AI Medical Compendium Journal:
The Journal of neuroscience : the official journal of the Society for Neuroscience

Showing 11 to 20 of 46 articles

Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived ...

Finding Distributed Needles in Neural Haystacks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The human cortex encodes information in complex networks that can be anatomically dispersed and variable in their microstructure across individuals. Using simulations with neural network models, we show that contemporary statistical methods for funct...

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be measured using functional and structural MRI, the subtle patterns associated with the initial stages...

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and ...

Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from as...

Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial social partners remain unclear. The uncanny valley (UV) hypothesis predicts that humans prefer anth...

The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual a...

Cascaded Tuning to Amplitude Modulation for Natural Sound Recognition.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The auditory system converts the physical properties of a sound waveform to neural activities and processes them for recognition. During the process, the tuning to amplitude modulation (AM) is successively transformed by a cascade of brain regions. T...

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
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 ...

Neural Classifiers with Limited Connectivity and Recurrent Readouts.

The Journal of neuroscience : the official journal of the Society for Neuroscience
For many neural network models in which neurons are trained to classify inputs like perceptrons, the number of inputs that can be classified is limited by the connectivity of each neuron, even when the total number of neurons is very large. This pose...