Orientation preference maps in Microcebus murinus reveal size-invariant design principles in primate visual cortex

Orientation preference maps (OPMs) are a prominent feature of primary visual cortex (V1) organization in many primates and carnivores. In rodents, neurons are not organized in OPMs but are instead interspersed in a ‘‘salt and pepper’’ fashion, although clusters of orientation-selective neurons have been reported. Does this fundamental difference reflect the existence of a lower size limit for orientation columns (OCs) below which they cannot be scaled down with decreasing V1 size? To address this question, we examined V1 of one of the smallest living primates, the 60-g prosimian mouse lemur (Microcebus murinus). Using chronic intrinsic signal imaging, we found that mouse lemur V1 contains robust OCs, which are arranged in a pinwheel-like fashion. OC size in mouse lemurs was found to be only marginally smaller compared to the macaque, suggesting that these circuit elements are nearly incompressible. The spatial arrangement of pinwheels is well described by a common mathematical design of primate V1 circuit organization. In order to accommodate OPMs, we found that the mouse lemur V1 covers one-fifth of the cortical surface, which is one of the largest V1-to-cortex ratios found in primates. These results indicate that the primate-type visual cortical circuit organization is constrained by a size limitation and raises the possibility that its emergence might have evolved by disruptive innovation rather than gradual change

Self Organisation and Hierarchical Concept Representation in Networks of Spiking Neurons

The aim of this work is to introduce modular processing mechanisms for cortical functions implemented in networks of spiking neurons. Neural maps are a feature of cortical processing found to be generic throughout sensory cortical areas, and self-organisation to the fundamental properties of input spike trains has been shown to be an important property of cortical organisation. Additionally, oscillatory behaviour, temporal coding of information, and learning through spike timing dependent plasticity are all frequently observed in the cortex. The traditional self-organising map (SOM) algorithm attempts to capture the computational properties of this cortical self-organisation in a neural network. As such, a cognitive module for a spiking SOM using oscillations, phasic coding and STDP has been implemented. This model is capable of mapping to distributions of input data in a manner consistent with the traditional SOM algorithm, and of categorising generic input data sets. Higher-level cortical processing areas appear to feature a hierarchical category structure that is founded on a feature-based object representation. The spiking SOM model is therefore extended to facilitate input patterns in the form of sets of binary feature-object relations, such as those seen in the field of formal concept analysis. It is demonstrated that this extended model is capable of learning to represent the hierarchical conceptual structure of an input data set using the existing learning scheme. Furthermore, manipulations of network parameters allow the level of hierarchy used for either learning or recall to be adjusted, and the network is capable of learning comparable representations when trained with incomplete input patterns. Together these two modules provide related approaches to the generation of both topographic mapping and hierarchical representation of input spaces that can be potentially combined and used as the basis for advanced spiking neuron models of the learning of complex representations

The Development of Bio-Inspired Cortical Feature Maps for Robot Sensorimotor Controllers

Full version unavailable due to 3rd party copyright restrictions.This project applies principles from the field of Computational Neuroscience to Robotics research, in particular to develop systems inspired by how nature manages to solve sensorimotor coordination tasks. The overall aim has been to build a self-organising sensorimotor system using biologically inspired techniques based upon human cortical development which can in the future be implemented in neuromorphic hardware. This can then deliver the benefits of low power consumption and real time operation but with flexible learning onboard autonomous robots. A core principle is the Self-Organising Feature Map which is based upon the theory of how 2D maps develop in real cortex to represent complex information from the environment. A framework for developing feature maps for both motor and visual directional selectivity representing eight different directions of motion is described as well as how they can be coupled together to make a basic visuomotor system. In contrast to many previous works which use artificially generated visual inputs (for example, image sequences of oriented moving bars or mathematically generated Gaussian bars) a novel feature of the current work is that the visual input is generated by a DVS 128 silicon retina camera which is a neuromorphic device and produces spike events in a frame-free way. One of the main contributions of this work has been to develop a method of autonomous regulation of the map development process which adapts the learning dependent upon input activity. The main results show that distinct directionally selective maps for both the motor and visual modalities are produced under a range of experimental scenarios. The adaptive learning process successfully controls the rate of learning in both motor and visual map development and is used to indicate when sufficient patterns have been presented, thus avoiding the need to define in advance the quantity and range of training data. The coupling training experiments show that the visual input learns to modulate the original motor map response, creating a new visual-motor topological map.EPSRC, University of Plymouth Graduate Schoo

