Rebound Spiking as a Neural Mechanism for Surface Filling-in

Hans Supèr1,2,3 and August Romeo1 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021

Abstract ■ Perceptual filling-in is the phenomenon where visual informa- hibition produces rebound or after-discharge spiking in tion is perceived although information is not physically present. that otherwise do not receive sensory information. The behavior For instance, the blind spot, which corresponds to the retinal loca- of rebound spiking mimics the immediate surface filling-in illusion tion where there are no photoreceptor cells to capture the visual observed at the blind spot and also reproduces the filling-in of an signals, is filled-in by the surrounding visual signals. The neural empty object after a background flash, like in the color dove illu- mechanism for such immediate filling-in of surfaces is unclear. sion. In conclusion, we propose rebound spiking as a possible By means of computational modeling, we show that surround in- neural mechanism for surface filling-in. ■

INTRODUCTION filling-in processes but they are probably too slow to explain The blind spot is the region in the that corre- the rather immediate surface filling-in at the blind spot (see sponds to the optic disk where the optic nerve leaves the Komatsu, 2006). The other hypothesis, the cognitive or sym- . At this location, there are no light-detecting pho- bolic filling-in theory, postulates that blind regions are ig- toreceptor cells to capture the visual events, and conse- nored and object representation is realized at high cortical quently this part of the visual field is not perceived. Yet we level on the basis of information from lower areas do not see a hole in our visual scene when we look with (Pessoa et al., 1998). Feedback projections from these higher one eye because the location of the blind spot is filled-in areas have large axonal termination fields in the early visual by the surrounding visual information (see Figure 1A). areas and may so provide sensory information to neurons in This is shown by neurophysiological reports that describe the lower areas located at the blind spot region. However, neural responses related to filling-in at the blind spot in the it has been shown that feedback has a role in modulating early visual cortex (Matsumoto & Komatsu, 2005; Komatsu, stimulus-evoked responses and does not activate otherwise Kinoshita, & Murakami, 2000, 2002; Fiorani, Rosa, Gattas, & silent neurons (Ekstrom, Roelfsema, Arsenault, Bonmassar, Rocha-Miranda, 1992), which are consistent with neural de- & Vanduffel, 2008). This indicates that cortical neurons at scriptions of other forms of surface filling-in early visual cor- the blind spot region need to be activated, presumably by tex (Huang & Paradiso, 2008; MacEvoy, Kim, & Paradiso, feed-forward connections. 1998; De Weerd, Gattass, Desimone, & Ungerleider, 1995). How can retinal signals be effective in activating cells in The neural mechanisms for filling-in of are still a matter early cortical areas that do not receive feed-forward exci- of debate. Two different theories have been put forward to tatory projections? The excitatory retinal information is explain the filling-in completion phenomenon. One theory accompanied by inhibitory signals. Besides the global influ- postulates that spreading of neural activity in early visual ence, inhibition is robust, fast, and prominent in retina, areas is the basis for filling-in of visual information (Pessoa, LGN, and visual cortex (Alitto & Usrey, 2008; Solomon, Thompson, & Noe, 1998; Ramachandran & Gregory, 1991). Lee, & Sun, 2006; Blitz & Regehr, 2005). It is well known This theory is based on the assumption that cells at contrast that strong inhibition may cause rebound excitation at the borders spread their activity to surrounding cells. In such a end of the hyperpolarized period. Rebound or paradoxical case, filling-in is accomplished by the dense network of hori- excitation is a biophysical feature of neurons in which, fol- zontal connections that exist in the visual cortex. Horizontal lowing a period of strong hyperpolarization below the rest- connections have slow conduction velocities (0.1–0.2 m/sec; ing membrane potential, the membrane potential briefly Angelucci & Bressloff, 2006) and may explain slow surface rebounds to a more depolarized level resulting in firing spikes. Rebound spiking is thus triggered by inhibition and not by direct sensory activation. After-discharges may 1University of Barcelona, 2Institute for Brain, Cognition, and also be evoked by rebounds through inhibitory networks Behavior, 3Catalan Institution for Research & Advanced Stud- (Macknik & Martinez-Conde, 2004; Macknik & Livingstone, ies (ICREA) 1998). Here we prefer to use the term rebound spikes

