scholarly journals Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells

2020 ◽  
Vol 6 (22) ◽  
pp. eaba3742 ◽  
Author(s):  
Giulio Matteucci ◽  
Davide Zoccolan
Keyword(s):  
Computational Models ◽  
Visual Experience ◽  
Temporal Structure ◽  
Visual Environment ◽  
Postnatal Life ◽  
Simple Cells ◽  
Complex Cells ◽  
Temporal Continuity ◽  
Shape Tuning ◽  
Visual Cortical

Unsupervised adaptation to the spatiotemporal statistics of visual experience is a key computational principle that has long been assumed to govern postnatal development of visual cortical tuning, including orientation selectivity of simple cells and position tolerance of complex cells in primary visual cortex (V1). Yet, causal empirical evidence supporting this hypothesis is scant. Here, we show that degrading the temporal continuity of visual experience during early postnatal life leads to a sizable reduction of the number of complex cells and to an impairment of their functional properties while fully sparing the development of simple cells. This causally implicates adaptation to the temporal structure of the visual input in the development of transformation tolerance but not of shape tuning, thus tightly constraining computational models of unsupervised cortical learning.

Spatial Phase and the Temporal Structure of the Response to Gratings in V1

1998 ◽  
Vol 80 (2) ◽  
pp. 554-571 ◽  
Author(s):  
Jonathan D. Victor ◽  
Keith P. Purpura
Keyword(s):  
Spatial Frequency ◽  
Spike Train ◽  
Temporal Structure ◽  
Spike Timing ◽  
Dependent Manner ◽  
Data Set ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Contrast Information

Victor, Jonathan D. and Keith P. Purpura. Spatial phase and the temporal structure of the response to gratings in V1. J. Neurophysiol. 80: 554–571, 1998. We recorded single-unit activity of 25 units in the parafoveal representation of macaque V1 to transient appearance of sinusoidal gratings. Gratings were systematically varied in spatial phase and in one or two of the following: contrast, spatial frequency, and orientation. Individual responses were compared based on spike counts, and also according to metrics sensitive to spike timing. For each metric, the extent of stimulus-dependent clustering of individual responses was assessed via the transmitted information, H. In nearly all data sets, stimulus-dependent clustering was maximal for metrics sensitive to the temporal pattern of spikes, typically with a precision of 25–50 ms. To focus on the interaction of spatial phase with other stimulus attributes, each data set was analyzed in two ways. In the “pooled phases” approach, the phase of the stimulus was ignored in the assessment of clustering, to yield an index H pooled. In the “individual phases” approach, clustering was calculated separately for each spatial phase and then averaged across spatial phases to yield an index H indiv. H pooled expresses the extent to which a spike train represents contrast, spatial frequency, or orientation in a manner which is not confounded by spatial phase (phase-independent representation), whereas H indiv expresses the extent to which a spike train represents one of these attributes, provided spatial phase is fixed (phase-dependent representation). Here, representation means that a stimulus attribute has a reproducible and systematic influence on individual responses, not a neural mechanism for decoding this influence. During the initial 100 ms of the response, contrast was represented in a phase-dependent manner by simple cells but primarily in a phase-independent manner by complex cells. As the response evolved, simple cell responses acquired phase-independent contrast information, whereas complex cells acquired phase-dependent contrast information. Simple cells represented orientation and spatial frequency in a primarily phase-dependent manner, but also they contained some phase-independent information in their initial response segment. Complex cells showed primarily phase-independent representation of orientation but primarily phase-dependent representation of spatial frequency. Joint representation of two attributes (contrast and spatial frequency, contrast and orientation, spatial frequency and orientation) was primarily phase dependent for simple cells, and primarily phase independent for complex cells. In simple and complex cells, the variability in the number of spikes elicited on each response was substantially greater than the expectations of a Poisson process. Although some of this variation could be attributed to the dependence of the response on the spatial phase of the grating, variability was still markedly greater than Poisson when the contribution of spatial phase to response variance was removed.

scholarly journals Development of cortical orientation selectivity in the absence of visual experience with contour

2011 ◽  
Vol 106 (4) ◽  
pp. 1923-1932 ◽  
Author(s):  
Tomokazu Ohshiro ◽  
Shaista Hussain ◽  
Michael Weliky
Keyword(s):  
Cortical Neurons ◽  
Computational Models ◽  
Visual Experience ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Experimental Result ◽  
Full Development ◽  
Visual Inputs ◽  
Eye Opening ◽  
Visual Cortical

Visual cortical neurons are selective for the orientation of lines, and the full development of this selectivity requires natural visual experience after eye opening. Here we examined whether this selectivity develops without seeing lines and contours. Juvenile ferrets were reared in a dark room and visually trained by being shown a movie of flickering, sparse spots. We found that despite the lack of contour visual experience, the cortical neurons of these ferrets developed strong orientation selectivity and exhibited simple-cell receptive fields. This finding suggests that overt contour visual experience is unnecessary for the maturation of orientation selectivity and is inconsistent with the computational models that crucially require the visual inputs of lines and contours for the development of orientation selectivity. We propose that a correlation-based model supplemented with a constraint on synaptic strength dynamics is able to account for our experimental result.

