type of resistive switches. Their performance is rapidly improving, yet the activation…