In order to understand how the microtubule cytoskeleton is organized in the branches of class IV dendritic arborization (da) neurons, we analyzed the dynamics of EB1-GFP comets throughout the entire dendritic arbor in vivo. We expressed UAS-EB1-GFP using the class IV specific promoter, ppk-Gal4 and focused on third-instar larvae

96 hr after egg laying, because although Selleckchem IWR-1 the arbor is well established and the primary branches are stable, the terminal branches are still dynamic ( Lee et al., 2011; Parrish et al., 2009; Ye et al., 2007). Using EB1-GFP to mark the growing plus ends of microtubules, we found that microtubules grew predominantly in the retrograde direction toward the cell body in long (>50 μm) primary branches, consistent with previous reports in other classes of neurons ( Figure 1A; Mattie

et al., 2010; Rolls et al., 2007; Satoh et al., 2008; Song et al., 2012; Stone et al., 2008; Zheng et al., 2008). However, in shorter branches (20–30 μm), we detected mixed microtubule polarity ( Figure 1B), similar to that defined in mammalian neurons ( Baas et al., 1988; Kapitein et al., 2010). Branches of this length corresponded to higher order branches, such as the secondary and tertiary branches, from which terminal branches originate (see Figure S1 available online). In even shorter terminal branches (<20 μm), EB1 comets grew predominantly in the anterograde direction toward the distal tip of the branch ( Figures 1C and 1D). Therefore, microtubule orientation within the dendritic arbor correlates with the length of the dendrite branch. Longer, more established SB203580 branches contain predominately retrograde EB1 comets and shorter branches are composed of mainly anterograde EB1 comets not ( Figure 1E). This held true for branches in both the proximal and distal regions of the arbor. The speeds of the anterograde

and retrograde comets were comparable in all branches, and closely matched the growth rates of microtubules in other systems ( Figure 1F; Akhmanova et al., 2001; Stepanova et al., 2003). In order to generate different patterns of microtubule polarity throughout the dendritic arbor, there likely exist a variety of mechanisms for microtubule nucleation. We therefore wanted to understand the origins of anterograde and retrograde EB1 comets growing specifically within the terminal branches. Anterograde comets originated from three main sources: the parent branch (Figure 2A), the branchpoint (Figure 2B), and within the terminal branch (Figure 2C). EB1 comets growing retrogradely along the parent branch could be directed into a smaller daughter branch and grow anterogradely toward the distal tip; however, this was the least common source of EB1 comets for the terminal branches (20% and 5% of anterograde comets in <10 μm and >10 μm branches, respectively) (Figures 2A, 2D, and 2E).

, 1998), V4 (Mazer and Gallant, 2003), and FEF (Serences and Yantis, 2007 and Thompson and Bichot, 2005). However, neural activities in all these areas are also involved in top-down attentional direction. It is therefore unclear whether the observed neural correlates of saliency are relayed

from brain regions upstream along the visual pathway, and whether they are the cause or the consequence of selection. In particular, because salient visual inputs typically enter awareness, it is difficult to determine whether the observed neural activities represent saliency as such, as opposed to being caused by the consequent perception of the selected stimuli. A dominant view of the saliency map (Itti and Koch, 2001, Koch and Ullman, 1985 and Wolfe, 1994) presumes that saliency selleck chemical results from pooling different visual features, being independent of whether the feature distinction making a location salient is in color, orientation, or other features. Hence, previous attempts to find the saliency map have typically concentrated in higher cortical areas, particularly the parietal cortex, whose neurons, unlike those in primary visual cortex (V1), are less selective to specific visual features. By contrast,

buy PD98059 Li, 1999 and Li, 2002 proposed that V1 (which, notably, projects directly and indirectly to all the previously proposed brain regions for the saliency map [Shipp, 2004]) creates a saliency map via intracortical interactions that are manifest in contextual influences (Allman et al., 1985). According to this theory, the saliency of a location is monotonically related to the highest neural response among all the V1 cells that cover that location

with their spatial receptive fields (relative to the V1 responses to the other locations), regardless of of the preferred feature of the most responsive neuron. Many psychophysical predictions arising from this proposal have been confirmed (Koene and Zhaoping, 2007 and Zhaoping and May, 2007). One particularly interesting confirmation is that an eye of origin singleton, e.g., a bar presented to the left eye among many other bars presented to the right eye, can distract attention away from a very salient visual search target (e.g., a uniquely oriented bar presented to the right eye), even when observers cannot distinguish this eye of origin singleton from other items (Zhaoping, 2008). This supports the V1 theory, because the reason that observers cannot distinguish this singleton is that the eye of origin feature is not represented in any cortical area except V1. Indeed, Wolfe and Franzel (1988) reported that observers found it impossible to find a visual search target defined by its unique eye of origin.

