Neuronal latency - Generalization to other features

6.2 Generalization to other features

7.1.2 Neuronal latency

Based on a RC circuit, the relationship between frequency of a spike train and latency has been established. Analogous relationships have also been discussed and demonstrated to exist for the photoreceptors and a model of the retina. While nonlinearity pervades in these relationships, for the sake of simplicity they have been reduced to linear functions. It remains to determine what are the effects of such an oversimplification.

9.1.3 Architectures

The feedfonvard and recursive architectures could both c o n f m that asynchrony, stem- ming from differences in signal amplitudes, helps to solve a specific problem, yet easily generalizable. The first architecture (feedfonvard) was shown to be restricted to extracting edges in an image and, thus, was not representative of human perception which can sense the surface of objects. For that reason, a diffusive stage was added to this architecture to remedy to this limitation (forming the second architecture). Given that visual information was considered to arrive asynchronously, this architecture required a feedback to continuously reevaluate the co- efficients needed by the diffusive stage. In consequence, this architecture was referred to as recursive. Performances of this model were found to be satisfying in the tasks considered. More- over, its ability to explain perceptual tasks involving static images that synchronous models fail to analyze effectively, represents a major improvement in the understanding of the early visual system.

7.1.4 Dynamic transinformation

When using an asynchronous model, an extension of the notion of transinformation to asynchronous signals has given a satisfying explanation of the increase in performance of information extraction for images strongly corrupted by Gaussian and uniform noise. The dy- narnic transinformation has been applied to two symbols representing, for instance, a background and a foreground. In CHAPTER 6 the problem of segmentation has also been ad- dressed, representing an extension of the figure-ground separation problem to more that only two symbols. The preliminary results of such a segmentation also indicate an increase in performance. Nevertheless, to obtain more satisfactory results from complex scenes, a better edge estimation will have to be designed.

7.2 Dissertation on asynchrony

7.2.1 A usefull concept

The concept of dynamic transinformation aimed at proving that a synchronous approach can compromise the performances of a task by mixing all information originally temporally structured. Perceptual performance of the human visual system can indeed be better matched by an asynchronous approach. Henceforth, it should not be ignored that a static image creates a dynamic data flow. Furthermore, if the temporal precedence applies to the early part of visual processing, it might also be questioned whether higher visual function could not benefit from it. This point is further discussed in section 7.3.

Apart from the processing of static images, the winner-take-all neuronal structure also

ruture work 145 demonstrated the importance of the temporal precedence. Thanks to this structure, it could be concluded that the dynarnical properties of a neuronal structure are dependent on time differences in the arrival of input signals. In particular, when the strongest signals appears first, the solution was shown to be faster to settle.

The temporal precedence has, nevertheless, the disadvantage of being demanding in terms of computer resources. By opposition, the brain is not penalized by the time dimension as its neuronal structures are wired and continuously process the data flow. The only temporal limitation of neuronal structures is the rate of temporal variations applied to their inputs, a limitation which has its origin in the leaky integrator nature of neurons.

7.2.2 Ontological and philosophical arguments

Whether two events can emerge simultaneously, or in synchrony, may be questioned on the basis that the notion of simultaneousness depends on the scale of analysis, thus on the Sam- pling time. In the notion of synchrony is contained an artificiality intended for conceptualizing a reality which, often, escapes our comprehension. Reintroducing asynchrony in models is thus justified only if this notion introduces new concepts. Those presented in this thesis (i.e. dynamic data flow and temporal precedence), I hope, have been convincingly shown to be well-founded.

If asynchrony is accepted as being natural, by opposition to synchrony, its origin must be sought. Signals with differing amplitudes or strengths were demonstrated to be one cause of asynchrony. In particular, it was found that the higher the amplitude the shorter the latency of neuronal responses. Does physics dictate this relation? In term of energy, this assumption appears reasonable. Indeed, the notion of amplitude exists only through the flux of energy which, in turn, is dependent on time. The higher the amplitude of a signal, the higher the flux of energy and, thus, the shorter the time required to transmit a fixed amount of energy.

