Geometry and dimensionality reduction of feature spaces in primary visual cortex

Some geometric properties of the wavelet analysis performed by visual neurons are discussed and compared with experimental data. In particular, several relationships between the cortical morphologies and the parametric dependencies of extracted features are formalized and considered from a harmonic analysis point of view

Modeling Reverse-Phi Motion-Selective Neurons in Cortex: Double Synaptic-Veto Mechanism

Reverse-phi motion is the illusory reversal of perceived direction of movement when the stimulus contrast is reversed in successive frames. Livingstone, Tsao, and Conway (2000) showed that direction-selective cells in striate cortex of the alert macaque monkey showed reversed excitatory and inhibitory regions when two different contrast bars were flashed sequentially during a two-bar interaction analysis. While correlation or motion energy models predict the reverse-phi response, it is unclear how neurons can accomplish this. We carried out detailed biophysical simulations of a direction-selective cell model implementing a synaptic shunting scheme. Our results suggest that a simple synaptic-veto mechanism with strong direction selectivity for normal motion cannot account for the observed reverse-phi motion effect. Given the nature of reverse-phi motion, a direct interaction between the ON and OFF pathway, missing in the original shunting-inhibition model, it is essential to account for the reversal of response. We here propose a double synaptic-veto mechanism in which ON excitatory synapses are gated by both delayed ON inhibition at their null side and delayed OFF inhibition at their preferred side. The converse applies to OFF excitatory synapses. Mapping this scheme onto the dendrites of a direction-selective neuron permits the model to respond best to normal motion in its preferred direction and to reverse-phi motion in its null direction. Two-bar interaction maps showed reversed excitation and inhibition regions when two different contrast bars are presented

Predictive Coding as a Model of Biased Competition in Visual Attention

Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of cortical function which proposes that feedback acts to suppress information predicted by higher-level cortical regions. Despite this discrepancy, this article demonstrates that the predictive coding model can be used to simulate a number of the effects of attention. This is achieved via a simple mathematical rearrangement of the predictive coding model, which allows it to be interpreted as a form of biased competition model. Nonlinear extensions to the model are proposed that enable it to explain a wider range of data

Neural population coding: combining insights from microscopic and mass signals

Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior

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

Gating Input to Visual Cortex by Feedback to LGN

Anatomical studies have documented massive back-projections from higher to lower visual cortices and to the lateral geniculate nucleus (LGN). The large number of synapses from these sources suggest that they should have a profound influence on the information carried by feed-forward inputs to these cells. However, the functional role of these connections is unclear. In order to explore the role of the feedback connections, we have recorded spike trains from electrodes placed in LGN in the macaque monkey under sufenta anesthesia, and have compared LGN cells' activity with and without suppression by cooling of feedback from primary visual cortex (V1). Normally, magno and parvo LGN cells show a wide range over which their responses are proportional to stimulus contrast. Inactivation of V1 feedback causes LGN cells to become more nonlinear and less sensitive to high contrast than during normal conditions. Responses during V1 inactivation have a similar shape to those of retinal ganglion cells. We have also tested the properties of the so-called extended surround as they relate to cortical activity and to influences on responses to LGN stimulation. A model of this data suggests an interpretation in terms of two fnuctional components of feedback: a contrast-dependent component which dominates at high input contrast, and a constant baseline level of inhibitory feedback. We also show that the influence of the extended surround on the classical center mechanism is more complicated than a simple integration model.National Institutes of Health (EY-05156); Office of Naval Research (N00014-95-1-409