© 2010 Massachusetts Institute of Technology Journal of Cognitive 23:2, pp. 491–501 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 Figure 1. Perceptual filling-in and model architecture. (A) A surface (input) is perceived although at some retinal parts there are no sensory receptors. The blind spot region is filled-in by the surrounding visual information so that cortical neurons whose locations correspond to the blind spot region respond to the surface stimulus. (B) The model consists Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021 of two neural layers, which are unidirectional connected. All layers receive point-to-point (retinotopic) excitatory input (black arrow). The first neural layer receives sensory input. Neurons in the second layer also receive inhibitory input from all preceding neurons (gray shading).

instead of after-discharges (Adrian & Matthews, 1927) be- METHODS cause in our study, neurons become active (rebound) after Model Architecture and Inputs the end of suppression rather than continue firing spikes after removal of the receptive field stimulus. The model is composed of two layers, each containing two In the , rebound spiking is observed in arrays of 64 × 64 units of neurons of the Izhikevich type the retina (Margolis & Detwiler, 2007; Mitra & Miller, (Izhikevich, 2003; Figure 1B). Each layer corresponds to a 2007a, 2007b), LGN (Bright, Aller, & Brickley, 2007; Zhu & visual region. We consider neurons in the first layer as ret- Lo, 1996; Mastronarde, 1987), and visual cortex (Moliadze, inal ganglion cells, which transform continuous or graded Zhao, Eysel, & Funke, 2003). In the retina amacrine cells input into spike activity. In this layer, the region of the optic may inhibit ganglion cells over a large region causing re- nerve corresponding to the blind spot was modeled by a bound spiking in these cells (see Mitra & Miller, 2007a), center region (16 × 16) void of neuronal cells. For the color and in the LGN, reticular cells may evoke rebound burst dove illusion, the center part represented the location of in relay cells (Destexhe & Sejnowski, 2002). Hence, strong, the empty object and contained neurons like in the normal global inhibition, and rebound spiking are prominent in visual field. The second layer may correspond to the LGN or early visual structures. Therefore, although it has been ar- V1. Neurons in the first layer receive surface input, which is gued that rebound activity may not represent visual infor- an array of 64 × 64 pixels. The pixel values of the input array mation (Buzsaki, 2006), we consider the possibility that are 1 and correspond to the preference of a single visual fea- rebound activity induced by widespread suppression can ture, like direction of motion or color. For the color dove be an alternative explanation for surface filling-in. illusion, the pixel values were set to 0 for the background To test this idea, we used computer simulations of a and object region. neural network model composed of biologically plausible spiking neurons (Izhikevich, 2003) that permit to investi- Feed-forward Connections gate such dynamic network behavior. Our results show that inhibition produced rebound spiking in neurons cor- The excitatory feed-forward projections from the input responding to the blind spot after surface stimulation. Sur- layer to the first neural layer and from the first to the sec- rounding cells also responded to the surface stimulus, ond neural layer are retinotopic (point-to-point connec- although they received the same inhibitory input as the tions), where pixel/ Nij in the one layer solely cells at the blind spot. The strength and onset latency of connects to neuron Nij in the next layer (Figure 1B). Thus, the rebound responses were similar to the ones of the stim- the excitatory part of a neuronʼs receptive field has size ulus evoked response, which agrees with complete and im- one. Neurons in the first neural layer do not receive inhib- mediate perceptual filling-in of the blind spot (Komatsu, itory signals from the surface stimulus input. Neurons in 2006; Ramachandran & Gregory, 1991). Finally, our model the second layer receive inhibition from all neurons lo- can explain the immediate filling-in of an empty object at cated in the preceding layer. Thus, inhibition is global. In- the normal visual field location at the end of a background hibition is achieved by assigning negative weights to the color flash, as happens in the color dove illusion. So we pro- connections. Neither intralaminar connections, that is, hori- pose rebound spiking as an alternative neural mechanism zontal connections between neurons within a layer, nor for some types of surface filling-in. feedback connections, that is, connections from the second

492 Journal of Cognitive Neuroscience Volume 23, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 neural layer to the first neural layer, are included in the net- fact applied to vij, uij,and∀ij. We used the Euler or Izhikevich work architecture. method with Δt = 0.20 msec. The input current I in Equa- tion 1 is the result of summing different matrix contribu- tions of the form Neuronal Cell Type

