Intracortical connections are not required for oscillatory activity in the visual cortex

1997 ◽  
Vol 14 (6) ◽  
pp. 963R-979R ◽  
Author(s):  
Geoffrey M. Ghose ◽  
Ralph D. Freeman
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Close Agreement ◽  
Power Spectra ◽  
Spike Trains ◽  
Cortical Layer ◽  
Oscillatory Activity ◽  
Complex Cells ◽  
Firing Rates ◽  
Simple And Complex Cells

Abstractarises from the integration of signals from strongly oscillatory cells within the LGN. The model also predicts the incidence of 50-Hz oscillatory cells within the cortex. Oscillatory discharge around 30 Hz is explained in a second model by the presence of intrinsically oscillatory cells within cortical layer 5. Both models generate spike trains whose power spectra and mean firing rates are in close agreement with experimental observations of simple and complex cells. Considered together, the two models can largely account for the nature and incidence of oscillatory discharge in the cat's visual cortex. The validity of these models is consistent with the possibility that oscillations are generated independently of intracortical interactions. Because these models rely on intrinsic stimulus-independent oscillators within the retina and cortex, the results further suggest that oscillatory activity within the cortex is not necessarily associated with the processing of high-order visual information.

Intracortical connections are not required for oscillatory activity in the visual cortex

1997 ◽  
Vol 14 (5) ◽  
pp. 963-979 ◽  
Author(s):  
Geoffrey M. Ghose ◽  
Ralph D. Freeman
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Close Agreement ◽  
Power Spectra ◽  
Cortical Layer ◽  
Oscillatory Activity ◽  
Complex Cells ◽  
Firing Rates ◽  
Cortical Oscillations ◽  
Simple And Complex Cells

AbstractSynchronized oscillatory discharge in the visual cortex has been proposed to underlie the linking of retinotopically disparate features into perceptually coherent objects. These proposals have largely relied on the premise that the oscillations arise from intracortical circuitry. However, strong oscillations within both the retina and the lateral geniculate nucleus (LGN) have been reported recently. To evaluate the possibility that cortical oscillations arise from peripheral pathways, we have developed two plausible models of single cell oscillatory discharge that specifically exclude intracortical networks. In the first model, cortical oscillatory discharge near 50 Hz in frequency arises from the integration of signals from strongly oscillatory cells within the LGN. The model also predicts the incidence of 50-Hz oscillatory cells within the cortex. Oscillatory discharge around 30 Hz is explained in a second model by the presence of intrinsically oscillatory cells within cortical layer 5. Both models generate spike trains whose power spectra and mean firing rates are in close agreement with experimental observations of simple and complex cells. Considered together, the two models can largely account for the nature and incidence of oscillatory discharge in the cat's visual cortex. The validity of these models is consistent with the possibility that oscillations are generated independently of intracortical interactions. Because these models rely on intrinsic stimulus-independent oscillators within the retina and cortex, the results further suggest that oscillatory activity within the cortex is not necessarily associated with the processing of high-order visual information.

Oscillatory discharge in the visual system: does it have a functional role?

1992 ◽  
Vol 68 (5) ◽  
pp. 1558-1574 ◽  
Author(s):  
G. M. Ghose ◽  
R. D. Freeman
Keyword(s):  
Visual Cortex ◽  
Functional Role ◽  
Power Spectra ◽  
Oscillatory Activity ◽  
Frequency Noise ◽  
Visual Response ◽  
Neural Firing ◽  
Frequency Range ◽  
Complex Cells ◽  
Oscillation Analysis

1. The discharge of individual neurons in the visual cortex and lateral geniculate nucleus (LGN) of anesthetized and paralyzed cats and kittens was examined for the presence of oscillatory activity. Neural firing was evoked through the monoptic or dichoptic presentation of drifting gratings and random sequences of flashed bars. The degree to which different oscillatory frequencies were present in neural discharge was quantified by computation of the power spectra of impulse train responses. 2. Action potentials from single cells were recorded extracellularly and isolated on the basis of amplitude. Receptive-field properties of the neurons under study were characterized initially by their discharge in response to gratings of sinusoidal luminance. By varying orientation and spatial frequency, optimal stimulus characteristics were determined. Oscillation analysis was performed on spike trains acquired during repeated presentations of the optimal stimulus by identification of power spectra peaks in the frequency range of rhythmic potentials observed in electroencephalograph studies (30-80 Hz). The amplitude and frequency of the largest peak in this range was used to characterize oscillatory strength and frequency. All discharge in which the peak amplitude exceeded the high-frequency noise by a factor > 1.5 was classified as oscillatory. 3. Of the 342 cortical cells examined, 147 cells displayed oscillatory activity in the 30 to 80-Hz range during portions of their visual response. Sixty out of 169 simple cells, 82 out of 166 complex cells, and 5 out of 7 special complex cells exhibited oscillations. There was no laminar bias in the distribution of oscillatory cells; the proportions of oscillatory cells were similar in all layers. All oscillatory discharge was variable with respect to frequency and strength between successive presentations of the same optimal stimulus. In as little as 10 s, for example, peak frequencies shifted by a factor of two. For many cells, these trial-to-trial variations obscured detectable oscillations when all trials were averaged together. 4. The potential role of neuronal maturation in the generation of oscillatory activity was investigated by studying neuronal responses from kittens at 4 wk postnatal. Of the 80 kitten cells studied, 27 exhibited oscillatory discharge. Although oscillations in the kitten visual cortex spanned the same frequency range as that seen in the adult, oscillations in the midfrequency range (36-44 Hz) are more common in the adult cortex. 5. To explore the possibility that oscillations might play a functional role in vision, we investigated the dependence of oscillations on different stimulus parameters.(ABSTRACT TRUNCATED AT 400 WORDS)