The protocol was originally developed by Kramer et al. (2003) and was subsequently modified by Gilbert et al. (2005). Formalin-fixed, paraffin-embedded tissue sections were ‘demasked’ by immersion in 0.1 M sodium citrate buffer, pH 6.0, and heated at minimum power in a microwave oven (Hinari, LifeStyle,

800W) for two 1-min cycles with a change of buffer. Blocking of endogenous peroxidase and detection of bound antibody was performed using the Universal LSAB2 Horseradish Peroxidase Kit (DakoCytomation, Ely, UK) according to the manufacturer’s instructions. This system uses a biotinylated secondary antibody that forms a complex with peroxidase-conjugated streptavidin, which then reacts with a chromogen [3,3′-diaminobenzidine (DAB)], leading to a brown coloured precipitate. To prevent non-specific binding of antibodies, slides were also blocked for 30 min with 1% bovine serum albumin (BSA) and 5% sucrose in wash buffer [10 mM GW572016 Tris-hydrochloride, pH 8.5; 150 mM sodium chloride, 0.1% (v/v) ‘Tween’-20]. Slides were subsequently incubated for

30 min with anti-WSP primary antibody at a dilution of 1/500 in blocking solution. Adjacent sections cut from the same block were incubated in parallel with normal rabbit serum (Sigma) at the same dilution (negative control). Sections cut from an O. ochengi nodule, a species already known to contain and stain for WSP ( Gilbert et al., 2005), were developed using the same protocol (positive control). Sections were counter-stained with Harris haematoxylin (HD Supplies) and mounted using DPX. Slides were photographed on a Selleckchem Navitoclax Microphot-FX digital microscope (Nikon, Tokyo, Japan). Small sections of dissected aorta were left in PBS

solution for 6 h at ambient temperature. This promoted the adult worms to emerge sufficiently from their tunnels to aid extraction from the aorta wall by applying gentle traction on the worm with forceps. The adult worms were immediately frozen at −80 °C and subsequently transported to the UK on dry ice. The worms were thawed and finely chopped with a scalpel blade, and genomic DNA was extracted from the macerate using DNAzol® reagent (Invitrogen, Paisley, UK) according to the manufacturer’s protocol. In order to visualise the DNA pellets and so minimise losses during washing, 2 μl of Pellet Paint® co-precipitant (Novagen®, VWR the International) was added to the homogenate prior to ethanol precipitation. DNA pellets were dissolved in 30 μl of 8 mM sodium hydroxide and stored at 4 °C. Published oligonucleotide sequences with broad specificity for the 16S rRNA (Casiraghi et al., 2001) and ftsZ ( Werren et al., 1995) genes of Wolbachia were used for custom primer synthesis (Sigma-Genosys, Haverhill, UK). For both assays, the reaction composition was 1 U Thermo-Start™ Taq DNA polymerase, 200 μM each dNTP and 1.5 mM magnesium chloride in 1× High Performance Buffer (all supplied by Abgene, Epsom, UK), with 1 μM each primer and 1 μl DNA template in a final volume of 20 μl.

Pups were perfused at P18–P20; 100 μm brain sections were immunostained with anti-GFP and imaged using confocal microscopy. Dendrite analysis were done using Neurolucida. NDR kinase assays were

done as described (Stegert et al., 2005). Covalent capture of thiophosphorylated substrate proteins was performed as described (Hertz et al., 2010) but with some modifications (see Supplemental Experimental Procedures). We thank Mark Wessels, Peter Soba, and Hye-Young Lee for technical help and Chao Zhang for the valuable suggestion of kinase activation mutations. We thank Jon Trinidad for advice on phosphoproteomics and David Maltby BIBW2992 chemical structure for mass spectrometer instrumentation advice. We thank Jan and Shokat lab members for discussion and critical reading of the manuscript. Mass spectrometry was made possible by National Institutes of Health