Less obvious is the reason why neuronal structures should always benefit from such a relationship and not from the opposite one, which would be the higher the amplitude, the longer the neuronal latency. This latter and artificial situation has been tested on the winner-take-all neuronal structure, resulting in a net increase in the convergence time to reach a stable state.

Consequence of this increase is obviously a decrease in the performances of the function performed by this structure.

Survival of species depends on their aptitudes to react quickly to fast perceptual variations or to strong sensations. Both situations imply sources of consequent energy and thus it seems logical that evolution gained advantage in a faster processing for signals coming first, that is to say, those corresponding to greater amount of energy. It is not the habit of nature to evolve against the laws that physics dictates and the question of temporal precedence, a resultant of asynchrony, seems once more to confirm this fact.

7.3 Future work

7.3.1 Introduction

In the context of this work, I see three major domains where future work is particularly in- dispensable. First, regarding psychological aspects where the concept of asynchrony should be

140 C H M l ' E K 7. Overall conclusions tested on the human visual system. Second, regarding the dynamics of neuronal structures where a solution to the contour completion problem should be sought. Third, in computer vision where solutions to analyze complex images have not yet been found. Some aspects of these three domains are successively discussed in

5

7.3.2, 7.3.3, and

5

7.3.4.

7.3.2 Psychological experiments

The hypothesis of the existence of a visual data flow was judged to be well-founded in view of the satisfactory results obtained from the recursive model. Nevertheless, it remains that there is no certitude in the validity of this model. One way of deciding upon this issue is to test asynchrony on the human visual system. To this end, I propose the following experiment.

The hypothesis of asynchrony and, thus, of the existence of a visual data flow, is taken as valid and its origin to be in luminance differences. Consequently, when a subject looks, for instance, at the iso-average figure/ground image defined in CHAPTER 6, he will be able to dis- tinguish the figure from the background only (it is an hypothesis) because the higher luminance values are perceived first by its early visual system. In this line of thought, if all regions of this image are forced to reach the early visual system in synchrony, the perception of the figure should disappear. For this experiment to be possible, I propose first to determine for every luminance value its intrinsic latency, and second to artificially delay the perception of regions according to their luminance.

To illustrate this procedure, consider the iso-average image described in Figure 6.8. In this image there are 5 regions of different luminance values. Imagine that the measured latencies for these luminances are, for a human subject, those found in the table in Figure 7.1. Then, the column "AT", for absolute time, would specify when a region of a given luminance should ap- pear on the screen. The effect of this procedure would contrive to receive all visual information in synchrony (in this case after 40 ms). Performance comparisons between normal perception (all regions of the image appearing at once on a screen) and artificial perception (regions of lower luminance appearing first, followed by those of higher luminance, according to their latency) would allow a valuable evaluation of the initial hypothesis,

luminance

I

latency AT

;I

Figure 7.1 : Hypothetical measures of latency to illustrate the experimental principle. The relationship between luminance value and latency should be governed by the kind of function presented in section 4.1. The column AT specifies the absolute time when the regions of corresponding luminance value should be displayed on the screen.

Note that the sum of a latency value and of an AT value (on the same line) always gives 40 ms. The resultant effect of this procedure would be that 40 ms after the regions of luminance value 0 have been displayed, all visual infor- mation would reach the visual system in synchrony.

7.33 Dynamics and neuronal structures

This work has been deliberately limited to address only one aspect of temporal analysis:

asynchrony. And still, not all the concepts related to this aspect have been explored. For instance, one concept only outlined was the coherency of neuronal signals. A substitute for modeling neuronal coherency was a vectorial method which, although involving nonlinear functions, could not be compared with the coincident fuing performed by neurons (see also the so- called synfire chains in Abeles 1982).

While an architecture could be conceived to extract edges and smoothen surfaces, the problem of contour completion was not dealt with. Only ad hoc methods were proposed for per- forming crude cooperative and competitive mechanisms. Contour completion is a difficult problem which, as for the determination of (physical) edges, should not be based on heuristic methods and a plethora of parameters to be solved. Instead, at the example of the diffusive process, only a few parameters should be required. Furthermore, their effects should be continu- ous, not at the image of thresholds, and possibly fixed on the basis of psychophysical experiments. Also, I see in the dynamics of neuronal structures a vast range of possibility where a solution to the problem of contour completion could be found. In particular, the oscillatory mode could accomplish nonlinear interactions between elementary units. Such interactions could indeed underlie the generation of illusory contours, one aspect of contour completion.

