Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rul...

We investigated the relationship between reading and explicit and implicit categorical learning by comparing university students with poor reading to students with normal reading abilities on two categorical learning tasks. One categorical learning task involved sorting simple geometric shapes into two groups according to a unidimensional rule. The sorting rule was easily stated by the particip...

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in compar...

The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated processes, whereas information-integration is thou...

The task of learning from environment is specified. It requires the learner to infer the laws of the environment in terms of its percepts and actions, and use the laws to solve problems. Based on research on problem space creation and discrimination learning, this paper reports an approach in which exploration, rule creation and rule learning are coordinated in a single framework. With this app...

This paper introduces Deft, a new multitask learning approach for rule learning algorithms. Like other multitask learning systems, the one proposed here is able to improve learning performance on a primary task through the use of a bias learnt from similar secondary tasks. What distinguishes Deft from other approaches is its use of rule descriptions as a basis for task similarity. By translatin...

The primary goal of the research reported in this paper is to identify what criteria are responsible for the good performance of a heuristic rule evaluation function in a greedy topdown covering algorithm. We first argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trade-off by determining optimal parameter s...

Based on an earlier study on lazy Bayesian rule learning, this paper introduces a general lazy learning framework, called LazyRule, that begins to learn a rule only when classifying a test case. The objective of the framework is to improve the performance of a base learning algorithm. It has the potential to be used for diierent types of base learning algorithms. LazyRule performs attribute eli...

We studied the role of acetylcholine (ACh) in creating learning-related long-lasting modifications in the rat cortex. Rats were trained to discriminate positive and negative cues in pairs of odors, until they demonstrated rule learning and entered a mode of high capability for learning of additional odors. We have previously reported that pyramidal neurons in olfactory (piriform) cortex from tr...

This paper presents an evolutionary Multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler and still accurate linguistic fuzzy models by learning fuzzy inference operators and applying rule selection. The Fuzzy Rule Based Systems obtained in this way, have a better trade-off between interpretability and accuracy in ling...