(NIH) grants (NCRR RR015804 and NCRR RR001614). Financial support for the purchase of the Linear Trap Quadrupole (LTQ)Velos Orbitrap mass spectrometer was provided by Howard Hughes Medical Institute and an NIH grant (NCRR01614 to A.L.B.). This work was supported by the National Alliance of Schizophrenia and Depression (NARSAD) Young Investigator Award (to S.K.U.), NARSAD Distinguished Investigator Award Selleck PD0325901 (to Y.N.J.), Human Frontiers Science Programfellowship (to W.P.G.), NIH grants (R37NS040929 and 5R01MH084234 to Y.N.J.;RO1EB001987 to K.M.S.), and Genentech predoctoral fellowship (to N.T.H.). K.M.S., L.Y.J., and Y.N.J. are investigators for the Howard Hughes Medical Institute. “
“The TM4SF2 gene on Xp11.4 encodes tetraspanin 7 (TSPAN7), member of the tetraspanin superfamily of evolutionarily-conserved membrane proteins that associate dynamically with numerous partner proteins in tetraspanin-enriched microdomains (TEMs) of the plasma membrane ( Boucheix and Rubinstein, 2001). Tetraspanins regulate cell morphology, motility, and signaling in brain, immune system, tumors,

and elsewhere ( Boucheix et al., 2001). Mutations in tetraspanins leading to loss of function phenotype are relatively rare probably because many tetraspanins overlap functionally ( Hemler, 2005). Nonetheless, specific tetraspanins play critical Cediranib (AZD2171) roles in oocytes during fertilization, fungi during leaf invasion, Drosophila embryos during neuromuscular synapse formation, T and B lymphocyte activation, retinal degeneration, and brain function ( Hemler, 2005). Some TM4SF2 mutations, including TM4SF2 inactivation by X;2 balanced translocation, a premature stop codon TGA (gly218-to-ter) ( Zemni et al., 2000), and a 2-bp deletion (564 delGT) resulting in a premature stop codon at position 192 ( Abidi et al., 2002) are directly associated with nonsyndromic intellectual disability. The gly218-to-ter nonsense mutation and the 2-bp deletion predict a truncated protein lacking the fourth transmembrane domain and cytoplasmic C-terminal tail.

Our results

are instead consistent with a role for beta oscillations in “sensorimotor integration” (Baker, 2007 and Lalo et al., 2007). Similar results have been reported in the STN of parkinsonian humans (Williams et al., 2003), where an instruction cue resulted in a beta ERS only if it was informative about the direction of a subsequent required movement. By contrast, the strong ERD seen after the ERS on Immediate-GO trials appeared more directly linked to motor performance. The ERD was present Doxorubicin mw as rats performed the left/right movement in all trial types, with a straightforward relationship to reaction times, and was absent following cues that successfully prompted animals not to move. A movement-linked beta ERD is consistent with many previous studies of human sensorimotor cortex (Jasper and Penfield, 1949), although in our experiments it occurred slightly

later than expected—near completion of the brief movement rather than initiation. The relatively long latency of the beta ERS places further constraints on its potential functional significance. As it typically trans-isomer solubility dmso occurred at, or just after, the fastest reaction times, the beta ERS does not appear to be a necessary link in a serial chain of subprocesses using sensory input to select and initiate motor output (Meyer et al., 1988). Similarly, it is unlikely that the beta ERS is causally involved in cue-evoked cancellation of movements, as in our Stop-signal task the second beta peak occurred substantially after the “stop-signal reaction time” (SSRT, Table S1 and Figure 4D), an inferred measure of the speed of action cancellation (Logan et al., 1984). Despite this relatively slow pace of cue-evoked beta power change, there was a clear relationship between the

presence of beta oscillations and ongoing behavior, with higher beta power Rutecarpine preceding more slowly initiated movements (see also Chen et al., 2007 and Pogosyan et al., 2009). Our present data are consistent with observations that cortical-BG circuits show both spontaneous and regulated transitions between discrete dynamic states (Berke, 2009), at least one of which is characterized by high beta power. We suggest that beta represents a relatively “stabilized” state during which a change in behavioral program is less likely. As brain circuits establish behavioral plans, entry into the stabilized state would serve the adaptive function of reducing interference from other salient cues and competing alternative actions. Conversely, premature or unregulated entry into beta at critical moments would tend to retard the preparation of intended actions, contributing to both natural reaction time variation in normal subjects, and movement difficulties in PD. This view of beta oscillations builds upon extensive prior findings and theoretical discussion.