Hodgkin–Huxley models are too slow for network opera- Iij ¼ Iexc ij þ Iinh ij ð4Þ tions, and integrate-and-fire models are unrealistically simple and incapable of producing rich spiking and bursting dy- where “exc” stands for “excitatory,”“inh” for “inhibitory,” namics exhibited by cortical neurons. We opted to use the and i, j are spatial indices. Further, for neural layers, spiking neurons of Izhikevich (2003). These neurons com- Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021 bine the biologically plausibility of Hodgkin–Huxley-type Iexc ¼ ωexcF; dynamics and the computational efficiency of integrate- ! and-fire neurons and are capable of producing rich firing 1 X patterns exhibited by real biological neurons. I ¼ ω F 1 ; ð5Þ inh inh N2 ij N N 1; j

Model Dynamics F is either the two-dimensional figure itself or the binary Cell dynamics is described by the “simple” spiking model array defined by the presence of spikes, that is, with ones of Izhikevich (2003) (Equations 1 and 2) where Equation 2 is satisfied and zeros elsewhere. The 1N × N symbol denotes an N × N matrix containing just ones. Because excitatory receptive fields have size one, ex- dv 2 ¼ 0:04v þ 5v þ 140 u þ I citatory signals are point-by-point (retinotopic) copies of dt F itself, multiplied by the corresponding weight. The in- hibitory part, whose associate receptive field has the same du ¼ aðbv uÞ; ð1Þ size as F, produces a spatially constant term—hence, the dt 1N × N matrix—which is proportional to the normalized supplemented with the after-spike reset rule sum of all the F coefficients times the inhibitory weight. Al- ternatively, we used a receptive field with size of 64 × 64. In our design, the employed weights are ωexc =1,ωinh = 0 for v ← c ω if v ≥ v ; then ð2Þ the stimulus input to neural Layer 1 and exc =2000(1000), sp u ← u þ d: ωinh = −1800 (−500) for the signals from neural Layer 1 to neural Layer 2 in the blind spot (color dove) experiment. The v, u, I, and t are dimensionless versions of membrane voltage, recovery variable, current intensity, and time. Fur- ther, a is a time scale, b measures the recovery sensitivity, c is the reset value for v,andd is the height of the reset jump RESULTS for u. A capacitance factor C was chosen to be 1 and there- Neurons in the first layer receiving the continuous input fore omitted (Izhikevich, 2003). For all our simulations, a = from the surface stimulus responded with a transient burst − 0.02, b =0.25,c = 55, d = 0.05, and vsp,=30.Whendi- of six action potentials after the onset of a surface stimulus. mensions are reintroduced, voltages are read in millivolts Subsequently, the corresponding, that is, at the same reti- and time in milliseconds. These values correspond to the notopic location, Layer 2 neurons responded with a similar phasic bursting type of the Izhikevich neuron (Izhikevich, spike burst (Figure 2). Thus, the global inhibition that all 2003). We choose the neurons to be phasic bursting be- Layer 2 neurons received did not annul the excitatory drive cause of the importance of bursts in sensory processing of the feed-forward connections from Layer 1 neurons. (Swadlow & Gusev, 2001). Spike bursts report the begin- ning of the stimulation and can transmit the saliency of the input because the effect of a burst on the postsynaptic Surface Filling-in of the Blind Spot neuron is stronger than the effect of a single spike, and For those Layer 2 neurons located at the center (represent- bursts are needed to overcome the synaptic transmission ing the blind spot region), the global inhibition was the failure and reduce neuronal noise. sole input since no neuronal cells were present at the cen- As initial conditions at t0 =0,weset ter of the first layer. Without the excitatory drive, the global inhibition resulted in a strong and rapid hyperpolarization vðt0Þ¼c; uðt0Þ¼bvðt0Þð3Þ of the membrane potential of the center neurons. At the end of the hyperpolarizing period, these neurons produced for all the positions in our arrays (because we deal with two- rebound spikes (Figure 2A and B). The onset latency of re- dimensional objects, Equations 1 and 2 are actually meant bound spikes is variable (Tremere, Pinaud, Irwin, & Allen, for v → vij, u → uij, I → Iij, i,j =1,…, N,andEquation3isin 2008; Margolis & Detwiler, 2007). Rebound responses can

Supèr and Romeo 493 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 Figure 2. Neural responses to surface stimulus. (A) Model output and firing rates of neurons located at the blind spot region and at the surrounding region. The light- dark squares represent the matrix of neurons of the model. The white center square in Layer 1 represents the blind spot, and the dotted white lines in Layer 2 delineate this region. Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021 The gray circles depict neurons and the arrows originating from them point to the corresponding spike responses. (B) The spike times of all the neurons from a center column of the model matrices (gray vertical lines). Each small dot represents a spike. Time is from stimulus onset. (C) Amplification of the first two spikes of the central neuron shown in panel A.

