Publication

Computational models of episodic-like memory in food-caching birds

Related publications (71)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Kernel Memory Networks: A Unifying Framework for Memory Modeling

Wulfram Gerstner, Johanni Michael Brea, Georgios Iatropoulos

We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classifica ...

2022

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Tilo Schwalger, Valentin Marc Schmutz

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal rep ...

PUBLIC LIBRARY SCIENCE2022

Structure-preserving approaches and data-driven closure modeling for model order reduction

Nicolò Ripamonti

In this thesis, we propose model order reduction techniques for high-dimensional PDEs that preserve structures of the original problems and develop a closure modeling framework leveraging the Mori-Zwanzig formalism and recurrent neural networks. Since high ...

EPFL2022

Where does the engram come from? Study of prefrontal cortex inputs during memory consolidation

Lucie Pauline Dixsaut

The formation and storage of memories has been under deep investigation for several decades. Nevertheless, the precise contribution of each brain region involved in this process and the interplay between them across memory consolidation is still largely de ...

EPFL2022

Aesthetic experience enhances first-person spatial representation

Mariana Babo Rebelo, Marie Chatel

Episodic autobiographical memories are characterized by a spatial context and an affective component. But how do affective and spatial aspects interact? Does affect modulate the way we encode the spatial context of events? We investigated how one element o ...

NATL ACAD SCIENCES2022

Where memories are stored in the brain is an age-old question in psychology and neuroscience alike. In particular, whether hippocampus-encoded memories are transferred to the cortex or remain hippocampus-dependent over time has not been definitely answered ...

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2022

Memory-Based Model Editing at Scale

Antoine Bosselut

Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. Model editors make local updates to the behavior of base (pre-trained) models to inject updated knowledge or correct undesirable behaviors. ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Morphological Sensitivity and Falling Behavior of Paper V-Shapes

Josephine Anna Eleanor Hughes

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) ...

MIT PRESS2022

Reward biases spontaneous neural reactivation during sleep

Dimitri Nestor Alice Van De Ville, Maximilien Catsiyannis

Sleep favors the reactivation and consolidation of newly acquired memories. Yet, how our brain selects the noteworthy information to be reprocessed during sleep remains largely unknown. From an evolutionary perspective, individuals must retain information ...

NATURE RESEARCH2021

Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning

Hakgu Kim

Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bott ...

IEEE COMPUTER SOC2021