Impaired phagocytic efficiency by beclin 1-deficient BV2 cells could also be rescued by recovering beclin MEK inhibitor 1 levels (Figure 2B). To confirm these findings in primary cells, we next isolated microglia from beclin 1 heterozygous knockout mice (beclin 1+/−) ( Figure 2C), which show a 40%–50% reduction in beclin 1 levels ( Pickford et al., 2008 and Qu et al., 2003). In agreement with our lentiviral approach,

beclin 1+/− microglia also showed impairments in phagocytic efficiency when analyzed by flow cytometry ( Figure 2D). To determine if impaired phagocytic efficiency in beclin 1-deficient cells resulted from beads stalling at the cell surface or from a disruption in the kinetics of phagocytosis, we used microscopy and live-cell imaging. We observed that while beads were initially phagocytosed at a similar rate ( Figure S2A), beclin 1-deficient BV2 cells were less able to phagocytose subsequent beads ( Figure S2B and Figure 2E). Quantification of cell migration confirmed that beclin 1-deficient cells have a similar migratory capacity as control cells, indicating that impaired movement VX-809 research buy is not responsible for phagocytic deficits ( Figure 2F). Instead, our data suggest that beclin 1 deficiency impairs the ability of cells to phagocytose subsequent

beads beyond the initial phagocytic event (see Figure 2G for representative live-cell images), resulting in overall reduced phagocytic uptake ( Figure S2C). Phagocytosis is initiated by numerous receptors that recognize molecular structures on extracellular substrates. Upon binding and internalization of substrates, phagocytic receptors are recycled back to the cell surface to be used again. Accordingly, disruptions in Edoxaban phagocytic receptor recycling have dramatic consequences on phagocytic efficiency (Chen et al., 2010). To determine if reduced phagocytic efficiency seen in beclin 1-deficient cells is due to

changes in phagocytic receptor dynamics, we used an established receptor recycling assay (Mitchell et al., 2004) (Figure 3B). Indeed, beclin 1-deficient BV2 cells showed a prominent reduction in recycling of the phagocytic receptor CD36 (Figures 3C and 3D), a class B scavenger receptor involved in phagocytosing a wide range of substrates, including Aβ (El Khoury et al., 2003) and latex beads (Figure 3A). Primary microglia obtained from beclin 1+/− mice also showed a similar deficit in CD36 recycling ( Figures 3E and 3F). Importantly, flow cytometric analysis demonstrated that beclin 1 shRNA did not affect baseline cell surface expression of CD36 in the absence of ligand ( Figure 3G). Additionally, because the phagocytic receptor Trem2 has been reported to recycle ( Prada et al., 2006) and is a risk factor for AD ( Guerreiro et al., 2013 and Jonsson et al.

Lentivirus SP600125 in vitro expressing shRNA-HCN1 was infused in the CA1 region of the dorsal hippocampus, which expressed on 7 days postinfusion (DPI) and up to at least six months (Figure 1B) and spread mediolaterally (about 0.7–1.0 mm) and anteroposteriorly (about 1.2–1.6 mm)

(Figure 1C). We quantified the local silencing efficiency of HCN1 protein by immunohistochemistry and western blotting. The HCN1 protein expression was significantly decreased without alteration in HCN2 and MAP2 protein expression in the shRNA-HCN1-infected region as compared to non-infected or shRNA-control-infected CA1 regions (Figures 2A–2D). Quantification of protein expression from isolated lentiviral shRNA-HCN1-infected dorsal CA1

region showed a 58% reduction in HCN1 protein PI3K Inhibitor Library concentration expression without change in HCN2 and β-tubulin protein expression as compared to shRNA-control-infected region (Figure 2E), suggesting specificity for knockdown of HCN1 channels. To determine whether silencing of HCN1 gene had an effect on the physiology of the dorsal CA1 pyramidal neurons, Ih-sensitive electrophysiological parameters were measured using the whole-cell current-clamp method ( Narayanan and Johnston, 2007; Figures 3 and S2). ShRNA-HCN1-infected CA1 pyramidal neurons had hyperpolarized resting membrane potentials ( Figures 3C), higher steady-state input resistance ( Figure 3D), and slower membrane time constant ( Figure 3E) than noninfected or shRNA-control-infected CA1 pyramidal

