A Neural Model of Surface Perception: Lightness, Anchoring, and Filling-in

This article develops a neural model of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models have clarified how the brain can compute the relative contrast of images from variably illuminate scenes. How the brain determines an absolute lightness scale that "anchors" percepts of surface lightness to us the full dynamic range of neurons remains an unsolved problem. Lightness anchoring properties include articulation, insulation, configuration, and are effects. The model quantatively simulates these and other lightness data such as discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, and the Craik-O'Brien-Cornsweet illusion. The model also clarifies the functional significance for lightness perception of anatomical and neurophysiological data, including gain control at retinal photoreceptors, and spatioal contrast adaptation at the negative feedback circuit between the inner segment of photoreceptors and interacting horizontal cells. The model retina can hereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A later model cortical processing stages, boundary representations gate the filling-in of surface lightness via long-range horizontal connections. Variants of this filling-in mechanism run 100-1000 times faster than diffusion mechanisms of previous biological filling-in models, and shows how filling-in can occur at realistic speeds. A new anchoring mechanism called the Blurred-Highest-Luminance-As-White (BHLAW) rule helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural images under variable lighting conditions.Air Force Office of Scientific Research (F49620-01-1-0397); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-01-1-0624

Excitatory postsynaptic potentials in rat neocortical neurons in vitro. III. Effects of a quinoxalinedione non-NMDA receptor antagonist

1. Intracellular microelectrodes were used to obtain recordings from neurons in layer II/III of rat frontal cortex. A bipolar electrode positioned in layer IV of the neocortex was used to evoke postsynaptic potentials. Graded series of stimulation were employed to selectively activate different classes of postsynaptic responses. The sensitivity of postsynaptic potentials and iontophoretically applied neurotransmitters to the non-N-methyl-D-asparate (NMDA) antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) was examined. 2. As reported previously, low-intensity electrical stimulation of cortical layer IV evoked short-latency early excitatory postsynaptic potentials (eEPSPs) in layer II/III neurons. CNQX reversibly antagonized eEPSPs in a dose-dependent manner. Stimulation at intensities just subthreshold for activation of inhibitory postsynaptic potentials (IPSPs) produced long-latency (10 to 40-ms) EPSPs (late EPSPs or 1EPSPs). CNQX was effective in blocking 1EPSPs. 3. With the use of stimulus intensities at or just below threshold for evoking an action potential, complex synaptic potentials consisting of EPSP-IPSP sequences were observed. Both early, Cl(-)-dependent and late, K(+)-dependent IPSPs were reduced by CNQX. This effect was reversible on washing. This disinhibition could lead to enhanced excitability in the presence of CNQX. 4. Iontophoretic application of quisqualate produced a membrane depolarization with superimposed action potentials, whereas NMDA depolarized the membrane potential and evoked bursts of action potentials. At concentrations up to 5 microM, CNQX selectively antagonized quisqualate responses. NMDA responses were reduced by 10 microM CNQX. D-Serine (0.5-2 mM), an agonist at the glycine regulatory site on the NMDA receptor, reversed the CNQX depression of NMDA responses

Does Corticothalamic Feedback Control Cortical Velocity Tuning?

The thalamus is the major gate to the cortex and its contribution to cortical receptive field properties is well established. Cortical feedback to the thalamus is, in turn, the anatomically dominant input to relay cells, yet its influence on thalamic processing has been difficult to interpret. For an understanding of complex sensory processing, detailed concepts of the corticothalamic interplay need yet to be established. To study corticogeniculate processing in a model, we draw on various physiological and anatomical data concerning the intrinsic dynamics of geniculate relay neurons, the cortical influence on relay modes, lagged and nonlagged neurons, and the structure of visual cortical receptive fields. In extensive computer simulations we elaborate the novel hypothesis that the visual cortex controls via feedback the temporal response properties of geniculate relay cells in a way that alters the tuning of cortical cells for speed.Comment: 31 pages, 7 figure

Does Corticothalamic Feedback Control Cortical Velocity Tuning?

The thalamus is the major gate to the cortex and its contribution to cortical receptive field properties is well established. Cortical feedback to the thalamus is, in turn, the anatomically dominant input to relay cells, yet its influence on thalamic processing has been difficult to interpret. For an understanding of complex sensory processing, detailed concepts of the corticothalamic interplay need yet to be established. To study corticogeniculate processing in a model, we draw on various physiological and anatomical data concerning the intrinsic dynamics of geniculate relay neurons, the cortical influence on relay modes, lagged and nonlagged neurons, and the structure of visual cortical receptive fields. In extensive computer simulations we elaborate the novel hypothesis that the visual cortex controls via feedback the temporal response properties of geniculate relay cells in a way that alters the tuning of cortical cells for speed

Spatiotemporal adaptation through corticothalamic loops: A hypothesis

The thalamus is the major gate to the cortex and its control over cortical responses is well established. Cortical feedback to the thalamus is, in turn, the anatomically dominant input to relay cells, yet its influence on thalamic processing has been difficult to interpret. For an understanding of complex sensory processing, detailed concepts of the corticothalamic interplay need yet to be established. Drawing on various physiological and anatomical data, we elaborate the novel hypothesis that the visual cortex controls the spatiotemporal structure of cortical receptive fields via feedback to the lateral geniculate nucleus. Furthermore, we present and analyze a model of corticogeniculate loops that implements this control, and exhibit its ability of object segmentation by statistical motion analysis in the visual field

Higher brain functions served by the lowly rodent primary visual cortex

It has been more than 50 years since the first description of ocular dominance plasticity-the profound modification of primary visual cortex (V1) following temporary monocular deprivation. This discovery immediately attracted the intense interest of neurobiologists focused on the general question of how experience and deprivation modify the brain as a potential substrate for learning and memory. The pace of discovery has quickened considerably in recent years as mice have become the preferred species to study visual cortical plasticity, and new studies have overturned the dogma that primary sensory cortex is immutable after a developmental critical period. Recent work has shown that, in addition to ocular dominance plasticity, adult visual cortex exhibits several forms of response modification previously considered the exclusive province of higher cortical areas. These "higher brain functions" include neural reports of stimulus familiarity, reward-timing prediction, and spatiotemporal sequence learning. Primary visual cortex can no longer be viewed as a simple visual feature detector with static properties determined during early development. Rodent V1 is a rich and dynamic cortical area in which functions normally associated only with "higher" brain regions can be studied at the mechanistic level.National Eye Institute (Grant RO1 EY023037)National Institute of Mental Health (U.S.) (Grant K99 MH09965