AIMC Topic:
Learning
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Biological modelling of a computational spiking neural network with neuronal avalanches.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Jun 28, 2017
In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed ne...
Central and peripheral vision for scene recognition: A neurocomputational modeling exploration.
Journal of vision
Apr 1, 2017
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is m...
TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Jan 5, 2017
Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually const...
Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Jan 5, 2017
A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippoca...
Digging deeper on "deep" learning: A computational ecology approach.
The Behavioral and brain sciences
Jan 1, 2017
We propose an alternative approach to "deep" learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of ...
The fork in the road.
The Behavioral and brain sciences
Jan 1, 2017
Machines that learn and think like people should simulate how people really think in their everyday lives. The field of artificial intelligence originally traveled down two roads, one of which emphasized abstract, idealized, rational thinking and the...
Understand the cogs to understand cognition.
The Behavioral and brain sciences
Jan 1, 2017
Lake et al. suggest that current AI systems lack the inductive biases that enable human learning. However, Lake et al.'s proposed biases may not directly map onto mechanisms in the developing brain. A convergence of fields may soon create a correspon...
Thinking like animals or thinking like colleagues?
The Behavioral and brain sciences
Jan 1, 2017
We comment on ways in which Lake et al. advance our understanding of the machinery of intelligence and offer suggestions. The first set concerns animal-level versus human-level intelligence. The second concerns the urgent need to address ethical issu...
What can the brain teach us about building artificial intelligence?
The Behavioral and brain sciences
Jan 1, 2017
Lake et al. offer a timely critique on the recent accomplishments in artificial intelligence from the vantage point of human intelligence and provide insightful suggestions about research directions for building more human-like intelligence. Because ...
Children begin with the same start-up software, but their software updates are cultural.
The Behavioral and brain sciences
Jan 1, 2017
We propose that early in ontogeny, children's core cognitive abilities are shaped by culturally dependent "software updates." The role of sociocultural inputs in the development of children's learning is largely missing from Lake et al.'s discussion ...
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