Object recognition is a complex neuronal process determined by interactions between many visual areas: from the retina, thalamus to the ventral visual pathway. These structures transform variable, single pixel signal in photoreceptors to a stable object representation. Neurons in visual area V4, midway in ventral stream, represent such stable shape detector. A feed forward hierarchy of increasing in size and complexity receptive fields (RF) leads to grand mother cell concept. Our question is how these processes might identify an object or its elements in order to recognize it in new, unseen conditions? We propose a new approach to this problem by extending the classical definition of the RF to a fuzzy detector. RF properties are also determined by the computational properties of the bottom-up and top-down pathways comparing stimulus with many predictions. The “driver-type”.ogic (DTL) of bottom-up computations looks for large number of possible object parts (hypotheses –.ough set (RS) upper approximation), as object’s elements are similar to RF properties. The optimal combination is chosen, in unsupervised, parallel, multi-hierarchical pathways by the “modulator-type”.ogic (MTL) of top-down computations (RS lower approximation). Interactions between DTL (hypotheses) and MTL (predictions) terminates when RS boundary became small - the object is recognized.
PrzybyszewskiA.W., KaganI. and SnodderlyM., Primate area V1: Largest response gain for receptive fields in the straight-ahead direction, NeuroReport25 (2014), 1109–1115.
2.
PrzybyszewskiA.W., The neurophysiological bases of cognitive computation using rough set theory, Trans Rough Sets IX, LNCS5390 (2008), 287–317.
3.
PawlakZ.Rough Sets—Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Boston, London, Dordrecht, 1999.
4.
PolkowskiL. and SkowronA., Rough mereology: A new paradigm for approximate reasoning, Int J Approx Reason15 (1996), 333–365.
5.
HubelD.H. and WieselT.N., Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex, J Physiol160 (1962), 106–154.
FukushimaK., Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biol Cyber36(4) (1980), 193–202.
8.
GaskaJ.P., PollenD.A. and CavanaghP., Diversity of complex cell responses to even- and odd-symmetric luminance profiles in the visual cortex of the cat, Exp Brain Res68 (1987), 249–259.
9.
CadieuC., KouhM., PasupathyA., ConnorC.E., RiesenhuberM. and PoggioT., A model of V4 shape selectivity and invariance, J Neurophysiol98(3) (2007), 1733–1750.
10.
RiesenhuberM. and PoggioT., Neural mechanisms of object recognition, Curr Opin Neurobiol12 (2002), 162–168.
11.
GaskaJ.P., PollenD.A. and CavanaghP., Diversity of complex cell responses to even- and odd-symmetric luminance profiles in the visual cortex of the cat, Exp Brain Res68 (1987), 249–259.
12.
PollenD.A., PrzybyszewskiA.W., RubinM.A. and FooteW., Spatial receptive field organization of macaque V4 neurons, Cerebral Cortex12 (2002), 601–616.
13.
BazanJ., Son, H.Nguyen, T. Trung, Nguyen, A. Skowron and StepaniukJ., Decision rules synthesis for object classification, in: OrιowskaE. (ed.). Incomplete Information: Rough Set Analysis, Physica-Verlag, Heidelberg, 1998, pp. 23–57.
14.
BazanJ.G., SzczukaM., RSES and RSESlib—A collection of tools for rough set computations, in: ZiarkoW. and YaoY. (eds.), RSCTC 2000 LNCS(LNAI), 2001, vol. 2005. Springer, Heidelberg, pp. 106–113.
15.
Grzymaιa-BusseJ.W., A new version of the rule induction system LERS, Fund Inform31(1) (1997), 27–39.
16.
DuboisD. and PradeH., Rough fuzzy sets and fuzzy rough sets, Int J Gen Syst17 (1990), 91–209.
RizaL., JanuszA., BergmeirC., CornelisC., HerreraF., SlezakD. and BenítezJ.M., Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”, Inform Sci287 (2014), 68–89.
19.
KufflerS.W., Neurons in the retina: Organization, inhibition and excitation problems, Cold Spring Harbor Symp Quant Biol17 (1952), 281–292.
20.
BarlowH.B., The coding of sensory messages, in: ThorpeW.H. and ZangwillO.L. (eds), Current Problems in Animal Behaviour, Cambridge University Press, Cambridge, 1961, pp. 331–360.
21.
DaugmanJ.D., Uncertainty relation for the resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, J Opt Sci Am A2 (1985), 1160–1169.
22.
PrzybyszewskiA.W., An analysis of the oscillatory patterns in the central nervous system with the wavelet method, J Neurosci Methods38 (1991), 247–257.
23.
PrzybyszewskiA.W., LankheetM.J. and van de GrindW.A., Nonlinearity and oscillations in X-type ganglion cells of the cat retina, Vis Res33 (1993), 861–875.
24.