be as fast as 5 msec or take several seconds to occur after vation and widespread inhibition in Layer 2 neurons. the end of hyperpolarization period. Our data show that a The balance between excitation and inhibition is so that rebound spike occurred immediately after each inhibitory the magnitude of the local excitatory input is sufficient pulse from the first neural layer (see Figure 2C). Similar to overcome the strong global suppressive input. For the biphasic spikes are observed frequently in the visual cortex (Gold, Girardin, Martin, & Koch, 2009). Therefore, the on- set of the rebound burst was similar (almost identical) to the excitatory-driven burst from the surrounding neurons in the second layer. To calculate the effectiveness of the sur- face filling-in, we divided the number of spikes in the re- bound response by the number of spikes evoked by the surrounding Layer 2 neurons. The results show that the strength of the rebound responses was identical to the re- sponse strength of the other Layer 2 cells. When the sur- face stimulus contrast was decreased, this ratio remained unit although the spike rate decreased (Figure 3). Stimu- lus contrast below 0.4 did not evoke spikes in Layer 1 cells. This signifies that the magnitude of surface filling-in of the blindspotisasrobustastheresponsetothesurrounding surface. Figure 4 summarizes the results of the surface filling-in at Layer 2 of the model. A small stimulus confined to the blind spot region will remain invisible for the Layer 2 Figure 3. The strength of filling-in as a function of stimulus contrast. cells, like in the visual cortex (Komatsu et al., 2002). How- Ratio is the number of rebound spikes of a Layer 2 neuron within ever, a surface stimulus excites many (all) Layer 1 neurons. the blind spot divided by the number of spikes from a surrounding On their turn, these neurons produce retinotopic acti- neuron. Stimulus contrast below 0.4 did not evoke spikes in Layer 1.

494 Journal of Cognitive Neuroscience Volume 23, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 Figure 4. Explanation of the observed filling-in process. A small sensory stimulus that falls within the blind spot region will not evoke a neural response. A large stimulus (surface) will activate surrounding cells, which will produce rebound spiking in neurons whose receptive fields are located within the blind spot. Open and filled Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021 circles denote inactive and active neurons, respectively.

neurons located at the blind spot, the global inhibitory can be filled-in. For example, in the color dove illusion, signal produces strong hyperpolarization of the mem- an empty object (dove) will fill-in when the surrounding brane potential, which after termination of the inhibitory background (sky) flashes. Directly at the end of the flash, pulse results in a rebound spike. So a hole in a surface the “empty bird” becomes filled-in with a color similar to is filled-in immediately by the Layer 2 cells, which is not the previous color of the sky, albeit intensity of the filled-in caused by lateral spreading of visual information but by color is less than the one of the surrounding color. Thus, global inhibition. the background color produces an afterimage on an “empty” shape where physically no color was presented. This illusion holds for moving as well as for static images Filling-in of an Empty Object: Color Dove Illusion and bears similarities to the Twinkle after image. The Instant filling-in not only occurs at the blind spot or oc- color dove effect is different to the common afterimage cluded regions where no visual events are recorded but effect, which produces the perception of the complemen- also regions that correspond to the normal visual field tary color at the same retinal location.

Figure 5. Filling-in of an empty object. (A) Illustration of the afterimage effect in the color dove illusion. An empty object and background is presented. After flashing the background, the object gets filled-in by the same color as the color of the flashed background. (B) Responses of the model. The gray circles depict neurons and the arrows originating from them point to the corresponding spike responses. The flashed background produces spike activity in the first and second layer. After termination of the background, color neurons at the object location produce a spike burst. Squares indicate the layer matrices of the model. Horizontal black bar in the bottom panel indicates onset and duration of background flash. Time is from stimulus onset.