This field has still to be explored. Furthermore, it would be interesting to analyze the effect of asynchronous entries on the behavior of such oscillatory mode.

7.3.4 Generalization of asynchrony

Depending on the visual functions where asynchrony would operate, a generalization of the temporal. precedence paradigm could have various consequences. For the early visual system, the effects of asynchrony (in a visual data flow) can be summarized as follows:

increase in the performances of the figurelground separation problem, generalizable to the segmentation problem;

temporal precedence of objects of higher luminance;

* temporal precedence of key-points and other features.

A direct consequence of the temporal precedence of visual features coming out of the early stages of the visual system (beyond Vl), could be to attract the attention of higher centers to particular regions "perceived" first. Thus, the temporal precedence paradigm would result in pruning out the background and objects whose features do not elicit early responses. This pruning would be beneficial as it would allow a reduction in the amount of visual information to be treated. Furthermore, this pruning process would not be definitive. Indeed, all regions would eventually elicit responses; it is not like a threshold where a decision must be taken regarding what must be kept and what must be got rid of. For that reason, the temporal precedence paradigm is viewed as a major improvement in computer vision.

Another aspect of this paradigm consists in the possibility that high visual centers could filter out incoming visual information on the basis of previously received visual data. Such a mechanism would require a global feedback to act on the early visual system, at the image of the physiological one, presumed to exist in the human visual system (see LGN in section 2.3).

In consequence, the dynamic visual data flow would be processed dynamically. Coupled with

148 C H M 1 bK 7, Overall conclusions oscillatory interactions in neuronal structures, this system would represent the most elaborated generalization of a temporal analysis.

References

Abeles M. ( 1982). Local Cortical Circuits, Springer-Verlag.

Adrian E.D. and Matthews R. (1927). The action of light on the eye. 11. The processes involved in retinal excitation. Journal of Physiolorry, 64,279-301.

Allman J. (1987). Maps in context: some analogies between visual cortical and genetic maps.

In Matters of Intelligence, ed. Vaina L.M., pp. 369-393, Reidel D. Publishing Company.

Allman J., Miezin F., and McGuiness E. (1985). Stimulus specific responses from beyond the classical receptive field. Annual Review Neuroscience, 8,407-430.

Alpern Ivf. (1968). A note on visual latency. Psvcholonical Review, 75,260-264.

Andersen R.A., Bracewell R.M., Barash S., Gnadt J.W., and Fogassi L. (1990). Eye position effects on visual, memory, and saccade-related activity in areas LIP and 7a of macaque.

The Journal of Neuroscience, l0, 1176-1 196.

Andersen R.A. (1989). Visual and eye movement functions of the posterior parietal cortex.

&-

nual Review of Neuroscience, 2,377-403.

Arbib M.A. (1989). The Metauhorical Brain 2, John Wiley and Sons.

Arbib M.A. (1987). Levels of modeling of mechanisms of visually guided behavior. Behavior- al and Brain Sciences, 10,407-465.

Armstrong R.A., Slaven A., and Harding G.F.A. (1991). 'Visual evoked magnetic fields to flash and pattern in 100 normal subjects. Vision Research,

2,

1859-1864.

Attneave F. (1954). Some informational aspects of visual perception. Psvcholo~ical Review, 6l, 183-193.

Attwell D. and Tessier-Lavigne M. (1989). Designing synaptic connections in the outer retina.

In The Com~uting Neuron, eds. Durbin R., Miall C., and Mitchison G., pp. 337-354, Add- ison-Wesley.

Attwell D. (1986). Ion channels and signal processing in the outer retina. Quarterly Journal of Ex~erimental Physiology, 3,497-536.

Bader C.R. (1988). L'oeil et la transformation de signaux lumineux en signaux Clectriques.

Cahiers de la FacultC de MCdecine, l6, Universite de Genkve, 15-38.

