Cortical Computation of Stereo Disparity

Our ability to see the world in depth is a major accomplishment of the brain. Previous models of how positionally disparate cues to the two eyes are binocularly matched limit possible matches by invoking uniqueness and continuity constraints. These approaches cannot explain data wherein uniqueness fails and changes in contrast alter depth percepts, or where surface discontinuities cause surfaces to be seen in depth although they are registered by only one eye (da Vinci stereopsis). A new stereopsis model explains these depth percepts by proposing how cortical complex cells binocularly filter their inputs and how monocular and binocular complex cells compete to determine the winning depth signals.Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (90-0175); Office of Naval Research (N00014-91-J-4100); James S. McDonnell Foundation (94-40); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

The dendritic density field of a cortical pyramidal cell

Much is known about the computation in individual neurons in the cortical column. Also, the selective connectivity between many cortical neuron types has been studied in great detail. However, due to the complexity of this microcircuitry its functional role within the cortical column remains a mystery. Some of the wiring behavior between neurons can be interpreted directly from their particular dendritic and axonal shapes. Here, I describe the dendritic density field (DDF) as one key element that remains to be better understood. I sketch an approach to relate DDFs in general to their underlying potential connectivity schemes. As an example, I show how the characteristic shape of a cortical pyramidal cell appears as a direct consequence of connecting inputs arranged in two separate parallel layers

Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method

The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher scales. The neural network outputs (elastic properties of the microfibril) are used as inputs for the homogenisation computation to determine the properties of mineralised collagen fibril. The mechanical and geometrical properties of bone constituents (mineral, collagen and cross-links) as well as the porosity were taken in consideration. This paper aims to predict analytically the effective elastic constants of cortical bone by modelling its elastic response at these different scales, ranging from the nanostructural to mesostructural levels. Our findings of the lowest scale's output were well integrated with the other higher levels and serve as inputs for the next higher scale modelling. Good agreement was obtained between our predicted results and literature data.Comment: 2

Noise-enhanced computation in a model of a cortical column

Varied sensory systems use noise in order to enhance detection of weak signals. It has been conjectured in the literature that this effect, known as stochastic resonance, may take place in central cognitive processes such as the memory retrieval of arithmetical multiplication. We show in a simplified model of cortical tissue, that complex arithmetical calculations can be carried out and are enhanced in the presence of a stochastic background. The performance is shown to be positively correlated to the susceptibility of the network, defined as its sensitivity to a variation of the mean of its inputs. For nontrivial arithmetic tasks such as multiplication, stochastic resonance is an emergent property of the microcircuitry of the model network

An inhibitory pull-push circuit in frontal cortex.

Push-pull is a canonical computation of excitatory cortical circuits. By contrast, we identify a pull-push inhibitory circuit in frontal cortex that originates in vasoactive intestinal polypeptide (VIP)-expressing interneurons. During arousal, VIP cells rapidly and directly inhibit pyramidal neurons; VIP cells also indirectly excite these pyramidal neurons via parallel disinhibition. Thus, arousal exerts a feedback pull-push influence on excitatory neurons-an inversion of the canonical push-pull of feedforward input

A perspective on cortical layering and layer-spanning neuronal elements

This review article addresses the function of the layers of the cerebral cortex. We develop the perspective that cortical layering needs to be understood in terms of its functional anatomy, i.e., the terminations of synaptic inputs on distinct cellular compartments and their effect on cortical activity. The cortex is a hierarchical structure in which feed forward and feedback pathways have a layer-specific termination pattern. We take the view that the influence of synaptic inputs arriving at different cortical layers can only be understood in terms of their complex interaction with cellular biophysics and the subsequent computation that occurs at the cellular level. We use high-resolution fMRI, which can resolve activity across layers, as a case study for implementing this approach by describing how cognitive events arising from the laminar distribution of inputs can be interpreted by taking into account the properties of neurons that span different layers. This perspective is based on recent advances in measuring subcellular activity in distinct feed-forward and feedback axons and in dendrites as they span across layers