Function of attention in learning process in the olfactory bulb

It has been suggested that in the olfactory bulb, odor information is processed through parallel channels and learning depends on the cognitive environment. The synapse’s spike effective time is defined as the effective time for a spike from pre-synapse to post-synapse, which varies with the type of synapse. A learning model of the olfactory bulb was constructed for synapses with varying spike effective times. The simulation results showed that such a model can realize the multi-channel processing of information in the bulb. Furthermore, the effect of the cognitive environment on the learning process was also studied. Different feedback frequencies were used to express different attention states. Considering the information’s multi-channel processing requirement for learning, a learning rule considering both spike timing and average spike frequency is proposed. Simulation results showed that habituation and anti-habituation of an odor in the olfactory bulb might be the result of learning guided by a common local learning rule but at different attention states.     

How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. We propose that the response selection stage sends attentional feedback signals to earlier processing levels, forming synaptic tags at those connections responsible for the stimulus-response mapping. Globally released neuromodulators then interact with tagged synapses to determine their plasticity. The resulting learning rule endows neural networks with the capacity to create new working memory representations of task relevant information as persistent activity. It is remarkably generic: it explains how association neurons learn to store task-relevant information for linear as well as non-linear stimulus-response mappings, how they become tuned to category boundaries or analog variables, depending on the task demands, and how they learn to integrate probabilistic evidence for perceptual decisions.     

Function of attention in learning process in the olfactory bulb

Biological experiments[1—8] have shown that in the olfactory bulb, odor information is encoded into spatio-temporal patterns and processed in multi-channels. In the bulb, the information is divided into basic information and fine information, which are encoded into average spiking frequency (long-term pattern) and synchronization of spikes[1—8] respectively. Compared with other structures in which information is encoded into spatio-temporal patterns, the olfactory bulb抯 structure is rather …     

Brain states: top-down influences in sensory processing

All cortical and thalamic levels of sensory processing are subject to powerful top-down influences, the shaping of lower-level processes by more complex information. New findings on the diversity of top-down interactions show that cortical areas function as adaptive processors, being subject to attention, expectation, and perceptual task. Brain states are determined by the interactions between multiple cortical areas and the modulation of intrinsic circuits by feedback connections. In perceptual learning, both the encoding and recall of learned information involves a selection of the appropriate inputs that convey information about the stimulus being discriminated. Disruption of this interaction may lead to behavioral disorders, including schizophrenia.     

Cognition in insects

A traditional view of cognition is that it involves an internal process that represents, tracks or predicts an external process. This is not a general characteristic of all complex neural processing or feedback control, but rather implies specific forms of processing giving rise to specific behavioural capabilities. In this paper, I will review the evidence for such capabilities in insect navigation and learning. Do insects know where they are, or do they only know what to do? Do they learn what stimuli mean, or do they only learn how to behave?     

Cognition in insects

A traditional view of cognition is that it involves an internal process that represents, tracks or predicts an external process. This is not a general characteristic of all complex neural processing or feedback control, but rather implies specific forms of processing giving rise to specific behavioural capabilities. In this paper, I will review the evidence for such capabilities in insect navigation and learning. Do insects know where they are, or do they only know what to do? Do they learn what stimuli mean, or do they only learn how to behave?     

Visual feedback is not necessary for the learning of novel dynamics

Background

When learning to perform a novel sensorimotor task, humans integrate multi-modal sensory feedback such as vision and proprioception in order to make the appropriate adjustments to successfully complete the task. Sensory feedback is used both during movement to control and correct the current movement, and to update the feed-forward motor command for subsequent movements. Previous work has shown that adaptation to stable dynamics is possible without visual feedback. However, it is not clear to what degree visual information during movement contributes to this learning or whether it is essential to the development of an internal model or impedance controller.

Methodology/Principle Findings

We examined the effects of the removal of visual feedback during movement on the learning of both stable and unstable dynamics in comparison with the case when both vision and proprioception are available. Subjects were able to learn to make smooth movements in both types of novel dynamics after learning with or without visual feedback. By examining the endpoint stiffness and force after learning it could be shown that subjects adapted to both types of dynamics in the same way whether they were provided with visual feedback of their trajectory or not. The main effects of visual feedback were to increase the success rate of movements, slightly straighten the path, and significantly reduce variability near the end of the movement.

Conclusions/Significance

These findings suggest that visual feedback of the hand during movement is not necessary for the adaptation to either stable or unstable novel dynamics. Instead vision appears to be used to fine-tune corrections of hand trajectory at the end of reaching movements.     

Structure Learning in Bayesian Sensorimotor Integration

Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.     