Supèr and Romeo 495 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 We tested our model for the color dove filling-in effect Macknik, Martinez-Conde, & Haglund, 2000). In this illu- (Figure 5A; see Methods). Note that now the center loca- sion, the perception of the surface of a wide stimulus is tion in Layer 1 representing the empty object location con- weak or disrupted when it is briefly presented while edge tains neurons. To mimic the surrounding background flash, detection is normal. For longer presentation times, both 30 msec after starting the model, the values of the pixels the edges and the surface are clearly detected. This surface at the background were set to 1 for 20 msec and then filling-in is different than the previous stimuli we used in back to 0. Neurons at the background region in Layers 1 that neurons located at the surface region of the stimulus and 2 responded to this flashing by a single burst of spikes receive visual signals via the excitatory receptive field con- (Figure 5B). The center neurons of the second layer re- nections. In contrast, neurons in the blind spot and color

ceived strong suppression from the activated background dove illusion do not receive direct receptive field stimula- Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021 cells in Layer 1. Each time Layer 1 neurons fired a spike, the tion of the surface. In the unfilled flicker illusion experi- membrane potential of the neurons located at the object ment, we adopted the same weights of the connections location in Layer 2 became hyperpolarized, however, which as for the blind spot experiment and applied a 3 × 3 kernel was not sufficiently strong to produce a rebound spike. for the excitatory connections to the detect borders. We Only when the inhibitory signal was removed by switching then presented to the model a squared stimulus of 32 × off the background stimulus the center Layer 2 cells at the 32 or 16 × 16 pixels, with a pixel value of 1, for 10 or 50 msec, empty object location rebounded to more depolarized lev- respectively. The results show that for short stimulus pre- els producing a spike burst (Figure 5B). We used different sentation (10 msec), neurons located at the edge of the flash durations (30–1000 msec) to test the model behavior. stimulus had the highest spike frequency (Figure 7). For For all durations always a rebound burst was observed, the large squared figure, neurons at the center of the stim- which was always equally strong (six spikes) and only oc- ulus had the same response strength as the ones located at curred immediately after the removal of the background the background (Figure 7B), whereas for the small figure, stimulus. Furthermore, the number of spikes in the burst center neurons had stronger responses than background was lower than the number of spikes in the bursts of the neurons (Figure 7A). When the stimulus is presented for surrounding cells (Figure 6A). This result mimics the per- longer time (50 msec), neurons at the edge and at the ceived afterimage of the object in the color dove illusion surface of the stimulus showed a higher response rate com- where the perceived contrast is lower for filling-in re- pared with the response rate of the neurons at the back- gions compared with the surrounding region (see Meng, ground (Figure 7). Ferneyhough, & Tong, 2007). Finally, we tested the ro- bustness of the model by decreasing the background con- trast and object size. The findings show that for low DISCUSSION background contrasts and for small object sizes, a rebound Perceptual filling-in is a phenomenon where visual infor- burst always occurred after the termination of the back- mation is perceived although information is not physically ground flash (Figure 6B). Background contrast below 0.1 present. Filling-in occurs in normal and blind parts of the did not evoke spikes in Layer 1 cells. visual field. Some filling-in illusions take seconds to hap- pen, whereas others are rather instantaneous. Adaptation is believed to be the main cause for slow surface filling-in Unfilled Flicker Illusion of normal regions. The neural mechanisms for the im- To conclude, we tested our model for the unfilled flicker il- mediate filling-in, like at the blind spot, are unclear. In this lusion (Macknik, 2006; Macknik & Martinez-Conde, 2004; study, we show that the behavior of rebound spiking by

Figure 6. The strength of filling-in as a function of background contrast (A) and object size (B). Ratio is the number of rebound spikes of a Layer 2 neuron within the empty object divided by the number of spikes from a neuron on the background. Stimulus contrast below 0.1 did not evoke spikes in Layer 1.

496 Journal of Cognitive Neuroscience Volume 23, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 Figure 7. Filling-in of an unfilled flicker illusion for a small (A) and large (B) input figure. The arrows point to spike frequency (thick gray and black lines) of all neurons from a center row (dark gray horizontal line) of Layer 2 of the model matrix, depicted by the lower gray squares. The dotted white lines represent the small and large figure locations. Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021

