Curiously, the kinetics of KARs imposed by Neto protein is remarkably similar to those of NMDARs (see Figure 1). The difference between NMDAR activation kinetics and that of the faster AMPARs provides adequate timing for activation, since BGB324 in vivo the Mg2+ blockade implies that NMDAR activation would not be operative until sufficient membrane depolarization is attained. In contrast, the functional significance of the slower kinetics of KARs is starting to be illustrated by examples that provide comprehensive roles

for such a prolonged current in synaptic integration (Frerking and Ohliger-Frerking, 2002, Goldin et al., 2007, Sachidhanandam et al., 2009 and Pinheiro et al., 2013; see Figure 1). Another striking action of Neto1 and Neto2 is that association with these proteins greatly reduces inward rectification of KAR-mediated currents without modifying Ca2+ permeability (Fisher and Mott, 2012). It seems that three positive charges (RKK) in the C-terminal of Neto proteins preclude internal polyamine blockade of KAR channel. This effect is reminiscent of stargazin in AMPARs (Soto et al., 2007). However, the functional Rigosertib mw implication of this action remains to be defined. Apart from the clear effect of Netos on KAR channel gating and on current amplitudes (see Copits and Swanson, 2012), it remains unclear whether Neto proteins are involved

in KAR targeting to the synapse, although there is weak evidence indicating that this may be possible. Cultured hippocampal neurons express native KARs, but these are not targeted to synapses (Lerma et al., 1997). However, a small proportion of ESPCs may be mediated by KARs when such cells are transfected with Neto2 and GluK1, indicating that exogenous Neto2 may target a small proportion of exogenous GluK1 to synapses (Copits et al., 2011). Similar effects were observed in cerebellar granule cells and with GluK2 (Zhang et al., 2009). However, although GluK2 association with PSD95 is reduced in Neto2 null mice Hydrolase (Tang et al., 2012), the lack of Neto2 expression does

not prevent the presence of endogenous GluK1 or GluK2 in synaptic contacts, despite the fact that synaptic KARs are normally associated with Netos in hippocampal slices. Indeed, KAR-mediated EPSCs in brain slices display distinct kinetics in Neto-deficient animals and EPSCKARs are present in mice even deficient for the two Neto proteins, yet with fast kinetics, consistent with the idea that Netos are not key elements in the targeting of KARs to the synapse (Tang et al., 2011). From these data, it is clear that Netos exert an important influence on KARs, which may vary depending on the subunit combination. However, Neto proteins are not specific to KARs. Indeed, Neto1 was initially identified as a NMDAR interactor.

05; Table S2). Object-responsive activations within the grid were investigated by contrasting intact objects with their scrambled counterparts (Figure 5D; Table S2). In the control group, 66% ± 14%, and similarly in C1, 70% of the sectors in the RH showed object-related responses. Most of the sectors that were not responsive to the presentation of object stimuli were located in anterior and ventral sectors of the grid, thus in cortical regions that likely represent the periphery of the visual field. In SM, only 11% of the RH sectors showed object-related

responses. The number of activated sectors was significantly reduced in SM compared to the control group and to C1 (p < 0.05), but similar to healthy subjects, sectors that were not responsive were located anterior and ventral to the lesion and thus outside retinotopic cortex and http://www.selleckchem.com/products/ipi-145-ink1197.html LOC. Object-selective responses were investigated in an fMR-A paradigm. For 2D and 3D objects and line drawings, the same object was presented 16 times in the adapted condition, while 16 different objects were presented PD0325901 chemical structure once in the nonadapted condition. To investigate object-selective responses, we calculated an adaptation index (AI), which estimates the response difference between

the adapted and nonadapted conditions. A sample time course of fMRI signals for 2D objects is shown for SM in Figure S6. Figure 5E and Table S2 show the grid-sectors exhibiting object-selective responses in the control group, SM, and C1. In the group, 68% ± 13% of the grid in the RH showed object-selective responses,

and in C1, 61% of the grid in the RH showed object-selective responses, the majority of which were located in posterior and dorsal sectors of the grid Beta-glucuronidase and covered LOC. In SM, only 13% of the grid exhibited object-selective responses, which was significantly reduced compared to the control group and to C1 (p < 0.05). The sectors showing object-selective responses collectively covered LOC and were anatomically located dorsal to the lesion site. Patient SM’s LH was structurally intact, which allowed us to investigate whether a RH lesion of object-selective cortex may have consequences on anatomically equivalent locations in the contralesional hemisphere. To examine this issue, the four sectors of the rectangular grid covering the lesion in SM’s RH were centered on the posterior tip of the left lateral fusiform gyrus permitting the comparison of the lesioned RH and mirror-symmetric locations in the structurally intact LH (Figure 5A). Similar to the analysis of the RH, anatomically equivalent locations in the LH of control subjects were also probed.