On-line integration of computer controlled diagnostic devices and medical information systems in undergraduate medical physics education for physicians

We designed and evaluated an innovative computer-aided-learning environment based on the on-line integration of computer controlled medical diagnostic devices and a medical information system for use in the preclinical medical physics education of medical students. Our learning system simulates the actual clinical environment in a hospital or primary care unit. It uses a commercial medical information system for on-line storage and processing of clinical type data acquired during physics laboratory classes. Every student adopts two roles, the role of ‘patient’ and the role of ‘physician’. As a ‘physician’ the student operates the medical devices to clinically assess ‘patient’ colleagues and records all results in an electronic ‘patient’ record. We also introduced an innovative approach to the use of supportive education materials, based on the methods of adaptive e-learning. A survey of student feedback is included and statistically evaluated.The results from the student feedback confirm the positive response of the latter to this novel implementation of medical physics and informatics in preclinical education. This approach not only significantly improves learning of medical physics and informatics skills but has the added advantage that it facilitates students’ transition from preclinical to clinical subjects.     

The effect of molecular inhibition on evolutionary learning: studies in the hypernetwork architecture

The hypernetwork architecture is a biologically inspired learning model based on abstract molecules and molecular interactions that exhibits functional and organizational correlation with biological systems. Hypernetwork organisms were trained, by molecular evolution, to solve N-input parity tasks. We found that learning improves when molecules exhibit inhibitory sites, allowing molecular inhibition and opening the possibility of forming negative feedback regulatory pathways. Optimal learning is achieved when at least 20% of the molecules in each cell have inhibitory sites. Intra-cellular as well as inter-cellular molecular inhibitions play an important role in the information processing of hypernetwork organisms, by maintaining a balance of the molecular cascade reactions. Similar mechanisms inside neurons are considered important for memory.     

Bimanual Coordination Learning with Different Augmented Feedback Modalities and Information Types

Previous studies have shown that bimanual coordination learning is more resistant to the removal of augmented feedback when acquired with auditory than with visual channel. However, it is unclear whether this differential “guidance effect” between feedback modalities is due to enhanced sensorimotor integration via the non-dominant auditory channel or strengthened linkage to kinesthetic information under rhythmic input. The current study aimed to examine how modalities (visual vs. auditory) and information types (continuous visuospatial vs. discrete rhythmic) of concurrent augmented feedback influence bimanual coordination learning. Participants either learned a 90°-out-of-phase pattern for three consecutive days with Lissajous feedback indicating the integrated position of both arms, or with visual or auditory rhythmic feedback reflecting the relative timing of the movement. The results showed diverse performance change after practice when the feedback was removed between Lissajous and the other two rhythmic groups, indicating that the guidance effect may be modulated by the type of information provided during practice. Moreover, significant performance improvement in the dual-task condition where the irregular rhythm counting task was applied as a secondary task also suggested that lower involvement of conscious control may result in better performance in bimanual coordination.     

Plasticity of dendritic excitability

Dendrites are equipped with a plethora of voltage-gated ion channels that greatly enrich the computational and storage capacity of neurons. The excitability of dendrites and dendritic function display plasticity under diverse circumstances such as neuromodulation, adaptation, learning and memory, trauma, or disorders. This adaptability arises from alterations in the biophysical properties or the expression levels of voltage-gated ion channels-induced by the activity of neurotransmitters, neuromodulators, and second-messenger cascades. In this review we discuss how this plasticity of dendritic excitability could alter information transfer and processing within dendrites, neurons, and neural networks under physiological and pathological conditions.     

Neural correlates of variations in event processing during learning in central nucleus of amygdala

Attention or variations in event processing help drive learning. Lesion studies have implicated the central nucleus of the amygdala (CeA) in this process, particularly when expected rewards are omitted. However, lesion studies cannot specify how information processing in CeA supports such learning. To address these questions, we recorded CeA neurons in rats performing a task in which rewards were delivered or omitted unexpectedly. We found that activity in CeA neurons increased selectively at the time of omission and declined again with learning. Increased firing correlated with CeA-inactivation sensitive measures of attention. Notably CeA neurons did not fire to the cues or in response to unexpected rewards. These results indicate that CeA contributes to learning in response to reward omission due to a specific role in signaling actual omission rather than a more general involvement in signaling expectancies, errors, or reward value.     

