Responses of V1 Neurons to Two-Dimensional Hermite Functions

Victor, Jonathan D.; Mechler, Ferenc; Repucci, Michael A.; Purpura, Keith P.; Sharpee, Tatyana O.

doi:10.1152/jn.00498.2005

Cited by 43 publications

(58 citation statements)

References 100 publications

(128 reference statements)

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“…But the responses of layer 2/3 neurons are more typically nonlinear than layer 4 responses. The discrepancy between our receptive field measurements and other studies that found only single subregions in contrast-polarity-sensitive neurons (Bishop et al, 1971;Schiller et al, 1976;Hirsch et al, 2002;Kagan et al, 2002;Martinez et al, 2005) illustrates an important point about nonlinear responses: neurons with dynamic nonlinearities in their responses may have different-looking first-order receptive fields when different stimulus sets are used (Victor et al, 2006). We believe that the first-order receptive field structure that we measured here is different from previous studies, not because we recorded from a new class of neurons but because we used a different stimulus set to calculate first-order responses from nonlinear neurons.…”

Section: Relationship To Previous Work On Cells Responsive To a Singlcontrasting

confidence: 99%

“…We believe that the first-order receptive field structure that we measured here is different from previous studies, not because we recorded from a new class of neurons but because we used a different stimulus set to calculate first-order responses from nonlinear neurons. First-order responses even from simple cells need to be interpreted cautiously if nonlinear responses are common in V1 (David et al, 2004;Rust et al, 2005;Victor et al, 2006). It seems likely that even in simple cells, second-order (Jacobson et al, 1993;Rust et al, 2005) and higher-order interactions will need to be analyzed to fully characterize V1 neurons, particularly in layer 2/3.…”

Section: Relationship To Previous Work On Cells Responsive To a Singlmentioning

confidence: 99%

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A Dynamic Nonlinearity and Spatial Phase Specificity in Macaque V1 Neurons

Williams¹,

Shapley²

2007

J. Neurosci.

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While studying the visual response dynamics of neurons in the macaque primary visual cortex (V1), we found a nonlinearity of temporal response that influences the visual functions of V1 neurons. Simple cells were recorded in all layers of V1; the nonlinearity was strongest in neurons located in layer 2/3. We recorded the spike responses to optimal sinusoidal gratings that were displayed for 100 ms, a temporal step response. The step responses were measured at many spatial phases of the grating stimulus. To judge whether simple cell behavior was consistent with linear temporal integration, the decay of the 100 ms step response at the preferred spatial phase was used to predict the step response at the opposite spatial phase. Responses in layers 4B and 4C were mostly consistent with a linear-plus-staticnonlinearity cascade model. However, this was not true in layer 2/3 where most cells had little or no step responses at the opposite spatial phase. Many layer 2/3 cells had transient preferred-phase responses but did not respond at the offset of the opposite-phase stimuli, indicating a dynamic nonlinearity. A different stimulus sequence, rapidly presented random sinusoids, also produced the same effect, with layer 2/3 simple cells exhibiting elevated spike rates in response to stimuli at one spatial phase but not 180°away. The presence of a dynamic nonlinearity in the responses of V1 simple cells indicates that first-order analyses often capture only a fraction of neuronal behavior. The visual implication of our results is that simple cells in layer 2/3 are spatial phase-sensitive detectors that respond to contrast boundaries of one sign but not the opposite.

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Section: Relationship To Previous Work On Cells Responsive To a Singlcontrasting

confidence: 99%

Section: Relationship To Previous Work On Cells Responsive To a Singlmentioning

confidence: 99%

A Dynamic Nonlinearity and Spatial Phase Specificity in Macaque V1 Neurons

Williams¹,

Shapley²

2007

J. Neurosci.

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“…1, the ventral stream's hierarchy may not be as strict. For instance there may be units with relatively complex receptive fields already in V1 (Mahon and DeValois, 2001;Victor et al, 2006). A mixture of cells with various levels of selectivity has also commonly been reported in V2, V4, and IT (Tanaka, 1996;Hegde´and van Essen, 2007).…”

