How the brain keeps the eyes still

The brain can hold the eyes still because it stores a memory of eye position. The brain’s memory of horizontal eye position appears to be represented by persistent neural activity in a network known as the neural integrator, which is localized in the brainstem and cerebellum. Existing experimental data are reinterpreted as evidence for an “attractor hypothesis” that the persistent patterns of activity observed in this network form an attractive line of fixed points in its state space. Line attractor dynamics can be produced in linear or nonlinear neural networks by learning mechanisms that precisely tune positive feedback.

Brain mechanisms of quantity are similar in 5-year-old children and adults

Both 5-year-old children and adults determine the quantity of a number by the use of a similar parietal lobe mechanism. Event related potentials indicate that input from Arabic digits and from dot patterns reach areas involved in determining quantity about 200 ms after input. However, voluntary key presses indicating the relation of the input to the quantity five take almost three times as long in children. The ability to trace the networks of brain areas involved in the learning of school subjects should aid in the design and testing of educational methods.

Response-reinforcement learning is dependent on N-methyl-d-aspartate receptor activation in the nucleus accumbens core

The nucleus accumbens, a site within the ventral striatum, is best known for its prominent role in mediating the reinforcing effects of drugs of abuse such as cocaine, alcohol, and nicotine. Indeed, it is generally believed that this structure subserves motivated behaviors, such as feeding, drinking, sexual behavior, and exploratory locomotion, which are elicited by natural rewards or incentive stimuli. A basic rule of positive reinforcement is that motor responses will increase in magnitude and vigor if followed by a rewarding event. It is likely, therefore, that the nucleus accumbens may serve as a substrate for reinforcement learning. However, there is surprisingly little information concerning the neural mechanisms by which appetitive responses are learned. In the present study, we report that treatment of the nucleus accumbens core with the selective competitive N-methyl-d-aspartate (NMDA) antagonist 2-amino-5-phosphonopentanoic acid (AP-5; 5 nmol/0.5 μl bilaterally) impairs response-reinforcement learning in the acquisition of a simple lever-press task to obtain food. Once the rats learned the task, AP-5 had no effect, demonstrating the requirement of NMDA receptor-dependent plasticity in the early stages of learning. Infusion of AP-5 into the accumbens shell produced a much smaller impairment of learning. Additional experiments showed that AP-5 core-treated rats had normal feeding and locomotor responses and were capable of acquiring stimulus-reward associations. We hypothesize that stimulation of NMDA receptors within the accumbens core is a key process through which motor responses become established in response to reinforcing stimuli. Further, this mechanism, may also play a critical role in the motivational and addictive properties of drugs of abuse.

Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting

To investigate the nature of plasticity in the adult visual system, perceptual learning was measured in a peripheral orientation discrimination task with systematically varying amounts of external (environmental) noise. The signal contrasts required to achieve threshold were reduced by a factor or two or more after training at all levels of external noise. The strong quantitative regularities revealed by this novel paradigm ruled out changes in multiplicative internal noise, changes in transducer nonlinearites, and simple attentional tradeoffs. Instead, the regularities specify the mechanisms of perceptual learning at the behavioral level as a combination of external noise exclusion and stimulus enhancement via additive internal noise reduction. The findings also constrain the neural architecture of perceptual learning. Plasticity in the weights between basic visual channels and decision is sufficient to account for perceptual learning without requiring the retuning of visual mechanisms.

Blockade of N-methyl-d-aspartate receptor activation suppresses learning-induced synaptic elimination

Auditory filial imprinting in the domestic chicken is accompanied by a dramatic loss of spine synapses in two higher associative forebrain areas, the mediorostral neostriatum/hyperstriatum ventrale (MNH) and the dorsocaudal neostriatum (Ndc). The cellular mechanisms that underlie this learning-induced synaptic reorganization are unclear. We found that local pharmacological blockade of N-methyl-d-aspartate (NMDA) receptors in the MNH, a manipulation that has been shown previously to impair auditory imprinting, suppresses the learning-induced spine reduction in this region. Chicks treated with the NMDA receptor antagonist 2-amino-5-phosphonovaleric acid (APV) during the behavioral training for imprinting (postnatal day 0–2) displayed similar spine frequencies at postnatal day 7 as naive control animals, which, in both groups, were significantly higher than in imprinted animals. Because the average dendritic length did not differ between the experimental groups, the reduced spine frequency can be interpreted as a reduction of the total number of spine synapses per neuron. In the Ndc, which is reciprocally connected with the MNH and not directly influenced by the injected drug, learning-induced spine elimination was partly suppressed. Spine frequencies of the APV-treated, behaviorally trained but nonimprinted animals were higher than in the imprinted animals but lower than in the naive animals. These results provide evidence that NMDA receptor activation is required for the learning-induced selective reduction of spine synapses, which may serve as a mechanism of information storage specific for juvenile emotional learning events.

Neural mechanisms for visual memory and their role in attention

Recent studies show that neuronal mechanisms for learning and memory both dynamically modulate and permanently alter the representations of visual stimuli in the adult monkey cortex. Three commonly observed neuronal effects in memory-demanding tasks are repetition suppression, enhancement, and delay activity. In repetition suppression, repeated experience with the same visual stimulus leads to both short- and long-term suppression of neuronal responses in subpopulations of visual neurons. Enhancement works in an opposite fashion, in that neuronal responses are enhanced for objects with learned behavioral relevance. Delay activity is found in tasks in which animals are required to actively hold specific information “on-line” for short periods. Repetition suppression appears to be an intrinsic property of visual cortical areas such as inferior temporal cortex and is thought to be important for perceptual learning and priming. By contrast, enhancement and delay activity may depend on feedback to temporal cortex from prefrontal cortex and are thought to be important for working memory. All of these mnemonic effects on neuronal responses bias the competitive interactions that take place between stimulus representations in the cortex when there is more than one stimulus in the visual field. As a result, memory will often determine the winner of these competitions and, thus, will determine which stimulus is attended.

Traces of learning in the auditory localization pathway

One of the fascinating properties of the central nervous system is its ability to learn: the ability to alter its functional properties adaptively as a consequence of the interactions of an animal with the environment. The auditory localization pathway provides an opportunity to observe such adaptive changes and to study the cellular mechanisms that underlie them. The midbrain localization pathway creates a multimodal map of space that represents the nervous system's associations of auditory cues with locations in visual space. Various manipulations of auditory or visual experience, especially during early life, that change the relationship between auditory cues and locations in space lead to adaptive changes in auditory localization behavior and to corresponding changes in the functional and anatomical properties of this pathway. Traces of this early learning persist into adulthood, enabling adults to reacquire patterns of connectivity that were learned initially during the juvenile period.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.