Evolution through natural selection has addressed the problem of modifying synapses
throughout large networks of neurons by exploiting diffusely projecting neuromodulatory …

Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the
processing of fast yet content-poor feedback information to correct assumptions about the …

This internationally authored volume presents major findings, concepts, and methods of
behavioral neuroscience coordinated with their simulation via neural networks. A central …

A central problem in cognitive neuroscience is how animals can manage to rapidly master
complex sensorimotor tasks when the only sensory feedback they use to improve their …

Adaptive modifiability is a hallmark of the nervous system and many neural like networks.
Consistent with this presumption, there are many network simulations where connection …

In the field of the neurobiology of learning, significant emphasis has been placed on
understanding neural plasticity within a single structure (or synapse type) as it relates to a …

Neural networks are an increasingly important tool for the mechanistic understanding of
psychological phenomena. Three commonly used principles in neural-network design …

Animals must respond selectively to specific combinations of salient environmental stimuli in
order to survive in complex environments. A task with these features, biconditional …

This article considers initial problems that investigators developing model learning programs
might encounter. Model neural systems in invertebrates are of increasing interest as a …

Behavior analysis and neuroscience are disciplines in their own right but are united in that
both are subfields of a common overarching field—biology. What most fundamentally unites …