A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

2003 ◽  
Vol 15 (10) ◽  
pp. 2399-2418 ◽  
Author(s):  
Zhao Songnian ◽  
Xiong Xiaoyun ◽  
Yao Guozheng ◽  
Fu Zhi
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Visual Information ◽  
Spike Trains ◽  
Spike Timing ◽  
Contour Detection ◽  
System Level ◽  
Neuronal Spike ◽  
Neuronal Spike Trains ◽  
External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

Selective effects of aging on simple and complex cells in primary visual cortex of rhesus monkeys

2012 ◽  
Vol 1470 ◽  
pp. 17-23 ◽  
Author(s):  
Zhen Liang ◽  
Hongxin Li ◽  
Yun Yang ◽  
Guangxing Li ◽  
Yong Tang ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Rhesus Monkeys ◽  
Complex Cells ◽  
Simple And Complex Cells ◽  
Effects Of Aging ◽  
Selective Effects

Effect of electrical stimulation of locus coeruleus on the activity of neurons in the cat visual cortex

1989 ◽  
Vol 62 (4) ◽  
pp. 946-958 ◽  
Author(s):  
H. Sato ◽  
K. Fox ◽  
N. W. Daw
Keyword(s):  
Electrical Stimulation ◽  
Visual Cortex ◽  
Adrenergic Receptor ◽  
Suppressive Effect ◽  
Cortical Layer ◽  
Facilitatory Effect ◽  
Receptor Antagonists ◽  
Complex Cells ◽  
Beta Receptors ◽  
Stimulation Of

1. We studied the effect of electrically stimulating the locus coeruleus (LC) and iontophoresing noradrenergic antagonists on visual responses and spontaneous activity of individual cells in the cat primary visual cortex. 2. A bilateral projection from LC to visual cortex was demonstrated anatomically, by retrograde labeling using horseradish peroxidase. Where electrical stimulation of both ipsilateral and contralateral LC affected a cortical neuron, the effect induced by stimulating each side was similar. 3. One hundred and two cells were recorded in area 17: 52% of them had their activity suppressed and 36% had their activity facilitated by LC stimulation. The suppressive effect was predominant in cortical layers II + III and IV, whereas most cells in layer V and one-half of the cells in layer VI were facilitated by LC stimulation. This suggests that LC neurons innervate each cortical layer in a different manner. 4. Simple and complex cells were equally sensitive to LC stimulation. For simple cells, the suppressive effect of LC stimulation was dominant throughout all layers. For complex cells, the suppressive effect was dominant in layers II + III and IV, whereas the facilitatory effect was dominant in layers V and VI. 5. The suppressive effect of LC stimulation was blocked by iontophoretic application of beta-adrenergic receptor antagonists and the facilitatory effect was blocked by either alpha- or beta-adrenergic receptor antagonists. 6. Nonselective alpha-, and selective alpha 1- and alpha 2-receptor antagonists suppressed visual and spontaneous activity in almost all neurons tested, suggesting that these receptors are either facilitatory at a postsynaptic site or inhibitory at a site presynaptic to an inhibitory synapse in the visual cortex. 7. beta-Receptor antagonists facilitated activity in 45% and suppressed activity in 36% of the cells tested, suggesting there are both suppressive and facilitatory types of beta-receptors. 8. The effectiveness of alpha- and beta-antagonists on the activity of neurons without LC stimulation also suggested that spontaneously released noradrenaline activated noradrenergic receptors in the visual cortex even in the anesthetized and paralyzed cat. 9. In most cells tested, both alpha- and beta-receptor antagonists exerted effects on single neurons suggesting that endogenous noradrenaline acts on both alpha- and beta-receptors on the same cell. 10. The activation of LC did not improve the signal- (visual response)to-noise (spontaneous discharge) ratio of neurons in the visual cortex. 11. LC seemed to control the activity of each cortical layer differently, by activating different kinds of noradrenergic receptors in different layers.

scholarly journals Retinal origin of simple and complex cells in visual cortex

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S281
Author(s):  
Gwangsu Kim ◽  
Jaeson Jang ◽  
Se-Bum Paik
Keyword(s):  
Visual Cortex ◽  
Complex Cells ◽  
Simple And Complex Cells
Sign in / Sign up

Export Citation Format

Share Document