In the DDM, the decision process ends when the accumulating decision variable reaches a fixed bound. Accordingly, when the decision variable is aligned

in time to the end of the decision process, all of the curves should converge at a common level, regardless of their rate Carfilzomib mw of rise (Figure 3B). Certain FEF and LIP neurons show this behavior (Figure 3F; Ding and Gold, 2012a and Roitman and Shadlen, 2002). However, caudate activity does not (Ding and Gold, 2010; Figure 3D). Instead of converging to a peak level of activity that immediately precedes saccades, average caudate responses converge on a value that is lower than the peak activity achieved during motion viewing. Together, these results imply that the caudate’s contributions to the formation of the decision variable might be limited to early in the decision process. These contributions can causally affect the outcome of the ongoing decision process. To establish this causal role, we used electrical microstimulation in the caudate to bias both the choices and RTs of monkeys performing the dots task (Ding and Gold, 2012b). In relation to the DDM, these effects had two distinguishable components. One component reflected a bias in nonperceptual processes, such that nondecision selleck screening library times (i.e., the components of the monkey’s RT that were not accounted for by the DDM-like decision process, probably

including basic sensory and motor processing) increased for ipsilateral choices and decreased for contralateral choices. This result is consistent with the basal ganglia’s known role in facilitating saccadic eye movements to contralateral targets. The second component GABA Receptor included a decrease/increase in the total amount of accumulated evidence required for ipsilateral/contralateral choices. This component can be interpreted as a caudate-mediated offset in

the value of the decision variable in the DDM and was similar to results from LIP microstimulation, albeit opposite in sign (LIP microstimulation tended to cause a bias toward contralateral choices; Hanks et al., 2006). Neural activity reminiscent of an offset in the initial value of the decision variable was also observed in a small subpopulation of caudate neurons (Ding and Gold, 2010). This type of activity emerges early, well before motion onset. As illustrated in Figure 4A, a positive starting value reduces the total amount of evidence required for the choice with positive decision bound, thus making it more likely for the decision variable to cross that bound and creating a choice bias. This biasing effect is more profound when stimulus strength is low. In other words, on more difficult trials, in which low-coherence motion stimuli do not provide much evidence for either choice, the relative magnitude of the starting value is more predictive of the monkey’s subsequent saccadic choice.

R.), Mayo Foundation and MCF ALS Center donor funds (K.B.B.). R.R. is also funded by NIH grants R01 NS065782 and R01 AG026251. Some TDP-43 analysis was funded by NIH grant R01 AG037491 (K.A.J.). Z.K.W. is partially supported by the NIH/NINDS 1RC2NS070276, NS057567, P50NS072187, Mayo Clinic Florida (MCF) Research Committee CR program NVP-BKM120 price (MCF #90052030), Dystonia Medical Research Foundation, and the gift from Carl Edward Bolch, Jr., and Susan Bass Bolch (MCF #90052031/PAU #90052).The UBC studies were funded by the Canadian Institutes of Health Research (CIHR) Operating Grants #179009 and #74580 and by the Pacific Alzheimer’s Research Foundation (PARF) Center Grant C06-01. G-YRH is supported by a Clinical Genetics Investigatorship award

from the CIHR. A.L.B. is funded by R01AG038791, R01AG031278, the John Douglas French Foundation, the Hellman Family Foundation, and the Tau ABT-199 in vivo Research Consortium. B.L.M. is funded by

