Publication

Representational similarity modulates neural and behavioral signatures of novelty: Model simulations

Wulfram Gerstner
2026

Abstract

Here, we provide model simulation data accompanying the analysis of neural and behavioral data in our article 'Representational similarity modulates neural and behavioral signatures of novelty'. This data set contains

(i) simulations of similarity-based novelty models for fitting of V1 novelty responses: grid_search_results.zip

(ii) simulations for parameter robustness analysis of similarity-based novelty, fit to V1 novelty responses: gridsearch_robustness_width.zip

(iii) simulations of novelty-RL agents with similarity-based novelty, fit to mouse exploration behavior: ppc.zip

The code used to generate the simulated data as well as further description of the data set are available at: https://github.com/sobecker/sim_nov

The related peer- reviewed article is available at: https://doi.org/10.1016/j.neuron.2026.01.007

The related preprint is available at: https://www.biorxiv.org/content/10.1101/2024.05.01.592002v2.abstract

Official source

https://infoscience.epfl.ch/entities/publication/07f5ef9b-dcce-4385-8afa-521b3ba1de3b

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Wulfram Gerstner
2026

Abstract

(i) simulations of similarity-based novelty models for fitting of V1 novelty responses: grid_search_results.zip

(ii) simulations for parameter robustness analysis of similarity-based novelty, fit to V1 novelty responses: gridsearch_robustness_width.zip

(iii) simulations of novelty-RL agents with similarity-based novelty, fit to mouse exploration behavior: ppc.zip

The code used to generate the simulated data as well as further description of the data set are available at: https://github.com/sobecker/sim_nov

The related peer- reviewed article is available at: https://doi.org/10.1016/j.neuron.2026.01.007

The related preprint is available at: https://www.biorxiv.org/content/10.1101/2024.05.01.592002v2.abstract

Official source

https://infoscience.epfl.ch/entities/publication/07f5ef9b-dcce-4385-8afa-521b3ba1de3b

About this result

Related concepts (32)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Physical neural network

A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.

Models of neural computation

Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.

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Representational similarity modulates neural and behavioral signatures of novelty: Model simulations

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