Cross-correlation analysis of geniculostriate neuronal relationships in cats

1983 ◽  
Vol 49 (6) ◽  
pp. 1303-1318 ◽  
Author(s):  
K. Tanaka
Keyword(s):  
Cross Correlation ◽  
Striate Cortex ◽  
Receptive Fields ◽  
Cortical Cell ◽  
Correlation Technique ◽  
Simple Cells ◽  
Complex Cells ◽  
Cross Correlation Analysis ◽  
Special Complex ◽  
Visual Cortical

The organization of geniculate inputs to a cat's visual cortical cell was studied by a cross-correlation technique. Simultaneous extracellular recordings were made in the lateral geniculate nucleus and in the striate cortex, and neuronal connectivity between a geniculate cell and a striate cell was examined by cross-correlograms of their impulse discharges under photic stimuli. Of 243 pairs of geniculate and striate cells with overlapping receptive fields, 82 showed positive correlations with short (0.9-2.7 ms) delay times. The delays in 65 of the 82 pairs were short enough to infer that the geniculate cell exerted monosynaptic excitatory action on the striate cell. Monosynaptic excitations were found in all types of striate cells. Those in cells with exclusively an on area or an off area (E-on/off cells) or in simple cells originated mostly from X geniculate cells; those in special-complex cells originated exclusively from Y geniculate cells; and those in standard-complex cells arose from both X and Y geniculate cells. The convergence number from geniculate cells to an E-on/off or simple striate cell was estimated as more than 10, since about 1/10 of the discharges from an E-on/off or simple cell in response to a moving stimulus was correlated with discharges from a geniculate cell. A larger convergence number (more than 30) was obtained for complex cells. Convergence from 2 to 5 geniculate cells was actually demonstrated in 17 of the 32 striate cells, each of which was tested in pair with 3-14 geniculate cells. The converging inputs thus observed included both X and Y geniculate cells in one E-on, one simple, and three standard-complex cells. They included both on-center and off-center geniculate cells in one simple, one special-complex, and five standard-complex cells. Under stimulation with a stationary light slit, the center fields but not the surround fields of geniculate cells were found to contribute to the receptive fields of the simple striate cells. However, the surround fields of geniculate cells contributed to the subliminal response areas flanking the central areas of E-on/off cells. The center fields of the geniculate cells also contributed to the central areas of the E-on/off cells. These observations suggest different models for simple cells and E-on/off cells as regards the organization of their geniculate inputs; simple cells may receive inputs from both on-center and off-center geniculate cells, but E-on/off cells receive inputs only from one or the other of them.

Patterns in the discharge of simple and complex visual cortical cells

1981 ◽  
Vol 212 (1188) ◽  
pp. 279-297 ◽  
Keyword(s):  
Spatial Frequency ◽  
Cortical Neurons ◽  
Linear Time ◽  
Cell Activity ◽  
Invariance Property ◽  
Statistical Structure ◽  
Cortical Cells ◽  
Simple Cells ◽  
Complex Cells ◽  
Visual Cortical

The activity of visual cortical neurons (area 17) was recorded in anaesthetized cats in response to sinusoidal drifting gratings. The statistical structure of the discharge of simple and complex cells has been studied as a function of the various parameters of a drifting grating: spatial frequency, orientation, drifting velocity and contrast. For simple cells it has been found that the interspike interval distributions in response to drifting gratings of various spatial frequencies differ only by a time scale factor. They can be reduced to a unique distribution by a linear time transformation. Variations in the spatial frequency of the grating induce variations in the mean firing rate of the cell but leave unchanged the statistical structure of the discharge. On the contrary, the statistical structure of simple cell activity changes when the contrast or the velocity of the stimulus is varied. For complex cells it has been found that the invariance property described above for simple cells is not valid. Complex cells present in their activity in response to visual stimuli two different firing patterns: spikes organized in clusters and spikes that do not show this organization (‘isolated spikes’). The clustered component is the only component of the complex cell discharge that is tuned for spatial frequency and orientation, while the isolated spike component is correlated with the contrast of the stimulus.