P50AG023501, P01AG019724, the Larry Hillblom Foundation, and the State of CA and P50 AG1657303 to B.L.M. and W.W.S. “
“Amyotrophic lateral sclerosis (ALS, OMIM #105400) is a fatal neurodegenerative disease characterized clinically by progressive paralysis leading to death from respiratory failure, typically within two to three years of symptom onset (Rowland and Shneider, 2001). ALS is the third most common neurodegenerative disease in the Western world (Hirtz et al., 2007), and there are currently no effective therapies. Approximately 5% of cases are familial in nature, whereas the bulk of patients diagnosed with the disease are classified as sporadic as they appear to occur randomly throughout the population P-type ATPase (Chiò et al., 2008). There is growing recognition, based on clinical, genetic, and epidemiological data, that ALS and frontotemporal dementia (FTD, OMIM #600274) represent an overlapping continuum of disease, characterized pathologically by the presence of TDP-43 positive inclusions throughout the central nervous system (Lillo and Hodges, 2009 and Neumann et al., 2006). To date, a number of genes have been

discovered as causative for classical familial ALS, namely SOD1, TARDBP, FUS, OPTN, and VCP ( Johnson et al., 2010, Kwiatkowski et al., 2009, Maruyama et al., 2010, Rosen et al., 1993, Sreedharan et al., 2008 and Vance et al., 2009). These genes cumulatively account for ∼25% of familial cases, indicating that other causative genes remain to be identified. Each new gene implicated in the etiology of ALS or FTD provides fundamental insights into the cellular mechanisms underlying neuron degeneration, as well as facilitating disease modeling and the design and testing of targeted therapeutics; thus, the identification of new genes that cause ALS or FTD is of great significance. Linkage analysis of kindreds involving multiple cases of ALS, FTD, and ALS-FTD had suggested that there was an important locus for the disease on the short arm of chromosome 9 (Boxer et al., 2011, Morita et al.

The observation that the two attention axes we measured predicted behavior so well indicates that these were important for performance in this task. Further work will be needed to determine the effects of other cognitive processes on sensory neurons and behavior, and the extent to which the influence of each is dependent on the specifics of the task or behavioral context. In addition to addressing the question

of the similarity of feature and spatial attention, our results show that analyzing the relationship between the responses of populations of neurons and behavior can provide new insight into the mechanisms underlying cognitive processes. Simultaneous recordings from populations of neurons are becoming easier and more popular, but so far, these larger Chk inhibitor data sets have been used primarily to increase statistical power or to examine correlations between pairs of neurons. We used the responses of all of the neurons we recorded simultaneously

to estimate the amount of feature and spatial attention allocated to each stimulus on each trial. These estimates predict behavior on individual trials and are informative about the neuronal mechanisms underlying attention. Capitalizing on natural fluctuations in cognitive states within a task condition can provide insight about the way cognitive processes affect behavior and about the neuronal mechanisms underlying these processes that are not accessible using other measures. In the current study, we used these methods

Sunitinib ic50 to investigate interactions between the behavioral effects of feature and spatial attention as well as the cortical extent of modulation by each type of attention. This information is not available in average responses across task conditions: the structure of the task affects the way that the two types of attention modulate behavior and can also Evodiamine impose blockwise correlations between the amount of attention allocated different locations and features. For example, because exactly one stimulus changed per trial and the identity of the stimulus most likely to change alternated between blocks of trials, our task (and many other behavioral tasks) imposes a blockwise anticorrelation in the average amount of spatial attention allocated to the two stimuli. In contrast, the attention axis method revealed that the amount of attention allocated to each stimulus is in fact independent. Furthermore, looking at the effects of feature and spatial attention on individual trials resolved the question of whether feature and spatial attention are separable by revealing that feature attention modulates behavior even when spatial attention is constant and that either form of attention can dominate behavior. Finally, looking at the relationship between population activity and behavior provides the statistical power to associate the responses of particular groups of neurons with behavior.

Given the average of 15–20 release sites per thalamic axon (average 315 pA uEPSC

divided by average Q of 15 pA) (Hull et al., 2009), these data suggest that each thalamic afferent forms, on average, 4–6 such clusters (schematic, Figure 1C). What are the functional consequences for postsynaptic Ca transients of clustering multiple release sites together? The clustering of release sites suggests that Ca transients at each hotspot should be reliable, spike after spike, and graded, i.e., variable in proportion to Pr. We compared the response of Ca hotspots to single versus repeated stimulation of the thalamocortical pathway. Despite ∼50% depression of Pr by the second of two consecutive stimuli delivered at 1 Hz (as evaluated by the depression of the simultaneously recorded EPSC amplitude; Figure 5A), the second Ca transient at hotspots was very reliable (6% ± 3% failures, n = 7), The same was true for the last Ca transients of a see more train of 10 stimuli delivered at 1 Hz (10th