global suppression mimics the immediate surface filling-in thalamocortical cells. This notion is supported by absence illusions observed at the blind spot and the after image of of rebound activity in isolated cortical swabs (Grenier et al., an empty object as in the color dove illusion. Also our 1998). In V1, rebound activity is also observed. Here re- model replicates the perceptual effects observed in the sponses leading to rebound activity are feature selective unfilled flicker illusion. having similar orientation selectivity as the visual response (Huang, Levine, & Paradiso, 2008; Huang & Paradiso, 2008). In our model, feed-forward excitation and surround inhibi- Filling-in at Early Visual Stages by Rebound Activity tion are also feature specific. The surface responses in the second layer of our model are Conform with an early account for filling-in is the obser- explained by the relative differences between excitatory vation of interocular rivalry at the blind spot (Tong & Engel, and inhibitory inputs. Layer 1 neurons activated by the 2001) and with reports showing that surface filling-in occurs relatively large background region provoked a strong sup- in the absence of attention and takes place before sensory pression of Layer 2 neurons. Such surround or global sup- signals arrive at cortical level (Crossland & Bex, 2008; Meng pression is present at the first stages of sensory processing, et al., 2007; Tailby, Solomon, Peirce, & Metha, 2007; Meng, like in the retina and LGN (Solomon et al., 2006; Solomon, Remus, & Tong, 2005; He & Davis, 2001; Hardage & Tyler, White, & Martin, 2002; Ruksenas, Fjeld, & Heggelund, 1995). Besides our data, support for early filling-in by re- 2000). For the neurons located at the center (representing bound activity comes from studies on the aftereffect in the location of the blind spot), the global inhibitory signal the Twinkle illusion. It is hypothesized that the Twinkle was the sole input resulting in a strong and rapid hyperpo- illusion is a postinhibitory rebound effect of unstimulated larization of the membrane potential. After termination of cells after removal of inhibition from the surround stimu- the inhibitory input, the strong hyperpolarization caused lation (Crossland & Bex, 2008; Hardage & Tyler, 1995). It rebound spiking of these cells. This implies the existence has been suggested that the locus of the Twinkle aftereffect of a biphasic spike with first a large positive peak followed is within monocular magnocellular ganglion cells in the re- by a negative peak, which has been observed in the visual tina and/or cells in LGN with small receptive fields (Crossland cortex (Gold et al., 2009). Such inhibition-induced spiking & Bex, 2008). Hence, in accordance with the proposal of a is possible in neurons having slow h-currents or T-currents precortical filling-in process (Crossland & Bex, 2008; He & (Bessaïh, Leresche, & Lambert, 2008; Lüthi & McCormick, Davis, 2001; Hardage & Tyler, 1995), our data advocate that 1998) and occurs in rebound to fast GABAa-mediated in- surface filling-in occurs at early stages of visual processing. hibitory events (Baufreton & Bevan, 2008; Destexhe & Sejnowski, 2002; Grenier, Timofeev, & Steriade, 1998). Correspondence of Our Model to the Visual System Rebound activity is found in the retina and is a character- istic feature of Off ganglion cells (Margolis & Detwiler, 2007). The first layer of our model may correspond to the gang- Retinal cells may evoke rebound burst in the thalamic relay lion cell layer of the retina because these cells transform cells (Destexhe & Sejnowski, 2002). Rebound bursts in the continuous input into spikes. For the filling-in of empty ob- thalamocortical cells occur before rebound depolariza- jects, the second layer may also correspond to the retina tion in cortical cells (Grenier et al., 1998), suggesting that where certain types of ganglion cells receive, besides local rebound excitation in cortical neurons is inherited from excitation, global inhibition from spiking amacrine cells

Supèr and Romeo 497 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 or from recurrent interactions via gap junctions (Trong blind spot in V1 to the presence of a stimulus are similar & Rieke, 2008). Alternatively, the second layer may repre- as the ones of the surrounding cells (Komatsu et al., 2000). sent the LGN that receives powerful synaptic excitatory con- Such a fast filling-in may argue for a feed-forward control of tacts from a few retinal ganglion cells (Sincich, Adams, surround inhibition. Feedback input from extrastriate cor- Economides, & Horton, 2007). The same retinal ganglion tex, which is conjectured to be important for surface seg- cells also provide inhibitory postsynaptic currents (Blitz & regation (Lamme, Rodriguez-Rodriguez, & Spekreijse, Regehr, 2005). The surround suppression in the LGN may 1999), can act also fast and influences the earliest feed- be inherited from the retina because it is equal (Alitto & forward-induced responses (Hupe et al., 2001). Moreover, Usrey, 2008) or slightly different (Ruksenas et al., 2000) to corticogeniculate feedback projections may already in-