When Money Is Not Enough: Awareness,Success, and Variability in Motor Learning

When performing a skill such as throwing a dart, many different combinations of joint motions suffice to hit the target. The motor system adapts rapidly to reduce bias in the desired outcome (i.e., the first-order moment of the error); however, the essence of skill is to produce movements with less variability (i.e., to reduce the second-order moment). It is easy to see how feedback about success or failure could sculpt performance to achieve this aim. However, it is unclear whether the dimensions responsible for success or failure need to be known explicitly by the subjects, or whether learning can proceed without explicit awareness of the movement parameters that need to change. Here, we designed a redundant, two-dimensional reaching task in which we could selectively manipulate task success and the variability of action outcomes, whilst also manipulating awareness of the dimension along which performance could be improved. Variability was manipulated either by amplifying natural errors, leaving the correlation between the executed movement and the visual feedback intact, or by adding extrinsic noise, decorrelating movement and feedback. We found that explicit, binary, feedback about success or failure was only sufficient for learning when participants were aware of the dimension along which motor behavior had to change. Without such awareness, learning was only present when extrinsic noise was added to the feedback, but not when task success or variability was manipulated in isolation; learning was also much slower. Our results highlight the importance of conscious awareness of the relevant dimension during motor learning, and suggest that higher-order moments of outcome signals are likely to play a significant role in skill learning in complex tasks.     

Feedback regulation of EGFR signalling: decision making by early and delayed loops

Human-made information relay systems invariably incorporate central regulatory components, which are mirrored in biological systems by dense feedback and feedforward loops. This type of system control is exemplified by positive and negative feedback loops (for example, receptor endocytosis and dephosphorylation) that enable growth factors and receptor Tyr kinases of the epidermal growth factor receptor (EGFR)/ERBB family to regulate cellular function. Recent studies show that the collection of feedback regulatory loops can perform computational tasks - such as decoding ligand specificity, transforming graded input signals into a digital output and regulating response kinetics. Aberrant signal processing and feedback regulation can lead to defects associated with pathologies such as cancer.     

Computational aspects of feedback in neural circuits

It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning) to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also throw new light on the computational role of feedback in other complex biological dynamical systems, such as, for example, genetic regulatory networks.     

Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.     

Effects of feedback of information on clinical practice: a review.

OBJECTIVE--To establish what is known about the role of feedback of statistical information in changing clinical practice. DESIGN--Review of 36 studies of interventions entailing the use of statistical information for audit or practice review, which used a formal research design. SUBJECTS--Papers identified from computer searches of medical and health service management publications, of which 36 describing studies of interventions designed to influence clinical care and including information feedback from clinical or administrative data systems were reviewed. MAIN OUTCOME MEASURES--Evidence for effect of information feedback on change in clinical practice. RESULTS--Information feedback was most likely to influence clinical practice if it was part of strategy to target decision makers who had already agreed to review their practice. A more direct effect was discernable if the information was presented close to the time of decision making. The questions of the optimum layout and quantity of information were not addressed; the 36 papers were insufficient for defining good formats for information to be used for audit or quality assurance. CONCLUSIONS--Given the cost of information processing and the current emphasis on closing the audit loop in the health services, it is important that the use of information in the audit process should be critically evaluated.     

Partial reinforcement in human biofeedback learning.

This paper reviews the evidence for the efficacy of partial reinforcement in producing resistance to extinction in human biofeedback experiments. The methodological criteria necessary to demonstrate such effects are discussed, as is the status of the analogy of reinforcement and information feedback. It is suggested that the problem of maintaining responding in the absence of feedback should be tackled empirically rather than assuming the validity of findings from other areas of learning theory.     

Common Neural Mechanisms Underlying Reversal Learning by Reward and Punishment

Impairments in flexible goal-directed decisions, often examined by reversal learning, are associated with behavioral abnormalities characterized by impulsiveness and disinhibition. Although the lateral orbital frontal cortex (OFC) has been consistently implicated in reversal learning, it is still unclear whether this region is involved in negative feedback processing, behavioral control, or both, and whether reward and punishment might have different effects on lateral OFC involvement. Using a relatively large sample (N = 47), and a categorical learning task with either monetary reward or moderate electric shock as feedback, we found overlapping activations in the right lateral OFC (and adjacent insula) for reward and punishment reversal learning when comparing correct reversal trials with correct acquisition trials, whereas we found overlapping activations in the right dorsolateral prefrontal cortex (DLPFC) when negative feedback signaled contingency change. The right lateral OFC and DLPFC also showed greater sensitivity to punishment than did their left homologues, indicating an asymmetry in how punishment is processed. We propose that the right lateral OFC and anterior insula are important for transforming affective feedback to behavioral adjustment, whereas the right DLPFC is involved in higher level attention control. These results provide insight into the neural mechanisms of reversal learning and behavioral flexibility, which can be leveraged to understand risky behaviors among vulnerable populations.