A feedback model of perceptual learning and categorisation

Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This article employs a computational model of the cortical region interactions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorised and could also facilitate the learning of sub-ordinate level representations suitable for object identification and perceptual expertise

Pre-integration lateral inhibition enhances unsupervised learning

A large and influential class of neural network architectures use post-integration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through pre-integration lateral inhibition, does provide appropriate coding properties and can be used to efficiently learn such representations. Furthermore, this architecture is consistent with both neuro-anatomical and neuro-physiological data. We thus argue that pre-integration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible

Cortical region interactions and the functional role of apical dendrites

The basal and distal apical dendrites of pyramidal cells occupy distinct cortical layers and are targeted by axons originating in different cortical regions. Hence, apical and basal dendrites receive information from distinct sources. Physiological evidence suggests that this anatomically observed segregation of input sources may have functional significance. This possibility has been explored in various connectionist models that employ neurons with functionally distinct apical and basal compartments. A neuron in which separate sets of inputs can be integrated independently has the potential to operate in a variety of ways which are not possible for the conventional model of a neuron in which all inputs are treated equally. This article thus considers how functionally distinct apical and basal dendrites can contribute to the information processing capacities of single neurons and, in particular, how information from different cortical regions could have disparate affects on neural activity and learning

Neural coding strategies and mechanisms of competition

A long running debate has concerned the question of whether neural representations are encoded using a distributed or a local coding scheme. In both schemes individual neurons respond to certain specific patterns of pre-synaptic activity. Hence, rather than being dichotomous, both coding schemes are based on the same representational mechanism. We argue that a population of neurons needs to be capable of learning both local and distributed representations, as appropriate to the task, and should be capable of generating both local and distributed codes in response to different stimuli. Many neural network algorithms, which are often employed as models of cognitive processes, fail to meet all these requirements. In contrast, we present a neural network architecture which enables a single algorithm to efficiently learn, and respond using, both types of coding scheme

A model of partial reference frame transforms through pooling of gain-modulated responses

In multimodal integration and sensorimotor transformation areas of the posterior parietal cortex (PPC), neural responses often appear encoded in spatial reference frames that are intermediate to the in-trinsic sensory reference frames, for example, eye-centered for visual or head-centered for auditory stimulation. Many sensory responses in these areas are also modulated by direction of gaze. We demonstrate that certain types of mixed-frame responses can be generated by pooling gain-modulated responses—similar to how complex cells in the visual cortex are thought to pool the responses of simple cells. The proposed model simulates 2 types of mixed-frame responses observed in the PPC: in particular, sensory responses that shift differentially with gaze in horizontal and verti-cal dimensions and sensory responses that shift differentially for different start and end points along a single dimension of gaze. We distinguish these 2 types of mixed-frame responses from a third type in which sensory responses shift a partial yet approximately equal amount with each gaze shift. We argue that the empirical data on mixed-frame responses may be caused by multiple mechan-isms, and we adapt existing reference-frame measures to dis-tinguish between the different types. Finally, we discuss how mixed-frame responses may be revealing of the local organization of presynaptic responses

Modelling cortico basal-ganglionic loops and the development of sequential information encoding

A connectionist model consisting of thirty cortico-basal ganglionic loops was implemented. This model encodes temporal information into a spatial pattern of neuronal activations in the prefrontal cortex using neurophysiologically plausible activation functions and circuitry without learning. This neural architecture was used to model experiments with infants. Initial results suggest that the cortical basal ganglionic circuitry has an inherent ability to differentiate sequential information

The Experiences of Medically Fragile Adolescents Who Require Respiratory Assistance

The population of medically fragile adolescents has grown in recent decades because of the sequelae of prematurity, injuries, and chronic or terminal illnesses. Medically fragile adolescents who require respiratory assistance are part of this unique population with challenges in their daily lives, yet as nurses, we know little about their experiences and the best approaches to use in caring for them. The purpose of this study was to explore the experiences of medically fragile adolescents who require respiratory assistance. Interpretive phenomenology was used to describe and interpret the experience of 11 medically fragile adolescents who required respiratory assistance. The adolescents ranged in age from 13 to 18 years of age and required respiratory assistances of tracheostomies, ventilator support, and Bi-level positive airway pressure (BiPap). Audiotaped semi-structured interviews were conducted with the adolescents. Data analysis was completed using the steps delineated by Diekelmann and Allen (1989). Six themes and one pattern were identified from the interviews with the adolescents. The major themes were “Get to know me”, “Allow me to be myself”, “Being there for me”, “No matter what, technology helps”, “I am an independent person”, and “The only one I know of”. This study explored medically fragile adolescents who required a specific technology, respiratory assistance, within a distinct developmental stage. These adolescents have a clear view of who they are as a person. They want nurses to view them as a person, not just a patient. The adolescents felt that friends were there for them when they needed support. This was in contrast to those that they did not consider friends who were judgmental. Technology had meanings that encompassed enhanced daily living and existing as a part of their day, not their whole day. The adolescents viewed themselves as an independent person and were actively engaging in activities and strategies to achieve their goals of independence. This study contributes to nursing knowledge by helping nurses to understand what these adolescents experience in their daily lives and aiding nurses in providing better care for these adolescents. Recommendations for nursing practice, education, and research were identified in this study

Fitting Predictive Coding to the Neurophysiological Data

Recent neurophysiological data showing the effects of locomotion on neural activity in mouse primary visual cortex has been interpreted as providing strong support for the predictive coding account of cortical function. Specifically, this work has been interpreted as providing direct evidence that prediction-error, a distinguishing property of predictive coding, is encoded in cortex. This article evaluates these claims and highlights some of the discrepancies between the proposed predictive coding model and the neuro-biology. Furthermore, it is shown that the model can be modified so as to fit the empirical data more successfully.<br/

Classification using sparse representations:a biologically plausible approach

Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain

Learning posture invariant spatial representations through temporal correlations

A hierarchical neural network model is used to learn, without supervision, sensory-sensory coordinate transformations like those believed to be encoded in the dorsal pathway of the cerebral cortex. The resulting representations of visual space are invariant to eye orientation, neck orientation, or posture in general. These posture invariant spatial representations are learned using the same mechanisms that have previously been proposed to operate in the cortical ventral pathway to learn object representation that are invariant to translation, scale, orientation, or viewpoint in general. This model thus suggests that the same mechanisms of learning and development operate across multiple cortical hierarchies