A total of 23 figure positions were presented (the distance of th

A total of 23 figure positions were presented (the distance of the RF center relative to the figure center ranged from −5.5° to 5.5° with 0.5° steps, see Figure 2B). For 9 of the 46 V4 recording sites, we did not present figures at all

these Selleckchem Everolimus positions, but we used a subset of five positions (one center, two edge, and two background positions), and the data from these recording sites were not included in the space-time plots (Figures 6C and 6D). The stimulus also contained two curves (width 0.27°, luminance 82 cd·m-2) and two red circles (size 1.5°) in the hemifield opposite to the figure (upper hemifield for monkeys B and J and right hemifield for monkey C). One of the curves was connected to the fixation point (target curve) and the other curve was not (distracter Selleckchem Docetaxel curve). A small change close to the fixation point switched the target and distracter curve (in the example of Figures 2A and 2C the left curve is the target curve but in other trials the right curve was connected to the fixation point). All 23 figure positions × 2 curve configurations were presented in a randomly interleaved

sequence in both tasks. The animals underwent two surgeries under general anesthesia that was induced with ketamine (15 mg kg-1 injected intramuscularly) and maintained after intubation by ventilation with a mixture of 70% N2O and 30% O2, supplemented with 0.8% isoflurane, fentanyl (0.005 mg kg-1 intravenously), about and midazolam (0.5 mg kg-1 h-1 intravenously). In the first operation a head

holder was implanted and a gold ring was inserted under the conjunctiva of one eye for the measurement of eye position. In the second operation, arrays of 4 × 5 electrodes (Cyberkinetics Neurotechnology Systems Inc.) were chronically implanted in areas V1 and V4 (see Figure S1). All procedures complied with the NIH Guide for Care and Use of Laboratory Animals (National Institutes of Health, Bethesda, Maryland), and were approved by the institutional animal care and use committee of the Royal Netherlands Academy of Arts and Sciences. Details about the recording methods and information about the measurement of RFs in V1 and V4 can be found in Supplemental Experimental Procedures. We quantified visual responsiveness by first calculating the spontaneous mean activity, Sp, and the standard deviation, s, across trials in a 200 ms time window preceding stimulus onset. We then computed the peak response, Pe, by smoothing the average response over conditions with a moving window of 25 ms and taking the maximum during the stimulus period (0–600 ms after stimulus onset). The visual responsiveness index was then given by VR = (Pe-Sp)/s.

It is possible that learning to see words and then representing t

It is possible that learning to see words and then representing the results in a format appropriate for language systems takes place in parallel cortical circuits, but it would seem inefficient to expect that the same complex learning takes place in multiple circuits. A conservative position to explain the Target Selective Inhibitor Library current data is that the VWFA has uniquely evolved the capability of providing properly formatted sensory information to language areas (Devlin et al., 2006 and Jobard et al., 2003). Another recent report supports this view, showing that the VWFA circuitry is useful in communicating even somatosensory data to language systems in congenitally blind subjects (Reich et al., 2011). Nevertheless, it

remains possible that circuits not identified in this study are capable of both recognizing the sensory information see more and communicating the information to language (Richardson et al., 2011). If so, the circumstances in which these alternative routes are utilized should be further explored. The format of word representations required by the language system is probably independent of

most basic visual features, such as letter case and font (Dehaene et al., 2001, Polk and Farah, 2002 and Qiao et al., 2010). Our results provide evidence that even when stimulus features initiate activation in different parts of early visual cortex, the VWFA can use the pattern of activity to recognize the presence of a word form. Yet this feature-tolerance cannot be based on learning, because our experience with words is specific to line contours and junctions. Learning in the VWFA and VOT related to word forms may instead be about the statistical regularities between abstract shape representations (Binder et al., 2006, Dehaene et al., 2005, Glezer et al., 2009 and Vinckier et al., 2007), independent of the specific visual features that define these shapes. Feature-independent word form responses in the VWFA parallel feature-independent object responses Rolziracetam in the nearby lateral occipital complex

(Ferber et al., 2003, Grill-Spector et al., 1998 and Kourtzi and Kanwisher, 2001). In the object recognition literature this feature-tolerance is thought to help recognize objects whose detailed properties (e.g., spectral radiance) can vary depending on viewing conditions (e.g., ambient lighting). The need for feature-tolerance is reduced in reading because words are typically differentiated by line-contours, but the capability may exist because the same cortical circuits produce the shape representations used for seeing words and objects. Rather than the VWFA specifically learning feature-tolerance for word shapes, feature-tolerance may be present throughout VOT for all shape recognition tasks, including word form recognition. If feature-tolerant responses for words in humans are a consequence of general visual processing, then one might expect that these representations also exist in homologous regions of non-human primates.