Section: Two Basic Operations For Selectivity and Invariancementioning

confidence: 98%

A quantitative theory of immediate visual recognition

Serre

Kreiman

Kouh

et al. 2007

Progress in Brain Research

200

182

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Human and non-human primates excel at visual recognition tasks. The primate visual system exhibits a strong degree of selectivity while at the same time being robust to changes in the input image. We have developed a quantitative theory to account for the computations performed by the feedforward path in the ventral stream of the primate visual cortex. Here we review recent predictions by a model instantiating the theory about physiological observations in higher visual areas. We also show that the model can perform recognition tasks on datasets of complex natural images at a level comparable to psychophysical measurements on human observers during rapid categorization tasks. In sum, the evidence suggests that the theory may provide a framework to explain the first 100-150 ms of visual object recognition. The model also constitutes a vivid example of how computational models can interact with experimental observations in order to advance our understanding of a complex phenomenon. We conclude by suggesting a number of open questions, predictions, and specific experiments for visual physiology and psychophysics.

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“…These experiments ("stepping": intentionally moving the recording tetrode in 5-to 15-m steps) were interleaved between a larger battery of experiments devoted to receptive field analysis that were carried out with standard tetrode recording (Mechler et al 2007;Victor et al 2006). The details of these preparations were described in full there.…”

Section: Physiological Preparationmentioning

confidence: 99%

Three-dimensional localization of neurons in cortical tetrode recordings

Mechler

Victor

Ohiorhenuan

et al. 2011

Journal of Neurophysiology

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The recording radius and spatial selectivity of an extracellular probe are important for interpreting neurophysiological recordings but are rarely measured. Moreover, an analysis of the recording biophysics of multisite probes (e.g., tetrodes) can provide for source characterization and localization of spiking single units, but this capability has remained largely unexploited. Here we address both issues quantitatively. Advancing a tetrode (Ϸ40-m contact separation, tetrahedral geometry) in 5-to 10-m steps, we repeatedly recorded extracellular action potentials (EAPs) of single neurons in the visual cortex. Using measured spatial variation of EAPs, the tetrodes' measured geometry, and a volume conductor model of the cortical tissue, we solved the inverse problem of estimating the location and the size of the equivalent dipole model of the spike generator associated with each neuron. Half of the 61 visual neurons were localized within a radius of Ϸ100 m and 95% within Ϸ130 m around the tetrode tip (i.e., a large fraction was much further than previously thought). Because of the combined angular sensitivity of the tetrode's leads, location uncertainty was less than one-half the cell's distance. We quantified the spatial dependence of the probability of cell isolation, the isolated fraction, and the dependence of the recording radius on probe size and equivalent dipole size. We also reconstructed the spatial configuration of sets of simultaneously recorded neurons to demonstrate the potential use of 3D dipole localization for functional anatomy. Finally, we found that the dipole moment vector, surprisingly, tended to point toward the probe, leading to the interpretation that the equivalent dipole represents a "local lobe" of the dendritic arbor. recording radius; extracellular action potential; multisite recording; inverse problem; equivalent dipole; lead field theory MULTICONTACT RECORDINGS HAVE HIGH YIELD (Blanche et al. 2005;Csicsvari et al. 2003;Eckhorn and Thomas 1993;Gray et al. 1995;McNaughton et al. 1983) (Buzsaki and Kandel 1998;Drake et al. 1988;Henze et al. 2000) show, the shape and size of the extracellular action potential (EAP) waveform depends on the relative position of cell and probe. Thus, these recordings carry spatial information about spike sources that is not available in single electrode records. However, this spatial information is typically not exploited, because extracting it requires solution of an inverse problem, deducing the position and the size of the current source of a spiking neuron from measurements of its EAP at multiple locations.Here, we solve this inverse problem by modeling the spiking neuron with a single current dipole. The choice of a dipole model rests on reasoning presented in detail in a companion paper and its supplemental material (Mechler and Victor 2011). Briefly, although the membrane currents of a spiking neuron constitute a genuinely distributed current source, the resulting extracellular field is well approximated by that of a dipole beyond a minimum distance a...

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Responses of V1 Neurons to Two-Dimensional Hermite Functions

Cited by 43 publications

References 100 publications

A Dynamic Nonlinearity and Spatial Phase Specificity in Macaque V1 Neurons

A Dynamic Nonlinearity and Spatial Phase Specificity in Macaque V1 Neurons

A quantitative theory of immediate visual recognition

Three-dimensional localization of neurons in cortical tetrode recordings

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