Deconstructing multivariate decoding for the study of brain function
Multivariate decoding methods were developed originally as tools to enable accurate
predictions in real-world applications. The realization that these methods can also be …
predictions in real-world applications. The realization that these methods can also be …
Peeling the onion of brain representations
N Kriegeskorte, J Diedrichsen - Annual review of neuroscience, 2019 - annualreviews.org
The brain's function is to enable adaptive behavior in the world. To this end, the brain
processes information about the world. The concept of representation links the information …
processes information about the world. The concept of representation links the information …
Human orbitofrontal cortex represents a cognitive map of state space
Although the orbitofrontal cortex (OFC) has been studied intensely for decades, its precise
functions have remained elusive. We recently hypothesized that the OFC contains a" …
functions have remained elusive. We recently hypothesized that the OFC contains a" …
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis
J Diedrichsen, N Kriegeskorte - PLoS computational biology, 2017 - journals.plos.org
Representational models specify how activity patterns in populations of neurons (or, more
generally, in multivariate brain-activity measurements) relate to sensory stimuli, motor …
generally, in multivariate brain-activity measurements) relate to sensory stimuli, motor …
BrainIAK: the brain imaging analysis kit
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the
neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an …
neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an …
Representational dissimilarity metric spaces for stochastic neural networks
Quantifying similarity between neural representations--eg hidden layer activation vectors--is
a perennial problem in deep learning and neuroscience research. Existing methods …
a perennial problem in deep learning and neuroscience research. Existing methods …
Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns
Representational models specify how complex patterns of neural activity relate to visual
stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling …
stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling …
Neural computations underlying causal structure learning
MS Tomov, HM Dorfman… - Journal of Neuroscience, 2018 - Soc Neuroscience
Behavioral evidence suggests that beliefs about causal structure constrain associative
learning, determining which stimuli can enter into association, as well as the functional form …
learning, determining which stimuli can enter into association, as well as the functional form …
A state representation for reinforcement learning and decision-making in the orbitofrontal cortex
Despite decades of research, the exact ways in which the orbitofrontal cortex (OFC)
influences cognitive function have remained mysterious. Anatomically, the OFC is …
influences cognitive function have remained mysterious. Anatomically, the OFC is …
Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias
The activity of neural populations in the brains of humans and animals can exhibit vastly
different spatial patterns when faced with different tasks or environmental stimuli. The …
different spatial patterns when faced with different tasks or environmental stimuli. The …