, 2010) Wnt and Hh morphogens play essential and sometimes oppos

, 2010). Wnt and Hh morphogens play essential and sometimes opposing roles in development of the central nervous system and can act to affect both proliferation and cell fate (reviewed in Rowitch et al., 1997, Fuccillo et al., 2006 and Ulloa and Briscoe, 2007). Both pathways are also active in the adult VZ-SVZ and affect self-renewal, proliferation, find more and migration, as discussed above. To date, it is unknown whether these two pathways

interact functionally in this context. It is possible that Wnt may act in concert with FGF signaling and/or in opposition to Shh signaling, as is the case in early nervous system development (Ulloa et al., 2007 and Alvarez-Medina et al., 2008). Going forward,

it will be fascinating to understand how the many growth factor and morphogen-driven pathways active in the SVZ are functionally integrated to affect progenitor proliferation and differentiation. With some exceptions noted above, most of these pathways have been examined in isolation, and determining PI3K Inhibitor Library how these pathways interact will be essential to understanding the normal regulation of neurogenesis. In addition to the wealth of extracellular signaling pathways that are thought to act within the adult VZ-SVZ, intracellular actors, including transcription factors, nuclear receptors, chromatin-modifying complexes, and microRNAs, have been reported to affect the neural stem cell lineage. The transcription factors Dlx2, Mash1, and NeuroD1 are all associated with a neurogenic fate, while Olig2 is primarily gliogenic (Parras et al., 2004, Hack et al., aminophylline 2005, Marshall et al., 2005, Menn et al., 2006, Petryniak et al., 2007 and Gao et al., 2009). However, it is still unclear how niche-provided signals and subsequent intracellular signaling cascades ultimately result in the expression of specific neurogenic or gliogenic transcription factors. Recent work has highlighted the essential role of

epigenetic regulators such as the chromatin-modifying protein Mll1 and the microRNA miR-124 in the control of neurogenesis (Lim et al., 2006, Lim et al., 2009 and Cheng et al., 2009). The orphan nuclear receptor Tlx is also required for neural stem cell self-renewal and may mediate the repression of cell cycle inhibitory factors through the recruitment of Bmi-1 (Sun et al., 2007, Liu et al., 2008 and Liu et al., 2010). The expression of epigenetic regulators like Bmi-1 is altered as the organism ages and stem cell function declines (Molofsky et al., 2003, Molofsky et al., 2006 and Fasano et al., 2009). Although Mll1 and Bmi-1 are broadly expressed within the VZ-SVZ lineage, both proteins appear to function at specific points in this lineage to permit division or neurogenesis.

The results of these experiments are shown in Figure 2B (Callipho

The results of these experiments are shown in Figure 2B (Calliphora)

and Figure 2C (Drosophila). Lobula plate tangential cells respond to single ON or OFF steps imposed on a uniformly illuminated background with an increase in firing rate or a depolarization (see responses to the appearance of the first stripe). The direction selectivity of the motion detection circuit can be observed by comparing the responses to the second stripe with the responses to the first one. For ON-ON and OFF-OFF stimuli (first and second row in Figures 2B and 2C), the response amplitudes are larger when the stimulus sequence is in the Baf-A1 in vivo cell’s PD (red lines) than when the sequence is in the cell’s ND (blue lines). The opposite effect is observed for ON-OFF and OFF-ON stimulus sequences (third and fourth row in Figures 2B and 2C): here, the response to the second stimulus is smaller than the response to the first one when the sequence is in the cell’s PD, and larger than the first one when the sequence is in the cell’s ND. This effect SAHA HDAC order is called “PD-ND inversion” and is illustrated more clearly when the response

to an ND sequence is subtracted from the response to the corresponding PD sequence (black lines in Figures 2B and 2C): for ON-ON and OFF-OFF sequences, a positive signal is obtained; for ON-OFF and OFF-ON sequences, the resulting signal is negative. All this holds true for recordings from the H1 cell in Calliphora as well as for recordings from VS cells in Drosophila (compare Figure 2B with Figure 2C). While the responses to ON-ON and OFF-OFF stimuli can be explained by both a 4- as well as by a 2-Quadrant-Detector (Figures 1B and 1C, respectively), the responses to sequences of opposite sign (ON-OFF, Thiamine-diphosphate kinase OFF-ON) are hard to reconcile with a 2-Quadrant-Detector.