stimulus, 16% ± 5% failures, n = 8 hotspots from 7 neurons, different set than paired-pulse). Similar results were obtained in adult (>P39) animals (17% ± 2% failure rate, n = 4). This indicates that Ca transients at hotspots are reliable despite large variations in Pr. Decreasing Pr through repetitive stimulation reduced the amplitude of individual Ca transients (remaining Mdm2 inhibitor amplitude of successful Ca transients, 51% ± 3%, n = 11; Figure 5D) as did reducing Pr pharmacologically (baclofen and/or CPA; see above; 44% ± 4%; n = 19, Figure 5D). Importantly, the amplitude of the average of successful Ca transients was proportional to the decrease in Pr (Figure 5D; average remaining Pr 53% ± 2% for paired-pulse, 51% ± 3% for pharmacological reduction), suggesting that the local Ca concentration at hotspots varies in a graded manner with Pr. Are Ca hotspots composed of several spatially isolated Ca microdomains, each generated by one ifoxetine release site, or do all release sites contribute to a common postsynaptic Ca pool? If release sites share postsynaptic glutamate receptors, they by definition would contribute

to a common postsynaptic Ca pool. The low-affinity competitive glutamate receptor antagonist γ-DGG can be used to identify changes in cleft glutamate concentration due to changes in the number of active release sites with shared access to a pool of receptors (Tong and Jahr, 1994 and Wadiche and Jahr, 2001). We used paired pulse stimulation of thalamic afferent to compare the antagonism of γ-DGG on EPSCs generated by high (first pulse) versus low (second pulse) Pr. On average, γ-DGG (1 mM) reduced the first EPSC by 38% ± 3%, and the second EPSC by 56% ± 3% (n = 12; p < 0.0001; seven single thalamic fiber stimulation and five bulk stimulation) (Figures 6A and 6B), indicating changes in cleft glutamate concentration with changes in Pr.

For instance Lenvatinib activation of glutamate receptors (Beattie et al., 2000 and Ehlers, 2000) or increasing neural network activity by membrane depolarization or by unbalancing excitatory and inhibitory inputs to favor excitation (Lin et al., 2000) result in reductions in synaptic receptor accumulation through receptor internalization, whereas selective activation of synaptic NMDARs leads to facilitated AMPAR recycling and membrane insertion (Lu et al., 2001, Man et al.,

2003 and Park et al., 2004). Trafficking-dependent alterations in AMPAR synaptic localization serve as a primary mechanism not only for the expression of Hebbian-type synaptic plasticity (Malenka, 2003, Malinow and Malenka, 2002, Man et al., Pifithrin-�� chemical structure 2000a and Song and Huganir, 2002) but also for the expression of negative feedback-based homeostatic synaptic regulation (Lévi et al., 2008, Sutton et al., 2006, Turrigiano and Nelson, 1998 and Wierenga et al.,

2005). Ultimately, total receptor abundance is determined by a balance between receptor synthesis and degradation. At basal conditions, AMPARs have a half-life of about 20–30 hr (Huh and Wenthold, 1999 and Mammen et al., 1997). Molecular details and signaling pathways involved in AMPAR turnover have not been well studied, but both lysosomal and proteasomal activities have been implicated in AMPAR degradation (Ehlers, 2000, Lee et al., 2004 and Zhang et al., 2009). Enhanced AMPAR degradation is often observed following receptor ubiquitination and internalization (Lin et al., 2011, Lussier et al., 2011 and Schwarz et al., 2010), and under certain circumstances receptor internalization

is a prerequisite for degradation (Zhang et al., 2009). Furthermore, AMPARs can be synthesized locally in dendrites and spines from locally distributed receptor subunit mRNAs and protein synthesis machinery (Grooms Montelukast Sodium et al., 2006 and Sutton et al., 2004). Presumably, local AMPAR degradation in the spine might also occur, thereby enabling a rapid, synapse-specific adjustment in receptor abundance (Fonseca et al., 2006, Hegde, 2004, Segref and Hoppe, 2009 and Steward and Schuman, 2003). A central neuron receives thousands of inputs from presynaptic neurons distributed in a wide range of locations in the brain with varied levels of basal activity. Thus, the intensity of synaptic inputs at a neuron differs from one another, and changes from time to time depending on the cell type and local circuitry of each presynaptic neuron. Homeostatic regulation has been found to occur on the scale of neuronal networks, individual neurons (Burrone et al., 2002, Goold and Nicoll, 2010 and Ibata et al., 2008), or subcellular dendritic regions (Yu and Goda, 2009); but whether it is employed at the single synapse level, crucial in our understanding of synaptic plasticity and neuronal computation as well as higher brain function, remains to be investigated.

