2010
DOI: 10.1007/s10827-009-0211-1
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Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

Abstract: How spiking neurons cooperate to control behavioral processes is a fundamental problem in computational neuroscience. Such cooperative dynamics are required during visual perception when spatially distributed image fragments are grouped into emergent boundary contours. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity occur in response to binary spikes with irregular timing across many interacting cells. Some models have demonstrated sp… Show more

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Cited by 24 publications
(21 citation statements)
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References 119 publications
(144 reference statements)
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“…Models that implement biologically plausible learning rules at their synapses require single spikes. Incorporating both, spiking neurons as well as lateral connections and bipolar cells, results in a realistic model for the processing of illusory contours [103]. More recent neurophysiologic data, however, suggest that feedback 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 from higher-tier visual areas is required to perceive ICs, which has led to a Bayesian-based feedback model comprised of multiple sets of selective and invariant layers that is effective in size-invariant stimulus disambiguation only after recursive processing between layers and by extension the spatio-temporal interplay of neural populations [99][100].…”
Section: Figure I About Herementioning
confidence: 99%
See 1 more Smart Citation
“…Models that implement biologically plausible learning rules at their synapses require single spikes. Incorporating both, spiking neurons as well as lateral connections and bipolar cells, results in a realistic model for the processing of illusory contours [103]. More recent neurophysiologic data, however, suggest that feedback 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 from higher-tier visual areas is required to perceive ICs, which has led to a Bayesian-based feedback model comprised of multiple sets of selective and invariant layers that is effective in size-invariant stimulus disambiguation only after recursive processing between layers and by extension the spatio-temporal interplay of neural populations [99][100].…”
Section: Figure I About Herementioning
confidence: 99%
“…If, however, two conditions differ in more than whether a perceived figure can be bound to a coherent object, e.g. if the two figures attract different amounts of attention from the participants, gamma-band responses may instead reflect attention processes rather than binding (see also [61] for arguments based on neural modeling). …”
mentioning
confidence: 99%
“…1 allows us to bridge, in a parsimonious way, the temporal gap between the dynamics of perception and of neuronal populations and networks. Although using the full range of HodgkinHuxley dynamics would likely require some model refinements in order to handle issues such as fast synchronization, recent work on converting rate into spiking neural networks has clarified that the network organizational principles and architecture remain the same, even as finer dynamical and structural details that are compatible with this architecture are revealed (Cao & Grossberg, 2011;Grossberg & Versace, 2008;Léveillé, Versace, & Grossberg, 2010).…”
Section: Discussionmentioning
confidence: 99%
“…In the feedforward/feedback, or, equivalently, the bottom-up/top-down framework of vision [26,33,62], we further conjecture that the emergence of ghost shapes is due to the feed-forward computing in areas such as V1/V2, while their dissolution is due to the feedback supervision from highertier areas such as V4/LOC [45], which signals the lack of occluding evidences and hence rebuts the feed-forward null hypothesis on the existence of illusory shapes. The neurophysiological evidences for the involvement of higher-tier areas (in particular, LOC) in the perception of illusory shapes can be found in [35,56]. Figure 13 involves more realistic shapes of a lady figure and a butterfly.…”
Section: C) Illusory Shapes By Definition 7 the Illusory Square Is mentioning
confidence: 98%
“…Among them, perhaps the most well known example is Kanizsa's Triangle [31] (see . In brain and neural sciences, there also has been a rich literature on the neural and neural-network foundations for illusory shapes [24,34,35,56,64].…”
Section: Introductionmentioning
confidence: 99%