AIMC Topic: Discrimination, Psychological
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Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals.
Journal of computational neuroscience
Feb 1, 2025
The brain's ability to learn and distinguish rapid sequences of events is essential for timing-dependent tasks, such as those in sports and music. However, the mechanisms underlying this ability remain an active area of research. Here, we present a P...
Noise Correlations for Faster and More Robust Learning.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jun 30, 2021
Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented ...
Image memorability is predicted by discriminability and similarity in different stages of a convolutional neural network.
Learning & memory (Cold Spring Harbor, N.Y.)
Nov 16, 2020
The features of an image can be represented at multiple levels-from its low-level visual properties to high-level meaning. What drives some images to be memorable while others are forgettable? We address this question across two behavioral experiment...
Machine-learned analysis of side-differences in odor identification performance.
Neuroscience
Nov 2, 2019
A right-left dichotomy of olfactory processes has been recognized on several levels of the perception or processing of olfactory input. On a clinical level, the lateralization of components of human olfaction is contrasted by the predominantly birhin...
Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Nov 30, 2018
Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions ...
Delta activity encodes taste information in the human brain.
NeuroImage
Jul 20, 2018
The categorization of food via sensing nutrients or toxins is crucial to the survival of any organism. On ingestion, rapid responses within the gustatory system are required to identify the oral stimulus to guide immediate behavior (swallowing or exp...
Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jul 13, 2018
Primates, including humans, can typically recognize objects in visual images at a glance despite naturally occurring identity-preserving image transformations (e.g., changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechan...
A Dynamic Neural Gradient Model of Two-Item and Intermediate Transposition.
Neural computation
Jun 12, 2018
Transposition is a tendency for organisms to generalize relationships between stimuli in situations where training does not objectively reward relationships over absolute, static associations. Transposition has most commonly been explained as either ...
Fuzzy-Rough Cognitive Networks.
Neural networks : the official journal of the International Neural Network Society
Sep 28, 2017
Rough Cognitive Networks (RCNs) are a kind of granular neural network that augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different cl...
Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task.
PloS one
Sep 21, 2017
Emotions play a significant role in internal regulatory processes. In this paper, we advocate four key ideas. First, novelty detection can be grounded in the sensorimotor experience and allow higher order appraisal. Second, cognitive processes, such ...
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