AIMC Topic: Learning

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Reaction-diffusion memory unit: Modeling of sensitization, habituation and dishabituation in the brain.

PloS one
We propose a novel approach to investigate the effects of sensitization, habituation and dishabituation in the brain using the analysis of the reaction-diffusion memory unit (RDMU). This unit consists of Morris-Lecar-type sensory, motor, interneuron ...

Is coding a relevant metaphor for building AI?

The Behavioral and brain sciences
Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does. Here, we argue that it is an insufficient guide for building an artificial intelligence that learns to accomplish short- and long-term goals in a...

Ease of learning explains semantic universals.

Cognition
Semantic universals are properties of meaning shared by the languages of the world. We offer an explanation of the presence of such universals by measuring simplicity in terms of ease of learning, showing that expressions satisfying universals are si...

Reinforcement Learning for Improving Agent Design.

Artificial life
In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the agent's physical structure is rarely optimized for the task at hand. In...

A neural model of schemas and memory encoding.

Biological cybernetics
The ability to rapidly assimilate new information is essential for survival in a dynamic environment. This requires experiences to be encoded alongside the contextual schemas in which they occur. Tse et al. (Science 316(5821):76-82, 2007) showed that...

Humans and machines: Moving towards a more symbiotic approach to learning clinical reasoning.

Medical teacher
Artificial intelligence is a growing phenomenon that is driving major changes to how we deliver healthcare. One of its most significant and challenging contributions is likely to be in diagnosis. Artificial intelligence is challenging the physician's...

A complementary learning systems approach to temporal difference learning.

Neural networks : the official journal of the International Neural Network Society
Complementary Learning Systems (CLS) theory suggests that the brain uses a 'neocortical' and a 'hippocampal' learning system to achieve complex behaviour. These two systems are complementary in that the 'neocortical' system relies on slow learning of...

A review of learning in biologically plausible spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by ...

Stable memory with unstable synapses.

Nature communications
What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing b...

Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE).

Neural networks : the official journal of the International Neural Network Society
We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in...