Direction selectivity of complex cells in a comparison with simple cells

1975 ◽  
Vol 38 (6) ◽  
pp. 1524-1540 ◽  
Author(s):  
A. W. Goodwin ◽  
G. H. Henry
Keyword(s):  
Spontaneous Activity ◽  
Visual Information ◽  
Striate Cortex ◽  
Cell Types ◽  
Direction Selectivity ◽  
Complex Cell ◽  
Simple Cells ◽  
Complex Cells ◽  
Sequential Processing

Following our earlier study on direction selectivity in simple cells (5), the present findings on complex cells made it possible to compare the direction selectivity in the two types of striate cell. Common properties were found in the dimension of the smallest stimulus displacement giving a direction-selective response and in the role of inhibition in suppressing the response as the stimulus moved in the nonpreferred direction. However, the effectiveness of this inhibition varied in the two cell types since it suppressed both driven and spontaneous activity in the simple cell, but only driven firing in the complex cell. It is argued that direction selectivity must enter the response before the complex cell if the inhibition responsible for it's generation fails to influence the spontaneous activity of the cell. The consequences of this finding are considered in the terms of parallel or sequential processing of visual information in striate cortex.

Receptive-field characteristics of neurons in cat striate cortex: Changes with visual field eccentricity

1976 ◽  
Vol 39 (3) ◽  
pp. 512-533 ◽  
Author(s):  
J. R. Wilson ◽  
S. M. Sherman
Keyword(s):  
Visual Field ◽  
Receptive Field ◽  
Striate Cortex ◽  
Ocular Dominance ◽  
Receptive Fields ◽  
Direction Selectivity ◽  
Orientation Selectivity ◽  
Simple Cells ◽  
Complex Cells ◽  
Cat Striate Cortex

1. Receptive-field properties of 214 neurons from cat striate cortex were studied with particular emphasis on: a) classification, b) field size, c) orientation selectivity, d) direction selectivity, e) speed selectivity, and f) ocular dominance. We studied receptive fields located throughtout the visual field, including the monocular segment, to determine how receptivefield properties changed with eccentricity in the visual field.2. We classified 98 cells as "simple," 80 as "complex," 21 as "hypercomplex," and 15 in other categories. The proportion of complex cells relative to simple cells increased monotonically with receptive-field eccenticity.3. Direction selectivity and preferred orientation did not measurably change with eccentricity. Through most of the binocular segment, this was also true for ocular dominance; however, at the edge of the binocular segment, there were more fields dominated by the contralateral eye.4. Cells had larger receptive fields, less orientation selectivity, and higher preferred speeds with increasing eccentricity. However, these changes were considerably more pronounced for complex than for simple cells.5. These data suggest that simple and complex cells analyze different aspects of a visual stimulus, and we provide a hypothesis which suggests that simple cells analyze input typically from one (or a few) geniculate neurons, while complex cells receive input from a larger region of geniculate neurons. On average, this region is invariant with eccentricity and, due to a changing magnification factor, complex fields increase in size with eccentricity much more than do simple cells. For complex cells, computations of this geniculate region transformed to cortical space provide a cortical extent equal to the spread of pyramidal cell basal dendrites.

Binocular Combination of Contrast Signals by Striate Cortical Neurons in the Monkey

1997 ◽  
Vol 78 (1) ◽  
pp. 366-382 ◽  
Author(s):  
Earl L. Smith ◽  
Yuzo Chino ◽  
Jinren Ni ◽  
Han Cheng
Keyword(s):  
Cortical Neurons ◽  
Striate Cortex ◽  
Receptive Fields ◽  
Spatial Summation ◽  
Simple Cells ◽  
Complex Cells ◽  
Threshold Response ◽  
Binocular Interactions ◽  
Specific Complex ◽  
Left And Right