In control cells, pretreated with APV only (t = 33.25 ± 4.33 min, n = 8,4), the induction of both LTP and LTD was robust (Figure 3F), indicating the successful removal of the drug. Cells pretreated with APV and isoproterenol (24.7 ± 0.6 min, n = 7,3) exhibited robust LTP and no LTD (Figure 3G), whereas cells ALK inhibitor pretreated with methoxamine and APV (28.0 ± 1.1 min, n = 8,4) showed normal LTD but no LTP (Figure 3H). A two-way ANOVA test (p < 0.001) confirmed the significance of these differences, indicating that suppression of LTP

and LTD by α- and β-adrenergic receptors is initiated and expressed independently of changes in NMDAR function. Subsequently, we evaluated the longevity of the suppression of LTP and LTD. In the experimental setting described in Figure 3A, a 10 min isoproterenol exposure induces a transient suppression of LTD that recovers within 1 hr

of washout (LTD induced at 25.3 ± 0.9 min: 101% ± 2.9%, at 43.4 ± 0.9 min: 90.3% ± 5.0%, at 75.5 ± 8.5 min: 73.6% ± 4.4%. F(2,22) = 14.83, Selinexor solubility dmso p = 0.001) (Figure 3H). To explore whether the suppression could last longer we prolonged the agonist exposure. In slices incubated 1 hr in isoproterenol and tested at least 1 hr after wash out (97 ± 7 min) LTP induction was robust (140.2% ± 13.6%, paired t test: p = 0.017, n = 9) and LTD induction was minimal (100.9% ± 3.9%, p = 0.99, n = 11) (Figure 3H). However, robust LTD was induced if the slices were exposed methoxamine for 10 min prior the pairing (60.4% ± 10.7%, p = 0.008, n = 7), indicating that the β-adrenergic suppression of LTD can be reversed (Figure 3H). Similarly, 1 hr incubation with methoxamine induced a lasting suppression of LTP (LTP: 98.73% after 89.3 ± 8.0 min of wash, p = 0.56, n = 12; LTD: 81.33% ± 2.1%, p < 0.001, n = 12) that was reversed by 10 min exposure to isoproterenol Adenosine triphosphate prior the pairing (163.5% ± 14.5%, p = 0.002, n = 10). Altogether the results indicate that the suppression of LTD and LTD by β- and α-adrenergic receptors can be long lasting, yet reversible. Finally, the pull-push regulation of LTP and LTD raised the question

of whether the suppression of one form of plasticity depends on the upregulation of the other form. To address this issue we studied the effects of methoxamine in a phospho-mutant mouse line that expresses normal associative LTP but impaired associative LTD (Seol et al., 2007). In these mice serine at position 831 of the GluR1 subunit has been substituted by alanine to prevent phosphorylation, hence the mutation affects only the latest stages of plasticity pathway. We confirmed that the mutant has normal pairing-induced LTP compared to wild-type mice (p = 0.426. Figures 4A and 4C) but no LTD (p = 0.008) (Figure 4C). Interestingly, methoxamine suppressed paring-induced LTP (p = 0.0506) (Figures 4B and 4D) in both, wild-type and mutant. Thus, the suppression of LTP does not require the expression of LTD.