Barlow H.B. (1982). Physiology of the retina. In The Senses, eds. Barlow H.B. and Mollon J.D., pp. 102- 1 13, Cambridge University Press.

Barlow H.B. and Mollon J.D. (1982). Psychophysical measurements of visual performance. In

150 References The Senses, eds. Barlow H.B. and Mollon J.D., pp.114-132, Cambridge University Press.

Benoit J. and Lachapelle P. (1990). Temporal relationship between ERG components and geniculate unit activity in rabbit. Vision Research, 30,797-806.

Berzins V. (1984). Accuracy of Laplacian edge detectors. Computer Graphics and Image Pro- cessing, 27, 195-210.

Bloom E. and Lazerson A, (1985). Brain, Mind, and Behavior, Freeman W.H. and Cie.

Boer L.C. and Keuss J.G. (1982). Global precedence as a postperceptual effect: an analysis of speed-accuracy tradeoff functions. Percewtion and Psvcho~hvsics, 2,358-366.

Bolz J., Rosner G., and W&sle H. (1982). Response latency of brisk-sustained (X) and brisk- transient (Y) cells in the cat retina. Journal of Phvsiolonv,

%,

171-190.

Born R.T. and Tootell R.B. (1991). Single-unit and 2-deoxyglucose studies of side inhibition in macaque striate cortex. Proceedings of the National Academy of Sciences of the USA, 88,707 1-7075.

~ouzerdoum A., Nabet B., and Pinter R.B. (1991). Analysis and analog implementation of di- _- - rectionally sensitive shunting inhibitory neural networks. Visual Infomation Processing:

From Neurons to chi^, S P E 1473,29-37.

Braccini C., Garnbardella G., and Sandini G. (1981). A signal theory approach to the space and frequency variant filtering performed by the human visual system. Signal Processing,

3,

23 1-240.

Briffod C. and Burgi P.-Y. (1991). Vision B la Grossberg. A1 and Vision Group, Computing Science Center, Report 91.05, University of Geneva.

Bugmann G. (1992). Summation and multiplication: two distinct operation domains of leaky integrate-and-fire neurons. Network, in press.

Burgess A.E., Wagner R.F., Jennings R.J., and Barlow H.B. (198 1). Efficiency of human visual signal discrimination. Science, 214, 93-94.

Burgi P.-Y. (1992a). Figure-ground separation: evidence for asynchronous processing in visual perception?. Proceedings of Swiss Group for Artificial Intelligence and Cognitive Science, Bienne.

Burgi P.-Y. (1992b). Active segmentation: a consequence of asynchrony in visual analysis.

Proceedings of Pattern Recognition and Computer Vision, Lausanne, January 30-31, 75- 83.

Burgi P.-Y. (1992~). Analysis and understanding of neuronal structures. Neurocomputing,

4,

1 1-40.

Burgi P.-Y. and Pun T. (1991a). Temporal analysis of contrast and geometric selectivity in the early human visual system. In Channels in the Visual Nervous System, (ed.) Blum B., pp.

273-288, Freund Pub. House Ltd.

Burgi P. -Y. and Pun T. (1 99 1 b). Figure-ground separation: evidence for asynchronous processing in visual perception?. Perception, 20, 69.

Burgi P.-Y., Pun T., and Pellegrini C. (1989).Temporal analysis and modeling of the visual cir- cuitry. Perception, 18,501.

Burkhardt D.A., Gottesrnan J., and Keenan R.M. (1987). Sensory latency and reaction time:

dependence on contrast polarity and early linearity in human vision. Journal of Optical So- ciety of America A, &, 530-539.

Burnod Y. (1989). An Adaptive Neural Network: the Cerebral Cortex, Masson.

Carney T., Paradiso M.A., and Freeman R.D. (1989). A physiological correlate of the Pulfrich effect in cortical neurons of the cat. Vision Research, 29, 155- 165.

Cattell J.M. (1885). The influence of the intensity of the stimulus on the length of the reaction time. Brain, 8, 512-515.

Chapman R.M. (1961). Spectral sensitivity of single neural units in the bullfrog retina.

The

References 151 Optical Society of America, 5 l , 1 102- 1 1 12.