neurons. For proper comparison between groups, we held membrane potentials at −65 mV with current injection and compared electrophysiological properties ( Figures 4 and S3). ShRNA-HCN1-infected CA1 pyramidal neurons had less voltage sag ( many Figure 4A) and lower resonance frequency ( Figure 4B) compared to noninfected or shRNA-control-infected CA1 pyramidal neurons. In addition, shRNA-HCN1-infected CA1 pyramidal neurons generated more action potentials in response to depolarizing current steps (30–300 pA in 30 pA increments for 750 ms) ( Figure 4C), suggesting increased cellular excitability ( Shah et al., 2004). Similar results, however, were also obtained with neurons at their normal resting potentials ( Figure S2). To examine subthreshold synaptic integration (αEPSP), the response to repetitive current injections similar to multiple excitatory postsynaptic currents were measured using a train of 5 alpha current injections (α = 0.1, 20 Hz) ( Brager and Johnston, 2007; Dembrow et al., 2010; Poolos et al., 2002). ShRNA-HCN1-infected CA1 pyramidal neurons had larger αEPSP summation than noninfected or shRNA-control-infected CA1 pyramidal neurons ( Figure 4D). In agreement with our biochemical results, these data indicate that silencing of the HCN1 gene by shRNA-HCN1 produced electrophysiological changes consistent with a reduction in Ih.

, 2009) to compensate

for the individual differences using a nonrigid mapping software, JIP. Data were analyzed as described above for individual monkeys. Two ROIs were then defined in left and right TEpv between A0 and A4 in or on the lateral bank of the OTS, by taking all the voxels selectively more active for Learned symbols than for Untrained shapes and Faces within this subregion (114 voxels). Software for stimulus presentation BMS-754807 price and reward delivery was developed in-house and was written in C++. All experiments were done in accordance with procedures approved by the Harvard Medical School Standing Committee on Animals. This work was supported by NIH grant EY16187. We thank Wim Vanduffel for much help developing scanning technology, Tristram Savage for monkey training, and Winrich Freiwald, Doris Tsao, and Heather Sternshein for advice and comments. “
“(Neuron 68, 473–487; November 4, 2010) In the Supplemental Experimental Procedures related to this article, we erroneously stated that the patch-pipette solution

used for Ca2+-uncaging experiments contained, among other ingredients, 110 mM Cs-glutamate and 20 mM Cs-HEPES. This is incorrect. Instead, we used 110 mM K-gluconate and 20 mM K-HEPES. We apologize for this error. “
“(Neuron 73, 35–43; January 12, 2012) In the legend of Figure 4E, the statement “Values above 0 represent higher expression in PV than SST” should have been “Values above 0 represent higher expression in C59 wnt SST than PV.” This has now been corrected in the article online, and we regret the error. “
“The treatment of schizophrenia has relied heavily on antipsychotic drugs (APDs) that primarily impact on the dopaminergic system, principally at D2 receptors (Strange, 2001). APDs act with high affinity (in the low nanomolar

range) with clinical efficacy closely matching their published potency at D2 receptors (Strange, 2001). However, some aspects of APD action are difficult to explain based solely on their action at D2 receptors. For example, the therapeutic benefit of APDs increases slowly during treatment, Calpain an occurrence which correlates with their accumulation in tissue (Agid et al., 2003). APDs also inhibit voltage-gated sodium (Ogata and Narahashi, 1989) and calcium channels (Sah and Bean, 1994), implicating them in the control of neurotransmitter release. However, the clinical relevance of this potential presynaptic mechanism of action was largely dismissed, since it requires micromolar concentrations of APDs. It has been known for years that APDs such as clozapine and haloperidol exhibit increased accumulation in brain (Strange, 2001). This phenomenon has been ascribed to the fact that many APDs are weak bases, allowing them to accumulate in acidic intracellular compartments such as endosomes or synaptic vesicles (SVs) (Rayport and Sulzer, 1995).