A transient period for enabling motion vision precedes the critical period for ocular dominance plasticity

viii, 107 leaves : ill. (some col.) ; 28 cm.The premise that mature visual function depends upon the nature of visual experience during development is based primarily on experiments showing that visual deprivation during a 'critical' period early in life causes abnormalities in visual cortex and an enduring loss of spatial vision (amplyopia). There is, however, little evidence that early visual experience atually enables mature vision. Experments in this thesis provide such evidence. The measurement of optomotor responses daily from eye opening permanently enhances optomotor sensitivity and the perception of visual motion. The plasticity allowing this enhancement is transient and peaks in efficacy before the start of the classical 'critical ' period for ocular dominance plasticity. The enhancement is dependent upon optomotor responses generated by the movement of high spatial frequency visual stimuli, and is mediated by the visual cortex. These studies show that a form of experience-dependent plasticity, distinct from that of the critical period, enables mature motion vision

Visual recognition of objects : behavioral, computational, and neurobiological aspects

I surveyed work on visual object recognition and perception. In animals, vision has been studied mainly on the behavioral and neurobiological levels. Behavioral data typically show what the visual system, by itself or together with the rest of the organism, is capable of. They show, for example, that humans can recognie objects regardless of size and position, but that rotated objects pose problems. Important insights into the organization of behavior have also been provided by people who suffered localized brain damage. We have learned that the brain is divided into areas subserving different and relatively well-defined behaviors. The visual system itself is also organized in different subsystems; the visual cortex alone contains nearly twenty maps of the visual field. And individual neurons respond selectively to visual stimuli, e.g., the orientation of line segments, color, direction of motion, and, most intriguingly, faces. The question is how the actions of all these neurons produce the behavior we observe. How do neurons represent the shape of objects such that they can be recognized? Before we can answer the question, we have to understand the computational aspect of shape representation, the nature of the problem as it were. Many methods for representing shape have been explored, mainly by computer scientists, but so far no satisfactory answers have been found

Artificial ontogenesis: a connectionist model of development

This thesis suggests that ontogenetic adaptive processes are important for generating intelligent beha- viour. It is thus proposed that such processes, as they occur in nature, need to be modelled and that such a model could be used for generating artificial intelligence, and specifically robotic intelligence. Hence, this thesis focuses on how mechanisms of intelligence are specified.A major problem in robotics is the need to predefine the behaviour to be followed by the robot. This makes design intractable for all but the simplest tasks and results in controllers that are specific to that particular task and are brittle when faced with unforeseen circumstances. These problems can be resolved by providing the robot with the ability to adapt the rules it follows and to autonomously create new rules for controlling behaviour. This solution thus depends on the predefinition of how rules to control behaviour are to be learnt rather than the predefinition of rules for behaviour themselves.Learning new rules for behaviour occurs during the developmental process in biology. Changes in the structure of the cerebral 'cortex underly behavioural and cognitive development throughout infancy and beyond. The uniformity of the neocortex suggests that there is significant computational uniformity across the cortex resulting from uniform mechanisms of development, and holds out the possibility of a general model of development. Development is an interactive process between genetic predefinition and environmental influences. This interactive process is constructive: qualitatively new behaviours are learnt by using simple abilities as a basis for learning more complex ones. The progressive increase in competence, provided by development, may be essential to make tractable the process of acquiring higher -level abilities.While simple behaviours can be triggered by direct sensory cues, more complex behaviours require the use of more abstract representations. There is thus a need to find representations at the correct level of abstraction appropriate to controlling each ability. In addition, finding the correct level of abstrac- tion makes tractable the task of associating sensory representations with motor actions. Hence, finding appropriate representations is important both for learning behaviours and for controlling behaviours. Representations can be found by recording regularities in the world or by discovering re- occurring pat- terns through repeated sensory -motor interactions. By recording regularities within the representations thus formed, more abstract representations can be found. Simple, non -abstract, representations thus provide the basis for learning more complex, abstract, representations.A modular neural network architecture is presented as a basis for a model of development. The pat- tern of activity of the neurons in an individual network constitutes a representation of the input to that network. This representation is formed through a novel, unsupervised, learning algorithm which adjusts the synaptic weights to improve the representation of the input data. Representations are formed by neurons learning to respond to correlated sets of inputs. Neurons thus became feature detectors or pat- tern recognisers. Because the nodes respond to patterns of inputs they encode more abstract features of the input than are explicitly encoded in the input data itself. In this way simple representations provide the basis for learning more complex representations. The algorithm allows both more abstract represent- ations to be formed by associating correlated, coincident, features together, and invariant representations to be formed by associating correlated, sequential, features together.The algorithm robustly learns accurate and stable representations, in a format most appropriate to the structure of the input data received: it can represent both single and multiple input features in both the discrete and continuous domains, using either topologically or non -topologically organised nodes. The output of one neural network is used to provide inputs for other networks. The robustness of the algorithm enables each neural network to be implemented using an identical algorithm. This allows a modular `assembly' of neural networks to be used for learning more complex abilities: the output activations of a network can be used as the input to other networks which can then find representations of more abstract information within the same input data; and, by defining the output activations of neurons in certain networks to have behavioural consequences it is possible to learn sensory -motor associations, to enable sensory representations to be used to control behaviour