PrzybyszewskiA.W., LankheetM.J. and van de GrindW.A., On the complex dynamics of intracellular ganglion cell light responses in the cat retina, Biol Cyber74 (1996), 299–308.
25.
PrzybyszewskiA.W., LinsayP.S., GaudianoP. and WilsonC., Basic difference between brain and computer: Integration of asynchronous processes implemented as hardware model of the retina, IEEE Trans Neural Networks18 (2007), 70–85.
26.
WangD. and TermanD., Image segmentation based on oscillatory correlation, Neural Comput9 (1997), 805–836.
27.
ZadehL.A., Toward a perception-based theory of probabilistic reasoning with imprecise probabilities, J Statist Plan Inference105 (2002), 233–264.
28.
ZadehL.A., From computing with numbers to computing with words—From manipulation of measurements to manipulation of perceptions, Int J Appl Math Comput Sci12(3) (2002), 307–324.
29.
PrzybyszewskiA.W., Logical rules of visual brain: From anatomy through neurophysiology to cognition, Cogrt Syst Res11 (2010), 53–66.
30.
BardyC., HuangJ.Y., WangC., Fitz GibbonT. and DreherB., Simplification of responses of complex cells in cat striate cortex: Suppressive surrounds and ‘feedback’.nactivation, J Physiol574 (2006), 731–750.
31.
KaganI., GurM. and SnodderlyD.M., Spatial organization of receptive fields of V1 neurons of alert monkeys: Comparison with responses to gratings, J Neurophysiol88 (2002), 2557–2574.
32.
ShamsL. and von der MalsburgC., The role of complex cells in object recognition, Vis Res42 (2002), 2547–2554.
33.
PrzybyszewskiA.W., GaskaJ.P., FooteW. and PollenD.A., Striate cortex increases contrast gain of macaque LGN neurons, Vis Neurosci17 (2000), 485–494.
34.
DouglasR.J. and MartinK.A., Neuronal circuits of the neocortex, Annu Rev Neurosci27 (2004), 419–451.
35.
PrzybyszewskiA.W. and KonM.A., Synchronization-based model of the visual system supports recognition. Program No. 718.11. 2003 Abstract Viewer/Itinerary Planner. Society for Neuroscience, Washington, DC, Online.
36.
PrzybyszewskiA.W., Vision: Does top-down processing help us to see?Curr Biol8 (1998), R135–R139.
37.
JonesE.G., Synchrony in the interconnected circuitry of the thalamus and cerebral cortex, Ann NY Acad Sci1157 (2009), 10–23.
38.
ShermanS.M. and GuilleryR.W., On the actions that one nerve cell can have on another: Distinguishing drivers and modulators, Proc Natl Acad Sci USA95 (1998), 7121.
39.
RocklandK.S., SaleemK.S. and TanakaK., Divergent feedback connections from areas V4 and TEO in the macaque, Vis Neurosci11 (1994), 579–600.
40.
SchummersJ., MarinoJ. and SurM., Synaptic integration by V1 neurons depends on location within the orientation map, Neuron36 (2002), 969–978.
41.
PrzybyszewskiA.W., PotapovD.O., RocklandK.S., Feedback connections from area V4 to LGN, Ann Meet Society for Neuroscience, San Diego, USA, 2001. http://sfn.scholarone.coin/itin2001/prog#620.9
42.
GirardP., HupeL.J.M. and BullierJ., Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities, J Neurophysiol85 (2001), 1328–1331.
43.
BardyC., HuangJ.Y., WangC., Fitz GibbonT. and DreherB., Simplification of responses of complex cells in cat striate cortex: Suppressive surrounds and ‘feedback’.nactivation, J Physiol574 (2006), 731–750.
44.
PoggioT. and EdelmanS., A network that learns to recognize three-dimensional objects, Nature343 (1990), 263–266.
45.
AlonsoJ.M., UsreyW.M. and ReidR.C., Rules of connectivity between geniculate cells and simple cells in cat primary visual cortex, J Neurosci21 (2001), 4002–4015.
46.
BiedermanI., Recognition-by-components: A theory of human image understanding, Psychol Rev94 (1987), 115–147.
47.
SripatiA.P. and OlsonC.R., Responses to compound objects in monkey inferotemporal cortex: The whole is equal to the sum of the discrete parts, J Neurosci30 (2010), 7948–7960.
48.
PoortJ., SelfM.W., van VugtB., MalkkiH. and Roelf-semaP.R., Texture segregation causes early figure enhancement and later ground suppression in areas V1 and V4 of visual cortex, Cereb Cortex26(10) (2016), 3964–3976.
49.
MotterB.C., Stimulus conflation and tuning selectivity in V4 neurons: A model of visual crowding, J Vis18(1) (2018), 1–24.
50.
OleskiwT.D., NowackA. and PasupathyA., Joint coding of shape and blur in area V4, Nat Commun9(466) (2018), 1–13.