that in the retina. In addition, LGN interneurons may con- tegrate visual signals around the blind spot region (Yokoi Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021 tribute to surround inhibition (Norton & Godwin, 1992). & Komatsu, 2009). Furthermore, fast suppressive signals, The influence of inhibition in the LGN comes from a larger which sometimes arrive earlier than excitatory ones, could retinal region than that from excitation and takes place at be explained by the difference in synaptic distribution; in- the very beginning of a stimulus response (Alitto & Usrey, hibitory cells synapse near the soma, whereas excitatory 2008; Blitz & Regehr, 2005). Surround suppression of LGN contacts are made at more distal locations. In our model, neurons appears to be orientation sensitive (Solomon et al., we modeled global inhibition by adding a negative weight 2002; Sillito, Cudeiro, & Murphy, 1993), like in our model. to the feed-forward connections and not by introducing This observation may suggest a role of corticothalamic local inhibitory cells at Layer 2. In this way, the combina- feedback in LGN surround suppression (Sillito, Cudeiro, & tion in time of excitatory and strong inhibitory inputs mim- Jones, 2006), although other studies suggest no involve- ics the synchronous activation and the strong and global ment of the visual cortex in LGN surround suppression inhibition described in the early visual system. Further (Alitto & Usrey, 2008; Nolt, Kumbhani, & Palmer, 2007; studies should reveal how surface filling-in by rebound ac- Sceniak, Chatterjee, & Callaway, 2006; Bonin, Mante, & tivity occurs by including inhibitory cells and lateral cir- Carandini, 2005; Webb, Tinsley, Vincent, & Derrington, cuits. For instance, is rebound filling-in achieved by local 2005). acting inhibitory cells that receive widespread feedback The second layer of our model may also correspond to projections or by local feed-forward inhibition that is trans- the primary visual cortex. In this case, LGN present a re- mitted laterally within an area? lay of retinal information. The thalamocortical connec- tions are highly convergent maintaining the retinotopic Is Filling-in at the Blind Spot Different than Normal mapping in the visual cortex where they synchronously Surface Filling-in? activate Layer 4 spiny cells. Furthermore, thalamocortical synapses specifically and strongly excite the fast spiking It has been argued that different neural mechanisms for network (Gibson, Beierlein, & Connors, 1999). Fast spiking surface filling-in exists (Crossland & Bex, 2008; Komatsu, neurons form an inhibitory network connected through 2006; Hardage & Tyler, 1995). In the blind spot and color electric synapses and mediate strong thalamocortical inhi- dove illusion, surface filling-in is automatic and fast, bition (Sun, Huguenard, & Prince, 2006; Swadlow, 2003). whereas normal filling-in depends on retinal stability Surround suppression can suppress large regions (Sun et al., and may take seconds to occur. In addition, the visual ex- 2006; Ozeki et al., 2004; Bair, Cavanaugh, & Movshon, perience is different. In normal filling-in, the disappear- 2003; Hirsch et al., 2003; Swadlow, 2003) and can arrive even ance of a stimulus is experienced, whereas at the blind earlier to the target neuron than excitatory signals (Bair spot and color dove illusion, a sensory percept appears. et al., 2003). Surround suppression in V1 is comparable In fact, filling-in at the blind spot occurs without aware- to that observed in the LGN (Ozeki et al., 2004; Solomon ness. Finally, normal filling-in starts from the boundaries et al., 2002). Likely feed-forward inhibition plays a role be- of an object and gradually fills in the surface, and it oc- cause surround inhibition (Jones, Grieve, Wang, & Sillito, curs after the initial figure-ground segregation of the scene 2001), like filling-in (Ramachandran & Gregory, 1991), is (De Weerd et al., 1995), whereas for filling-in at the blind feature specific. Feedback connections to V1, which match spot, this is questionable. Our results show no gradual the full spatial range of surround interactions, also con- filling-in of the surface and may indicate that filling-in at tribute to surround suppression (Angelucci & Bressloff, the blind spot forms part of figure-ground segregation. 2006). Hence, we speculate that the neural mechanisms for sur- face filling-in at the blind spot to be different than for nor- mal surface filling-in. Biological Substrates of Fast Global Inhibition Surround inhibition is carried by lateral inhibitory connec- Limitations of Our Model tions and are modulated by feed-forward and feedback in- put. If lateral connections are the neural substrate of We constructed a simple feed-forward model architec- rebound spiking, the widespread inhibitory signal should ture on the basis of realistic spiking neurons to test the arrive fast because the response times of neurons at the idea that global inhibition may lead to surface filling-in by