However, the phenomenon of the PD-ND inversion is in agreement with predictions from the Reichardt Detector (Figure 1A) and its mathematical equivalent, the 4-Quadrant-Detector (Figure 1B): for ON-OFF or OFF-ON sequences, signals of opposite signs are multiplied, leading to the observed PD-ND inversion. Therefore, given the splitting of the photoreceptor output into ON and OFF components, these results seem to rule out the 2-Quadrant-Detector (Figure 1C) and rather imply a motion detection circuit of the 4-Quadrant type (Figure 1B). However, the above reasoning rests on two tacit assumptions: (1) information about the absolute brightness is fully eliminated, and only information about the change of the stimulus brightness is passed on to the rectification stage and the subsequent motion detection circuits; and (2) the threshold for the rectification stage is set at exactly the zero point of the incoming signal. As soon as we drop one of these assumptions, the signal separation becomes less strict, and a 2-Quadrant-Detector might respond to stimulus sequences of opposite sign as well.

The above analysis implicitly assumes that the minimum of the cos

The above analysis implicitly assumes that the minimum of the cost function over the allowed range of weights corresponds to a local minimum, so that the first derivative is zero and the second derivatives characterize deviations from the minimum. However, because Dale’s law constrains

the weights to be strictly nonnegative or nonpositive, the best-fit parameters can occur on the boundary of the permitted set of weights. In such cases, we also ATM Kinase Inhibitor chemical structure computed the gradient of the cost function to determine the direction of greatest sensitivity to infinitesimal changes in weights. However, for changes in weights large enough to lead to noticeable mistuning, the increase in the cost function due to linear changes along the gradient direction were much smaller than the quadratic changes determined by the sensitivity matrix (Figure S6G). In addition, because the gradient vector reflected weights that were prevented by Dale’s law from changing signs, its direction corresponded to increasing magnitudes of all zero-valued weights and therefore overlapped with eigenvector 2. Thus, for the circuits analyzed here, the gradient provided little additional information beyond that provided by the sensitivity matrix. This work was supported by NSF grant IIS-1208218-0 (M.S.G., E.R.F.A.), NIH grant

R01 MH069726 (M.S.G.), a Sloan Foundation Research Tofacitinib order Fellowship (M.S.G.), a Burroughs Wellcome Collaborative Research Travel Grant (M.S.G.), a UC Davis Ophthalmology Research to Prevent Blindness grant (M.S.G.), a Wellesley College Brachmann-Hoffman Fellowship (M.S.G.), a Burroughs Wellcome Career Award at the Scientific Interface (E.R.F.A.), and the Searle Scholars program (E.R.F.A.). We thank Guy Major, Jennifer Raymond, Sukbin Lim, Andrew Miri, Brian Mulloney, Michael Wright, Melanie Lee, and Jochen Ditterich TCL for helpful comments on this work and Melanie Lee for computational assistance. “
“We choose between objects based on their values, which we learn from past experience with rewarding consequences (Awh et al., 2012 and Chelazzi et al., 2013).

The values of some objects change flexibly, and we have to search valuable objects based on their consequent outcome (Barto, 1994, Dayan and Balleine, 2002, Padoa-Schioppa, 2011 and Rolls, 2000). On the other hand, the values of some other objects remain unchanged, and we have to choose the valuable objects based on the long-term memory. Since the stable value formed by repetitive experiences is reliable, we may consistently choose the object regardless of the outcome (Ashby et al., 2010, Balleine and Dickinson, 1998, Graybiel, 2008, Mishikin et al., 1984 and Wood and Neal, 2007). Both flexible and stable value-guided behaviors are critical to choose the valuable objects efficiently. If we rely only on flexible values, we would always have to make an effort to find valuable objects by trial and error.