Visual Cortex

The neurosciences have experienced tremendous and wonderful progress in many areas, and the spectrum encompassing the neurosciences is expansive. Suffice it to mention a few classical fields: electrophysiology, genetics, physics, computer sciences, and more recently, social and marketing neurosciences. Of course, this large growth resulted in the production of many books. Perhaps the visual system and the visual cortex were in the vanguard because most animals do not produce their own light and offer thus the invaluable advantage of allowing investigators to conduct experiments in full control of the stimulus. In addition, the fascinating evolution of scientific techniques, the immense productivity of recent research, and the ensuing literature make it virtually impossible to publish in a single volume all worthwhile work accomplished throughout the scientific world. The days when a single individual, as Diderot, could undertake the production of an encyclopedia are gone forever. Indeed most approaches to studying the nervous system are valid and neuroscientists produce an almost astronomical number of interesting data accompanied by extremely worthy hypotheses which in turn generate new ventures in search of brain functions. Yet, it is fully justified to make an encore and to publish a book dedicated to visual cortex and beyond. Many reasons validate a book assembling chapters written by active researchers. Each has the opportunity to bind together data and explore original ideas whose fate will not fall into the hands of uncompromising reviewers of traditional journals. This book focuses on the cerebral cortex with a large emphasis on vision. Yet it offers the reader diverse approaches employed to investigate the brain, for instance, computer simulation, cellular responses, or rivalry between various targets and goal directed actions. This volume thus covers a large spectrum of research even though it is impossible to include all topics in the extremely diverse field of neurosciences

Feature Topography and Sound Intensity Level Encoding in Primary Auditory Cortex

The primary auditory cortex: A1) in mammals is one of the first areas in the neocortex that receives auditory related spiking activity from the thalamus. Because the neocortex is implicated in regulating high-level brain phenomena, such as attention and perception, it is therefore important in regards to these high-level behaviors to understand how sounds are represented and transformed by neuronal circuits in this area. The topographic organization of neuronal responses to auditory features in A1 provides evidence for potential mechanisms and functional roles of this neural circuitry. This dissertation presents results from models of topographic organization supporting the notion that if the topographic organization of frequency responses, termed tonotopy or cochleotopy, is aligned along the longest anatomical line segment in A1, as supported by some physiological studies, then it is unlikely that any other topography is mapped monotonically along the orthogonal axis. Thresholds of neuronal responses to sound intensity level represent a particular feature that may have a local, highly periodic topography and that is vital to the sensitivity of the auditory system. The neuronal representation of sound level in A1, particularly as it relates to encoding accuracy, contains a distribution of neurons with varying amounts of inhibition at high sound levels. Neurons with large amounts of this high-level inhibition are described as nonmonotonic or level-tuned. This dissertation presents evidence from single neuron recordings in A1 that neurons exhibiting greater high-level inhibition also exhibit lower neuronal thresholds and that lower thresholds in these nonmonotonic neurons are preserved even when much of the neuronal population is adapted for accurately encoding more intense sounds. Evidence presented in this dissertation also suggests that nonmonotonic neurons have transient responses to time-varying: dynamic) level stimuli that adapt more quickly in response to low-level sounds than those of monotonic neurons. Together these results imply that under static, steady-state-dynamic and transient-dynamic sound level conditions, nonmonotonic neurons are specialized encoders of less intense sounds that allow the auditory system to maintain sensitivity under a variety of environmental conditions