The hippocampus and cerebellum in adaptively timed learning, recognition, and movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-86-C-0037, 90-0128); Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-92-J-1904); National Institute of Mental Health (MH-42900

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES

Umjetna neuronska mreža je sustav temeljen na radu biološke neuronske mreže, drugim riječima, ona predstavlja oponašanje biološke neuronske mreže. Cilj ovog rada je usporediti svojstva dviju različitih verzija neuronski mrežnih ART algoritama kao što su neizravne ART i ARTFC metode korištene za klasifikaciju plućnih funkcija, otkrivanje restriktivnih, opstruktivnih i normalinih uzoraka disajnih abnormalnosti putem svake neuronske mreže s podacima prikupljenim spirometrijom. Spirometrijski podaci su prikupljeni na 150 pacijenata standardnim postupkom prikupljanja, gdje se 100 ispitanika koristi za obuku i 50 za testiranje, respektivno. Rezultati su pokazali da standardi neizravni ART algoritam raste brže od ARTFC, koji uspješno rješava problem kategorizacije proliferacija.An artificial neural network is a system based on the operation of biological neural networks, in other words, it is an emulation of the biological neural system. The objective of this study is to compare the performance of two different versions of neural network ART algorithms such as Fuzzy ART vs. ARTFC methods used for classification of pulmonary function, detecting restrictive, obstructive and normal patterns of respiratory abnormalities by means of each of the neural networks, as well as the data gathered from spirometry. The spirometry data were obtained from 150 patients by standard acquisition protocol, 100 subjects used for training and 50 subjects for testing, respectively. The results showed that the standard Fuzzy ART grows faster than ARTFC, which successfully solves the category proliferation problem

On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)

Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

Effects of Early Ontogenetic Sensitive Periods on the Generalization of Risk and Safety Information in Wood Frog Tadpoles (Lithobates sylvaticus)

Environments often change within an organism’s lifetime. The ability to react and adapt to these changes is referred to as plasticity. Periods of development with heightened plasticity are called sensitive periods. Events experienced during sensitive periods can have disproportionate effects later in life across multiple phenotypes, a phenomenon called phenotypic resonance. Originally based on the phenomenon of phenotypic resonance, the phenomenon of cognitive resonance is described as the disproportionate effect a sensitive period has on the way information is used by an individual. Cognitive resonance has been studied using risk and safety information, but not on other cognitive processes such as generalization. This thesis focused on the effects of sensitive periods on the generalization of safety and risk related information. Wood frog tadpoles (Lithobates sylvaticus) were chosen as the model system and embryonic development as the sensitive period. In the first experiment, tadpoles were trained to recognize brook trout as a predator using a pairing with conspecific alarm cues, which are innately recognized as indicating risk. Tadpoles were then exposed to one of the following test odours to form a generalization gradient based on phylogenetic relatedness: brook trout, splake, tiger trout, rainbow trout, or goldfish. Tadpoles trained as embryos that brook trout was risky partially generalized risk to splake, tiger trout, and rainbow trout, which are all members of Salmonidae, but not to the distantly related goldfish. Tadpoles trained that brook trout was risky as larvae only generalized risk to splake and tiger trout, both of which are hybrids of brook trout. The second experiment followed similar procedures to the first. However, tadpoles were trained to recognize brook trout odour as safe through a process of repeated unpaired exposures called latent inhibition. Each tadpole was then taught one of the aforementioned test odours as risky through one paring with alarm cues. Tadpoles trained as embryos that brook trout was safe generalized safety partially to splake, tiger trout, and rainbow trout, but not to goldfish. Tadpoles trained that brook trout was safe as larvae only generalized to splake, the intra-genus hybrid with brook trout. These two studies indicate that embryonically exposed tadpoles generalize to more species than do larval tadpoles for both safety and risk related information. My research is among the first studies to delve into the effects of cognitive resonance and could help to further understand the effects of early development on cognitive abilities. This thesis also has implications for fields where knowledge of early development might make a difference, such as behavioural conservation and human cognitive development