The transcription Lumacaftor chemical structure factor gli1, which indicates high levels of pathway activation, is expressed predominantly in the ventral half of the VZ-SVZ ( Machold et al., 2003 and Palma et al., 2005), a region that is associated with the generation of deep granule interneurons and calbindin-positive PGCs ( Ihrie et al., 2011). It is likely that additional signaling pathways are activated in the other subregions of the adult VZ-SVZ and maintain the heterogeneous patterning of neural progenitors. Our knowledge of the anatomy and molecular properties of the adult VZ-SVZ stem cell niche has advanced significantly in the past decade. A number of studies have implicated various growth factors,

neurotransmitters, morphogens, epigenetic regulators, and transcription factors in the maintenance of the stem cell pool, activation of progenitors, and neuroblast migration. We also know that the precise mosaic patterning of the adult VZ-SVZ is present at birth, even before Selleck AZD2281 this germinal region has reached a fully mature state. How the effects of the multiple signaling pathways we describe here are integrated within the progenitors

of the VZ-SVZ is a particularly important challenge for future research. Higher resolution subcellular localization of specific receptors coupled to dynamic studies of lineage progression may provide important clues to when and where different extracellular molecules have their effects. However, many other fundamental questions remain unanswered. First, the lineage of an individual stem cell in vivo has not been traced—we do not know how many times a single stem cell divides, how many of these divisions are self-renewing, or whether this varies depending on location. Recent in vitro studies have begun

to suggest possible lineages and patterns of cell division in intermediate progenitor cells that generate young neurons (Costa et al., 2011). Neurogenesis also decreases significantly with age (Kippin et al., 2005b, Luo et al., 2006 and Molofsky Phenibut et al., 2006). Little is known about how the pattern of stem cell division might change with age and whether the pool of quiescent stem cells is depleted with time or if these cells are prevented from proliferating. The effect of niche-specific factors on the long-term retention of stem cells is largely unknown. Second, although anatomical studies have highlighted the organization of the apical surface of the adult VZ-SVZ, we do not know whether ventricular contact by type B cells is essential for their activation or specification. Although multiple candidate pathways may signal through the primary cilia of type B1 cells, the role of primary cilia in adult VZ-SVZ neural stem cells remains unknown. It is unclear whether signals such as Shh are provided through the cerebrospinal fluid, through more specialized contacts on the ventricular surface, or even through interactions in the basal compartment distant from the primary cilium.

The cells had to meet a specific criteria to be included in the analysis, such as well separated clusters, spikes with broad widths (peak-to-trough width > 300 μs), and presence of complex bursts with 2–7 spikes within 5–15 ms. To ensure that we recorded from the same place cell across sessions, we confirmed that the properties such as autocorrelation, waveform, firing rate and firing location were similar in both sessions. The inhibitory interneurons were easily identified by their high frequency of firing with narrow waveform width and place nonspecific firing. Position data of the mice, tracked by two colored LEDs, were collected at 50 Hz and sorted into 3 × 3 cm GSK2118436 mouse bins. Each sorted place cell

was visualized by plotting its firing rate on top of an animal’s walking path, with heat map colors ranging from blue (little or no firing) to red (high firing rate). A normalized firing rate map was obtained by dividing the spiking activity with

the animal’s position at a particular place. Firing rate maps were smoothed with a filter such that 1 cm equaled 2 pixels. Place field size was measured as in previous studies (Muller et al., 1987). Briefly, we calculated the number of pixels inside the enclosure where place cells fired normalized with the number of pixels the mice visited. Only check details the top 80% of the firing peak with at least 8 contiguous pixels was used and defined as the place field. The pixel area covered by the mice in the box or track enclosure was converted to the respective percentage (%) of total enclosure area for cross-comparison. Two separate measures were used for calculating place field stability. First, a peak-shift measure was used where the firing field peak of session 1 was compared with the firing peak of session 2. Linifanib (ABT-869) A shift (in cm) in peak 1 to peak 2 was calculated by the formula (x1−x2)2+(y1−y2)2,where x1, x2 are the x coordinates and y1, y2 are the y coordinates

of peaks 1 and 2. Second, a cross-correlation measure was used. Prior to applying this measure the firing maps were normalized to a standard size (Figure 4C). From the place field rate map only the in-field firing map (top 80% of the place field peak) was extracted and scaled down to a standard size of about 20 cm using the centroid as the midpoint. This was done to eliminate cross-correlation bias; correlation of larger place fields would produce better stability scores as there are more bins available. Normalized maps from session 1 were compared with session 2 using Pearson’s product moment correlations, given by the formula: r=1n−1∑i=1n[(Xi−X¯σX)(Yi−Y¯σY)]where Xi−X¯/σX,X¯ and σx are the standard score, sample mean, and sample standard deviation of data X, respectively. Spatial coherence estimates smoothness of a place field. It was calculated by correlating the firing rate in each pixel with firing rates averaged with its neighboring 8 pixels.