498 Journal of Cognitive Neuroscience Volume 23, Number 2 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2010.21512 by guest on 01 October 2021 producing rebound spiking in neurons located at the sur- receptive field center and extra-classical receptive field face of a stimulus. By omitting recurrent processing, we surround of primate V1 neurons. Progress in Brain Research, 154, 93–120. obviously restrained the model. This means that the model Bair, W., Cavanaugh, J. R., & Movshon, J. A. (2003). Time course is limited in its capacity, and it was thus not expected that and time–distance relationships for surround suppression all filling-in phenomena can be replicated by the model, in in macaque V1 neurons. Journal of Neuroscience, 23, particular taking into account that normal filling-in effects 7690–7701. occur after figure–ground segregation. However, our model Baufreton, J., & Bevan, M. D. (2008). D2-like dopamine receptor-mediated modulation of activity-dependent is versatile in the sense that our binary input can be extrapo- plasticity at GABAergic synapses in the subthalamic nucleus. lated to any visual feature, like orientation, color, brightness, Journal of Physiology, 586, 2121–2142. and direction of motion that is carried by the feed-forward Bessaïh, T., Leresche, N., & Lambert, R. C. (2008). T current Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/23/2/491/1774688/jocn.2010.21512.pdf by guest on 18 May 2021 connections. potentiation increases the occurrence and temporal fidelity Other models (Grossberg & Hong, 2006; Macknik, 2006; of synaptically evoked burst firing in sensory thalamic neurons. Proceedings of the National Academy of Sciences, Macknik & Martinez-Conde, 2004) explain surface filling-in U.S.A., 105, 11376–11381. by border detection followed by a gradual filling-in of the Blitz, D. M., & Regehr, W. G. (2005). Timing and specificity surface by lateral interactions. For instance, in the spatio- of feed-forward inhibition within the LGN. Neuron, 45, temporal edge model (Macknik et al., 2000), visual excita- 917–928. tion is transmitted laterally in the form of inhibition resulting Bonin, V., Mante, V., & Carandini, M. (2005). The suppressive field of neurons in lateral geniculate nucleus. Journal of in edge enhancement. We reproduced edge enhancement Neuroscience, 25, 10844–10856. in the unfilled flicker illusion that was predicted by the Bright, D. P., Aller, M. I., & Brickley, S. G. (2007). Synaptic spatio-temporal edge model. Thus, although we did not in- release generates a tonic relay neurons. Journal of clude lateral interactions, the spatio-temporal edge model Neuroscience, 27, 2560–2569. is expected to respond in the same way as ours. Buzsaki, G. (2006). Rhythms of the brain. Oxford: Oxford University Press. Crossland, M. D., & Bex, P. J. (2008). The Twinkle aftereffect Conclusions is pre-cortical and is independent of filling-in. Journal of Vision, 8, 1–10. Our data show that surround inhibition produces rebound De Weerd, P., Gattass, R., Desimone, R., & Ungerleider, L. G. spiking that may serve for surface filling-in in parts of the (1995). Responses of cells in monkey visual cortex during perceptual filling-in of an artificial scotoma. Nature, 377, visual field for which no retinal signal exists, for example, 731–734. in the blind spot. A functional role of rebound spiking in Destexhe, A., & Sejnowski, T. J. (2002). The initiation of visual processing is not known, perhaps because in vivo re- bursts in thalamic neurons and the cortical control of cordings of rebound activity are difficult to realize (Alviña, thalamic sensitivity. Philosophical Transactions of the Walter, Kohn, Ellis-Davies, & Khodakhah, 2008). However, Royal Society of London, Series B, Biological Sciences, 357, 1649–1657. according to our model data, it is attractive to consider re- Ekstrom, L. B., Roelfsema, P. R., Arsenault, J. T., Bonmassar, G., bound spiking as an important contributor to filling-in. & Vanduffel, W. (2008). Bottom–up dependent gating of frontal signals in early visual cortex. Science, 321, 414–417. Acknowledgments Fiorani, M., Rosa, M. G. P., Gattas, R., & Rocha-Miranda, C. E. (1992). Dynamic surrounds of receptive fields in primate This work was supported by the Spanish Ministry of Education and striate cortex: A physiological basis for perceptual Science (MICINN) (grant nos. SEJ2006-15095 and SAF2009-10367) completion? Proceedings of the National Academy of and the Catalan government (AGAUR) (grant no. 2009-SGR-308). Sciences, U.S.A., 89, 8547–8551. Reprint requests should be sent to Hans Supèr, Department of Ba- Gibson, J. R., Beierlein, M., & Connors, B. W. (1999). 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