, 2010, Goldberg and Reynolds, 2011, Morris et al , 2004, Raz et 

, 2010, Goldberg and Reynolds, 2011, Morris et al., 2004, Raz et al., 1996 and Smith et al., 2004) and that show stereotyped burst activity on presentation of salient stimuli (Aosaki

et al., 1994 and Matsumoto et al., 2001). We directly tested the intriguing possibility that activation of intralaminar thalamic glutamate inputs to striatum might also drive DA release via a striatal nAChR-dependent mechanism. Indeed, laser activation of ChR2-eYFP-expressing thalamostriatal axons arising from intralaminar thalamus in CaMKII-Cre mice evoked DA release in coronal striatal slices, and this was prevented by nAChR inhibition and, necessarily, glutamate receptor antagonists but not GABA receptor antagonists (Figure 4; n = 4 animals, TTX-sensitive, Ca2+-dependent). ACh-dependent DA signals can Selleckchem MG-132 therefore be driven by the thalamic inputs that synchronize activity in ChIs in vivo. It is interesting in this regard that the relatively “digital” nature of the stereotyped burst activity in the thalamostriatal network that is associated with salient event detection parallels the lack of simple frequency dependence in the ChI activation of DA release seen here. In any event, these data suggest that DA may be important for conveying

salience- or attention-related signals mediated not through changes in DA neuron firing but through activation Selleckchem Ribociclib of DA axons by ChIs and their inputs. Third, we would expect that a ChI-driven DA signal will have key outcomes for DA functions that are encoded by dynamic patterns of activity until in DA neurons themselves. The outcome will depend entirely on the timing of activity in DA neurons relative to ChIs. Pauses in ChIs have been suggested previously to remove a low-pass filter on

DA release during concurrent changes in DA neuron activity (Cragg, 2006). Prior ChI-driven DA release could shunt (limit) the impact of subsequent changes in DA neuron activity, while alternatively, postpause “rebound” facilitation in ChI activity (Aosaki et al., 1995, Apicella, 2007 and Morris et al., 2004), which probably corresponds to increased synchrony in the population, could critically supplement preceding DA signals and promote, for example, the selection of a behavior. In addition, discrete functions for DA could be driven by synchronous activity in ChIs despite an absence of accompanying phasic changes in DA neuron activity, which otherwise would be taken as evidence for functions not requiring phasic DA. Furthermore, what might be the outcome for nicotine action? By desensitizing nAChRs on DA axons, nicotine would be expected to prevent ChI-driven DA release (pilot observations suggest this to be the case, data not shown) and thereby devolve the control of DA release to activity in DA neurons without modulation by ChIs. In this case, DA release might be a more direct reporter of activity in DA neurons than with nAChRs active (Rice and Cragg, 2004).

, 2005 and Visser et al , 1999) Of these regions, superior front

, 2005 and Visser et al., 1999). Of these regions, superior frontal cortex and precentral cortex are involved in top-down cognitive control of processing sensory inputs and actions that guide behaviour (Miller and Cohen, 2001). In addition, precentral cortex and supramarginal cortex are associated with response inhibition abilities, such as those measured with stop signal tasks (Chambers et al., 2009). Although this study did not establish a link with functional impairment, the volume deficits in these cortical regions would suggest disruption of cognitive control functions associated

with atrophy in these regions, congruent with previous findings of cognitive impairments in AUDs (Moselhy et al., 2001). Furthermore, smaller parietal cortex volumes have been associated with frequent buy SB203580 findings of impairments in visual spatial abilities and sensory integration in AUDs (Sullivan et al., 2000). GM reduction in the insula, thalamus and putamen is also consistent with previous studies (Durazzo et al., 2004, Harding

et al., 2000, Kril et al., 1997 and Mechtcheriakov et al., 2007), regions associated with emotion regulation, arousal, attention and appetitive behaviour, functions that have been found to be disrupted in AUDs (e.g., George et al., 2001, Heinz et al., 2007 and Vollstadt-Klein et al., 2010). As expected, we did not find brain regions showing larger volumes Onalespib in AUDs compared to HCs. In contrast to previous VBM studies in AUD, our AUD group consisted of treatment seeking and community based AUDs (Fein et al., 2009, Jang

