Ideal binocular disparity detectors learned using independent subspace analysis on binocular natural image pairs

This work was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) grant [BB/K018973/1].An influential theory of mammalian vision, known as the efficient coding hypothesis, holds that early stages in the visual cortex attempts to form an efficient coding of ecologically valid stimuli. Although numerous authors have successfully modelled some aspects of early vision mathematically, closer inspection has found substantial discrepancies between the predictions of some of these models and observations of neurons in the visual cortex. In particular analysis of linear-non-linear models of simple-cells using Independent Component Analysis has found a strong bias towards features on the horoptor. In order to investigate the link between the information content of binocular images, mathematical models of complex cells and physiological recordings, we applied Independent Subspace Analysis to binocular image patches in order to learn a set of complex-cell-like models. We found that these complex-cell-like models exhibited a wide range of binocular disparity-discriminability, although only a minority exhibited high binocular discrimination scores. However, in common with the linear-non-linear model case we found that feature detection was limited to the horoptor suggesting that current mathematical models are limited in their ability to explain the functionality of the visual cortex.Publisher PDFPeer reviewe

Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells

Unsupervised adaptation to the spatiotemporal statistics of visual experience is a key computational principle that has long been assumed to govern postnatal development of visual cortical tuning, including orientation selectivity of simple cells and position tolerance of complex cells in primary visual cortex (V1). Yet, causal empirical evidence supporting this hypothesis is scant. Here, we show that degrading the temporal continuity of visual experience during early postnatal life leads to a sizable reduction of the number of complex cells and to an impairment of their functional properties while fully sparing the development of simple cells. This causally implicates adaptation to the temporal structure of the visual input in the development of transformation tolerance but not of shape tuning, thus tightly constraining computational models of unsupervised cortical learning

Invariant visual object recognition : biologically plausible approaches

Key properties of inferior temporal cortex neurons are described, and then, the biological plausibility of two leading approaches to invariant visual object recognition in the ventral visual system is assessed to investigate whether they account for these properties. Experiment 1 shows that VisNet performs object classification with random exemplars comparably to HMAX, except that the final layer C neurons of HMAX have a very non-sparse representation (unlike that in the brain) that provides little information in the single-neuron responses about the object class. Experiment 2 shows that VisNet forms invariant representations when trained with different views of each object, whereas HMAX performs poorly when assessed with a biologically plausible pattern association network, as HMAX has no mechanism to learn view invariance. Experiment 3 shows that VisNet neurons do not respond to scrambled images of faces, and thus encode shape information. HMAX neurons responded with similarly high rates to the unscrambled and scrambled faces, indicating that low-level features including texture may be relevant to HMAX performance. Experiment 4 shows that VisNet can learn to recognize objects even when the view provided by the object changes catastrophically as it transforms, whereas HMAX has no learning mechanism in its S-C hierarchy that provides for view-invariant learning. This highlights some requirements for the neurobiological mechanisms of high-level vision, and how some different approaches perform, in order to help understand the fundamental underlying principles of invariant visual object recognition in the ventral visual strea

The development of a high speed 3D 2-photon microscope for neuroscience

The progress of neuroscience is limited by the instrumentation available to it for studying the brain. At present, there is a serious instrumentation gap between functional Magnetic Resonance Imaging (fMRI) of whole brains and the microscopic scale functional imaging possible with today’s optical microscopes and electrophysiology techniques, such as patch clamping of individual neurons. This thesis describes the development of a new extension to optical microscopy that enables refocusing within 25 microseconds rather than the large fraction of a second possible by moving the sample or objective. The system is capable of refocusing a laser beam that is monitoring activity in 3D samples of live brain tissue 300 times faster than previously possible. This will make practical a new type of optical functional imaging for studying small sub-networks of neurons containing up to about 30,000 neurons at up to 30,000 sub micrometre sized monitored points of interest per second. The thesis describes the development of a detailed design for a new type of 3D scanner that uses Acousto-Optic Deflectors (AODs) to diffractively deflect and focus an intense laser beam beneath a conventional microscope objective. The fluorescence of calcium sensitive dyes in live neurons is used to monitor action potentials conveying signals between neurons. The optical and systems engineering problems and design trade-offs involved are discussed in detail. The results of extensive computer modelling are described and innovative solutions to several key optical physics based engineering problems are explained. The practical problems found in building a prototype machine incorporating these innovations are described and the encouraging first operational results from the machine reported

Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways

Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson's disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To “humanize” this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy. Keywords: alpha-synuclein; iPS cell; Parkinson’s disease; stem cell; mRNA translation; RNA-binding protein; LRRK2; VPS35; vesicle trafficking; yeas

Models, Simulations, and the Reduction of Complexity

Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

Media Infrastructures and the Politics of Digital Time

Digital media everyday inscribe new patterns of time, promising instant communication, synchronous collaboration, intricate time management, and profound new advantages in speed. The essays in this volume reconsider these outward interfaces of convenience by calling attention to their supporting infrastructures, the networks of digital time that exert pressures of conformity and standardization on the temporalities of lived experience and have important ramifications for social relations, stratifications of power, practices of cooperation, and ways of life. Interdisciplinary in method and international in scope, the volume draws together insights from media and communication studies, cultural studies, and science and technology studies while staging an important encounter between two distinct approaches to the temporal patterning of media infrastructures, a North American strain emphasizing the social and cultural experiences of lived time and a European tradition, prominent especially in Germany, focusing on technological time and time-critical processes