To investigate which model best described the data, we computed the Bayesian evidence E  m or probability of the model given the data for each model, using the Laplace approximation ( Kass and Raftery, 1995): equation(Equation 6) Em≈logp(θˆm)+logp(c1:T|θˆm)+12Gmlog2π−12log|Hm|.This quantity, like the Bayesian Information Criterion ( Schwarz, 1978), which can be derived from it via a further approximation) scores each model according to its fit to the data, penalized for overfitting due to optimizing the models’ parameters. Here, θˆm are the best fitting MAP parameters, p(θˆm) is the value of the prior

on the MAP parameters, p(c1:T|θˆm) is the likelihood of the series of observed choices on trials 1-T, Gm is the number of parameters in CP-868596 order the model m, and |Hm| is the determinant of the Hessian matrix of the second derivatives of the negative log posterior with respect to the parameters, evaluated at the MAP estimate. This Bayesian evidence can then be used to compare models of different complexity by correctly

penalizing models for their differing VE-822 solubility dmso (effective) number of free parameters. Having computed this score separately for each subject and model, to compare the fits at the population level, we used the random-effects Bayesian model selection procedure (Stephan et al., 2009), in which model identity is taken as a random effect—i.e., each subject might instantiate a different model—and the relative proportions of each model across the population are estimated. From these, we derive the exceedance probability XPm, i.e., the posterior probability, given the data, that a particular model m is the most common model in the group. To assess evidence for dose-dependent effects of the DAT1 polymorphism on any of the model parameters of the best-fitting model, we used Jonckheere-Terpstra for ordered alternatives, a nonparametric test due to non-Gaussianity of the parameters. Significance is reported at a very strict Bonferroni-corrected

significance level of 0.0083 (2 genes × 3 parameters). For completeness, we also tested until whether fitted parameter values in the losing model differed with DAT1 genotype. To assess whether the model could replicate the behavioral findings, we generated trial-by-trial choices using the fitted parameters of the best fitting model. We then analyzed these choices in the same way as the original data, again using robust regression analyses. We thank Sabine Kooijman for logistic support; Angelien Heister, Remco Makkinje, and Marlies Naber for genotyping; and Bradley Doll, Sean Fallon, Michael Frank, Guillaume Sescousse, and Jennifer Cook for insightful discussions and feedback. This work makes use of the Brain Imaging Genetics (BIG) database, first established in Nijmegen, the Netherlands, in 2007.

To couple the initial linear-nonlinear system to the kinetics block, the output of the nonlinearity, u(t), scales one or two rate constants. Although this means that the transition rate is proportional to the nonlinearity output, a higher-order dependence—such as the dependence of vesicle release on a higher power of the calcium concentration—can be captured in the nonlinearity itself.

We fit LNK models using a constrained optimization algorithm (see Experimental Procedures). The filter and nonlinearity were reduced to a set of 20 parameters, and the kinetics block contributed 5 parameters. The activation rate ka was scaled by the input, and most other rate constants were fixed. In addition, to capture the contrast dependence of the rate of slow adaptation, the input scaled the rate EGFR inhibitor of slow recovery ksr. The motivation for scaling of the slow rate constant by the input is discussed further below. We compared the LNK model output to the cell’s membrane potential response across the entire recording (300 s). The model accurately captured the response at all times,

including contrast transitions at both decreases and increases in contrast (Figure 2C, Figure S1). The correlation coefficient between the model and the response was 88 ± 4% (90 ± 2% for bipolar cells [n = 5], 89 ± 4% for BMS-354825 order amacrine cells [n = 9], and 86 ± 4% for ganglion cells [n = 7]), mean ± SEM. We then compared these values to the intrinsic variability of each cell by repeating a stimulus sequence two to three times. The accuracy of the model was nearly that of the variability

between repeats of the unless stimulus, which was 90 ± 5% (92 ±2% for bipolar cells, 92 ± 4% for amacrine cells, and 89 ± 6% for ganglion cells) (Figures 2D and 2E). Thus, the LNK model accurately captured the membrane potential response to changing contrast for inner retinal neurons. We then assessed how well the LNK model captured adaptive properties by fitting LN models to both the data and to the LNK model. Examining the temporal filters of these LN models, the LNK model captured the fast change in temporal processing between low and high contrast (Figure 3A). In addition, the LNK model captured fast changes in sensitivity between low and high contrast as well as fast and slow changes in baseline membrane potential (Figure 3B). Across a population of cells, the LNK model closely matched the temporal filtering and average overall sensitivity of the cell’s response across the full range of contrasts (Figures 3C and 3D). After a contrast step, the LNK model matched the fast change in average membrane potential of a cell across a range of contrast transitions (Figure 3E). Finally, the LNK model matched slow changes in baseline as the model matched the near steady-state average membrane potential value of a cell at the end of 20 s of constant contrast (Figure 3F).