et al., 2007, Kril and Halliday, 1999, Mechtcheriakov et al., 2007, Sullivan et al., 2005 and Visser et al., 1999). Compared to treatment-seeking alcoholics, treatment-naïve alcoholics have been reported to demonstrate a different drinking trajectory and less for severe levels of lifetime alcohol consumption (Fein and Landman, 2005), as well as lower magnitudes of alcohol-induced cerebral morphological abnormalities (Fein et al., 2002a). We found consistent GM volume reductions in our mixed treatment seeking and community based AUD group. This could be explained by the fact that, although overall our AUD group may have been less severely afflicted, the AUDs had shorter abstinence duration than in most other VBM studies including treatment seeking AUDs (Cardenas et al., 2007, Chanraud et al., 2007, Mechtcheriakov et al., 2007 and Rando et al., 2011). Indeed, abstinence has been shown to lead to a (partial) recovery of GM volumes (Agartz et al., 2003, Bartsch et al., 2007, Wobrock et al., 2009 and Gazdzinski et al., 2005). Further research is needed to test whether AUDs with longer abstinence duration resemble PRGs more on GM volumes than our current AUDs. Based on similarities in neuropsychological profiles between PRGs and AUDs (e.g., Goudriaan et al., 2006), we expected to find a similar pattern of reduced GM volumes in PRGs as in AUDs.

For a majority (n = 167)

of the neuropil transcripts we e

For a majority (n = 167)

of the neuropil transcripts we examined, we observed the opposite Epigenetic activity pattern: most were relatively de-enriched in the 100% glial sample (green in Figure 4A) and showed progressive enrichment as the glial contribution was reduced, relative to the neuropil sample (red in Figure 4A). For a relatively small group of targets, we observed a significant enrichment in the glial sample, these transcripts include some well-established glial genes such as Gfap ( Figure 4B; Table S7). How does the neuropil transcriptome compare to that found in the somatic compartment? As transcription occurs in the nucleus followed by export of the mRNA to the cytoplasm, all neuronal transcripts, regardless of their ultimate destination, reside in the cell body for some period of time. Thus, it is expected that, assuming perfect detection, all dendritic transcripts should also be discovered in the cell body. We compared our neuropil transcriptome to a somata data set, obtained from the microdissection of sister segments comprising the stratum pyramidale (cell body layer) of hippocampal area CA1. Deep sequencing (same protocol as above) of two different somatic tissue samples resulted in 1,099,501 reads that correspond to 8,044 unique mRNAs (Table

S3). We used Nanostring to estimate the relative enrichment of a subset of mRNAs in somata versus neuropil. We varied the relative amount of somatic ADAMTS5 tissue to neuropil tissue and identified a subset of mRNAs that is indeed enriched in the neuropil (Figures 4C and 4D). A unique cluster of mRNAs is also apparently HIF inhibitor enriched in the cell body layer (Figures 4C and 4D). We note here that enrichment in somata is influenced by many variables including transcript abundance, decay rates, and transport rates that have not yet been carefully measured or quantified. Furthermore, relative enrichment in somata does not rule out a dendritic function. For example, the most abundant dendritic mRNA, Camk2a ( Figure 3C), was not detected as a dendritically enriched transcript in two previous studies

( Poon et al., 2006 and Zhong et al., 2006). The neuropil is a composite tissue-comprising dendrites, axons, glial cells, interneurons, and some blood vessels. To refine our list of transcripts to those of dendritic and/or axonal origin we made use of recently published data sets to subtract transcripts enriched in other neuropilar cellular or subcellular compartments (Figure 2; Figure S3). First, we expanded our own list of glial-enriched transcripts with published data on transcripts enriched in astrocytes and oligodendrocytes obtained via cell-type-specific expression of a fluorescent protein (Cahoy et al., 2008 and Okaty et al., 2011) and subtracted them from the neuropil transcriptome (Figure 5A; Table S8).

, 2010) Thus, we hypothesized that DAF-21/Hsp90 and


, 2010). Thus, we hypothesized that DAF-21/Hsp90 and

EBAX-1 may be involved in suppressing the level of endogenous aberrant misfolded proteins during axon guidance. Numerous human diseases have been associated with amino acid mutations, resulting in metastable proteins with temperature-sensitive (ts) misfolding defects (Gelsthorpe et al., 2008, Kjaer and Ibáñez, 2003, Pedersen et al., 2003, Singh et al., 1997 and Vollrath and Liu, 2006). One well-studied HIF inhibitor example is the ΔF508 mutant of cystic fibrosis transmembrane conductance regulator (CFTR) identified in cystic fibrosis patients (Lukacs and Verkman, 2012). The advance in therapeutic treatment of cystic fibrosis has heavily relied on PQC studies of the CFTR ΔF508 mutant. Being susceptible targets of the PQC system, such metastable mutant proteins can serve as sensitized probes to examine the function of PQC regulators. To identify in vivo targets of EBAX-1 and DAF-21, we searched for temperature-sensitive mutants with protein misfolding defects in the slt-1/sax-3 pathway. selleck screening library We found that a previously reported ts mutation of sax-3 (ky200) caused striking temperature-dependent misfolding and mislocalization of the SAX-3 receptor in touch neurons. sax-3(ky200) contains a missense mutation at a conserved proline residue (P37S) in the first immunoglobin-like domain (Ig1) ( Figure 5A) ( Zallen et al., 1998). In the vertebrate Robo1, this amino acid is close to