Smith, Earl L., III, Yuzo Chino, Jinren Ni, and Han Cheng. Binocular combination of contrast signals by striate cortical neurons in the monkey. J. Neurophysiol. 78: 366–382, 1997. With the use of microelectrode recording techniques, we investigated how the contrast signals from the two eyes are combined in individual cortical neurons in the striate cortex of anesthetized and paralyzed macaque monkeys. For a given neuron, the optimal spatial frequency, orientation, and direction of drift for sine wave grating stimuli were determined for each eye. The cell's disparity tuning characteristics were determined by measuring responses as a function of the relative interocular spatial phase of dichoptic stimuli that consisted of the optimal monocular gratings. Binocular contrast summation was then investigated by measuring contrast response functions for optimal dichoptic grating pairs that had left- to right-eye interocular contrast ratios that varied from 0.1 to 10. The goal was to determine the left- and right-eye contrast components required to produce a criterion threshold response. For all functional classes of cortical neurons and for both cooperative and antagonistic binocular interactions, there was a linear relationship between the left- and right-eye contrast components required to produce a threshold response. Thus, for example for cooperative binocular interactions, a reduction in contrast to one eye was counterbalanced by an equivalent increase in contrast to the other eye. These results showed that in simple cells and phase-specific complex cells, the contrast signals from the two eyes were linearly combined at the subunit level before nonlinear rectification. In non-phase-specific complex cells, the linear binocular convergence of contrast signals could have taken place either before or after the rectification process, but before spike generation. In addition, for simple cells, vector analysis of spatial summation showed that the inputs from the two eyes were also combined in a linear manner before nonlinear spike-generating mechanisms. Thus simple cells showed linear spatial summation not only within and between subregions in a given receptive field, but also between the left- and right-eye receptive fields. Overall, the results show that the effectiveness of a stimulus in producing a response reflects interocular differences in the relative balance of inputs to a given cell, however, the eye of origin of a light-evoked signal has no specific consequence.

FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES

2009 ◽  
Vol 05 (01) ◽  
pp. 265-286
Author(s):  
MUSTAFA C. OZTURK ◽  
JOSE C. PRINCIPE
Keyword(s):  
Computational Models ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Reservoir Computing ◽  
Short Term ◽  
System Parameters ◽  
History Of ◽  
Spatio Temporal ◽  
Universal Approximators ◽  
Theoretical Results

Walter Freeman in his classic 1975 book "Mass Activation of the Nervous System" presented a hierarchy of dynamical computational models based on studies and measurements done in real brains, which has been known as the Freeman's K model (FKM). Much more recently, liquid state machine (LSM) and echo state network (ESN) have been proposed as universal approximators in the class of functionals with exponential decaying memory. In this paper, we briefly review these models and show that the restricted K set architecture of KI and KII networks share the same properties of LSM/ESNs and is therefore one more member of the reservoir computing family. In the reservoir computing perspective, the states of the FKM are a representation space that stores in its spatio-temporal dynamics a short-term history of the input patterns. Then at any time, with a simple instantaneous read-out made up of a KI, information related to the input history can be accessed and read out. This work provides two important contributions. First, it emphasizes the need for optimal readouts, and shows how to adaptively design them. Second, it shows that the Freeman model is able to process continuous signals with temporal structure. We will provide theoretical results for the conditions on the system parameters of FKM satisfying the echo state property. Experimental results are presented to illustrate the validity of the proposed approach.

scholarly journals Spatial and Temporal Features of Synaptic to Discharge Receptive Field Transformation in Cat Area 17

2010 ◽  
Vol 103 (2) ◽  
pp. 677-697 ◽  
Author(s):  
Lionel G. Nowak ◽  
Maria V. Sanchez-Vives ◽  
David A. McCormick
Keyword(s):  
Spatial Frequency ◽  
Response Latency ◽  
Frequency Tuning ◽  
Receptive Fields ◽  
Width Ratio ◽  
Area 17 ◽  
Simple Cells ◽  
Complex Cells ◽  
Temporal Features ◽  
Simple And Complex Cells

The aim of the present study was to characterize the spatial and temporal features of synaptic and discharge receptive fields (RFs), and to quantify their relationships, in cat area 17. For this purpose, neurons were recorded intracellularly while high-frequency flashing bars were used to generate RFs maps for synaptic and spiking responses. Comparison of the maps shows that some features of the discharge RFs depended strongly on those of the synaptic RFs, whereas others were less dependent. Spiking RF duration depended poorly and spiking RF amplitude depended moderately on those of the underlying synaptic RFs. At the other extreme, the optimal spatial frequency and phase of the discharge RFs in simple cells were almost entirely inherited from those of the synaptic RFs. Subfield width, in both simple and complex cells, was less for spiking responses compared with synaptic responses, but synaptic to discharge width ratio was relatively variable from cell to cell. When considering the whole RF of simple cells, additional variability in width ratio resulted from the presence of additional synaptic subfields that remained subthreshold. Due to these additional, subthreshold subfields, spatial frequency tuning predicted from synaptic RFs appears sharper than that predicted from spiking RFs. Excitatory subfield overlap in spiking RFs was well predicted by subfield overlap at the synaptic level. When examined in different regions of the RF, latencies appeared to be quite variable, but this variability showed negligible dependence on distance from the RF center. Nevertheless, spiking response latency faithfully reflected synaptic response latency.

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