Chen J.S. and Medioni C. (1987). Detection, localization and estimation of edges. Proceedings of Darpa Image Understanding Workshop, 988-1000.

Chou P.B. and Brown C.M. (1987). Multi-modal segmentation using markov random fields.

Proceedings of Darpa Image Understanding: Workshop, 663-670.

Clark J.J. (1989). Authenticating edges produced by zero-crossing algorithms. IEEE Transactions on Pattern Analvsis and Machine Intelligence, ll,43-57.

Cohen M.A. and Grossberg S. (1984). Neural dynamics of brightness perception: features, boundaries, diffusion, and resonance. Perception and Psycho~hysics, 36,428-456.

Connolly M. and Van Essen D. (1984). The representation of the visual field in parvicellular and magnocellular layers of the lateral geniculate nucleus in the macaque monkey.

The

Journal of Comparative Neurology, 226,544-564.

Connor J.A. and Stevens C.F. (197 1). Prediction of repetitive firing behaviour from voltage clamp data on an isolated neurone soma. Journal of Phvsiology, 213, 31-53.

Courtney S.M. and Buchsbaum G. (1991). Temporal differences between color pathways within the retina as a possible origin of subjective colors. Vision Research, 3l, 1541-1548.

Crick F. (1984). Function of the thalamic reticular complex: the searchlight hypothesis.

Proceedings of the National Academy of Sciences of the USA, 817 4586-4590.

Cursio C.A., Sloan K.R. Jr., Packer O., Hendrickson A.E., and Kalina R.E. (1987). Distribution of cones in human and monkey retina: individual variability and radial asymmetry.

Science, 236,579-582.

Daly S.J. and Normann R.A. (1985). Temporal information processing in cones: effects of light adaptation on temporal summation and modulation. Vision Research, 25, 1197-1206.

Daugman J.G. (1985). Representational issues and local filter models of two-dimensional spatial visual encoding. In Models of the Visual Cortex, eds. Rose D. and Dobson V.G., pp.

96-107, John Wiley & Sons Ltd.

Desimone R., Schein S.J., Moran J., and Ungerleider L.G. (1985). Contour, color and shape analysis beyond the striate cortex. Vision Research, 25,441-452.

Desimone R., Albright T.D., Gross C.G., and Bruce C. (1 984). Stimulus-selective properties of inferior temporal neurons in the macaque. The Journal of Neuroscience, 4,2051-2062.

Desimone R., Albright T.D., Gross C.G., and Bruce C. (1 984). Stimulus-selective properties of inferior temporal neurons in the macaque. The Journal of Neuroscience, 3,2251-2062.

Devijver P.A. and Dekesel M.M. (1987). Algorithmes d'apprentissage d'un mod8le markovien d'image. In Reconnaissance des forrnes et intelligence artificielle, ed. AFCET, pp. 193- 207, Dunod.

DeYoe E.A. and Van Essen D.C. (1988). Concurrent processing streams in monkey visual cortex. Trends in Neuro Sciences, ll, 219-226.

Dobbins A., Zucker S.W., and Cynader M.S. (1989). Endstopping and curvature. Vision Research, 29, 1371-1387.

Dobbins A,, Zucker S.W., and Cynader M.S. (1987). Endstopped neurons in the visual cortex for calculating curvature. Nature, 329,438-441.

Doma H. and Hallett P.E. (1989). Variable contributions of rods and cones to saccadic eye- movement latency in a non-foveating task. Vision Research,29,563-577.

152 References Donner K. (1989). Visual latency and brightness: an interpretation based on the responses of

rods and ganglion cells in the frog retina. Visual Neuroscience,

3,

39-51.

Dow B.M., Vautin R.G., and Bauer R. (1985)- The mapping of visual space onto foveal striate cortex in the macaque monkey. The Journal of Neuroscience, 2,890-902.

Dow B.M., Snyder A.Z., Vautin R.G., and Bauer R. (198 1). Magnification factor and receptive field size in foveal striate cortex of the monkey. Ex~erimental Brain Research, 44,213-228.

Drasdo D. (1977). The neural representation of visual space. Nature, 266,554-556.

Dresp B. and Bonnet C. (1991). Psychophysical evidence for low-level processing of illusory

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