the contact regions between Slit2 and Robo1 but does not directly mediate their interaction ( Morlot et al., 2007). sax-3(ky200) mutant animals click here showed marginal penetrance of AVM guidance defects at the permissive temperature (20°C), suggesting that most mutant SAX-3 is functional under this condition. At the restrictive temperature (22.5°C), the level of defects significantly increased in sax-3(ky200) mutants ( Figure 5B). As a control, the guidance defects in sax-3(ky123) null mutants did not show temperature dependence. To test whether the temperature sensitivity of sax-3(ky200) mutants is caused by protein misfolding, we examined the expression patterns

of GFP-tagged SAX-3(WT) and SAX-3(P37S) in touch neurons. SAX-3(WT)::GFP and SAX-3(P37S)::GFP were expressed in the wild-type and sax-3(ky200) mutant backgrounds, respectively, to ensure the homogeneity of endogenous and exogenous proteins. In touch neurons at late L1 to early L2 stages, SAX-3(WT)::GFP was predominantly localized on the cell surface at both 20°C and 22.5°C ( Figure 5C). In contrast, SAX-3(P37S)::GFP showed a mixture of cell-surface and cytosolic localization with a mild degree of cytosolic aggregation at 20°C ( Figure 5C). The cytosolic mislocalization and aggregation of SAX-3(P37S)::GFP were exacerbated at 22.5°C and accompanied with a reduction of surface signals that correlated with the aggravated AVM guidance defects in sax-3(ky200) mutants ( Figures 5B and 5C).

We then calculated the correlation coefficient between the observ

We then calculated the correlation coefficient between the observed

response pattern and the predicted response pattern. Note that the fine-scale orientation maps contain both a spatial response component and an orientation-tuning component. To investigate the contribution of these components, we also considered two reduced versions of the pooling model (see Experimental Procedures; Figure S5C). A space-only version was obtained by averaging across orientation at each Neratinib order fine-grid location. This model did not have any local orientation tuning. An orientation-only version was obtained by subtracting the space-only response from the measured data at each fine-grid location, leaving only orientation tuning. Thus, this model did not contain any local spatial information. The predicted response maps for two example neurons

(neurons II and III in Figures 2 and 3) are shown in Figure 7A (panels labeled “prediction”). Maps are shown for three different RF locations for each neuron. For the RF location marked “1”, the left panel shows the empirical data, while the other three panels show the predicted ISRIB responses from the full model and the two reduced models. Shown below the predicted response maps are the corresponding sections of the fine-scale orientation map, which were used to generate the predictions. To take the example of RF location 1 in neuron II, we can see clearly that the selectivity for medium-curvature shapes pointing upward arises from the layout of the fine-scale map; the middle location is tuned to horizontal elements, the upper-left location

Histone demethylase is tuned to elements tilted 45 degrees counterclockwise, and the upper-right location is tuned to elements tilted 45 degrees clockwise (and also vertical). There is a close correspondence between the data and the predicted patterns both for the full model and the orientation-only model. The space-only model performed less well but still explained significant parts of the response (ρ=0.43ρ=0.43 for the space-only model versus ρ=0.58ρ=0.58 for the orientation-only model). Thus, both spatial and orientation components contribute giving the best correlation (ρ=0.67ρ=0.67) for the full model. Only the predictions of the full model are shown for RF locations “2” and “3”. The model correlations (full model only) at each spatially significant location are shown in the lower left panel of Figure 7A. In the case of example neuron III, the local orientation tuning was highly heterogeneous and most of its curvature selectivity could be explained by local spatial tuning alone. As seen for RF location 1, the largest responses occur for composite shapes whose ends fall in the upper part of the fine-scale grid where the spatial response is higher (i.e., on the RF boundary).