, 2011), leading to the features of the disease (Schon and Area-Gomez, 2010). One other neurodegenerative disease that may be associated with MAM dysfunction is CMT, which can be caused by mutations both in MFN2 (Chen and Chan, 2009) and in ganglioside-induced

buy Osimertinib differentiation-associated protein 1 (GDAP1) (Pedrola et al., 2005), which interacts with MFN2 (Niemann et al., 2005). MFN2, like mitofusin-1 (MFN1), is required for mitochondrial fusion (Chen and Chan, 2009). However, a portion of MFN2 is enriched in the MAM, where it is required for the tethering of ER to mitochondria (de Brito and Scorrano, 2008). We note that CMT mutant MFN2 expressed in cultured dorsal root ganglion neurons induced abnormal clustering of fragmented mitochondria, as well as impaired axonal transport of mitochondria (Baloh et al., 2007). Perhaps these abnormalities resulted from an underlying defect in ER-mitochondrial communication. The study of MAM is a nascent field that has just begun to be recognized as a contributor to neurodegeneration, and likely will expand beyond the diseases cited above. For example, there may be a “MAM connection” in at least two other diseases in which the relevant proteins—both involved in phospholipid metabolism—appear to be enriched

in the MAM. These are SCA due to mutations in PPP2R2B, a regulatory subunit of protein phosphatase 2A (Giorgi et al., 2010) that promotes mitochondrial fission (Dagda et al., 2008a), presumably via MAM-localized Alpelisib clinical trial FIS1 (Iwasawa et al., 2011), and PD due to mutations in subunit β of the calcium-independent phospholipase A2 (iPLA2β; gene

PLA2G6), which plays a key role in ER-mitochondrial PD184352 (CI-1040) crosstalk during ER stress-induced apoptosis ( Lei et al., 2008). It would thus be fascinating to see if future studies on PPP2R2B and iPLA2β provide insight into a potential link between MAM and neurodegeneration in SCA and PD, and perhaps even beyond. Besides alterations in trafficking and in ER-mitochondrial communication, mitochondria can also fail to reach their destinations due to dysregulation of quality control systems. The cell has surveillance mechanisms to eliminate mutated, unfolded, and otherwise unwanted proteins, via autophagic and ubiquitin-proteasome systems located in the cytosol. In a similar manner, unwanted mitochondria can be disposed of, and their contents recycled, by mitophagy. Although there is currently no evidence that mitochondria contain proteasomes, they do have mechanisms to eliminate misfolded or unneeded polypeptides, via, for example, the AAA (ATPase associated with diverse cellular activities) protease paraplegin/SPG7 and the paraplegin-related protease AFG3L2, and their regulators, the prohibitins PHB and PHB2 ( Osman et al., 2009).

So far, direct electrophysiological recordings from granule cell dendrites have not been possible, due to the small diameter of these processes (approximately 0.8 μm in distal and medial molecular layer, Hama et al., 1989). We used combined two-photon excitation fluorescence and infrared-scanning gradient contrast (IR-SGC, Figure 1A), suitable for recording from thin neuronal processes (Nevian et al., 2007), to obtain MDV3100 purchase dual somatodendritic recordings from granule cells. We first studied the attenuation of action potentials evoked by somatic

current injection that back-propagated into granule cell dendrites (bAPs, Figures 1B and 1C, see insets for magnifications). The bAP amplitudes decreased strongly toward more remote dendritic recording sites (n = 20, Figure 1D), with an attenuation length constant of 86.0 ± 8.5 μm. This corresponds to a much steeper attenuation than reported either for pyramidal cell main apical (Golding et al., 2001) or basal dendrites (Nevian et al., 2007). When the bAP amplitudes were plotted over the somatodendritic distance normalized to the total length

of the dendrite, the attenuation length constant was 0.31 ± 0.04 BIBW2992 clinical trial (n = 14, Figure 1E). Using this analysis, attenuation normalized to total dendritic distance seemed to be similar to both pyramidal cell apical and basal dendrites (cf. Figure S3B in Nevian et al., 2007). Concomitantly,

the delay of the action potential peaks increased (Figure 1F). The average conduction velocity of action potentials back-propagating into granule cell dendrites was calculated from the action potential peak delays, yielding 149.3 ± 2.0 μm·ms−1. This conduction velocity is markedly different when compared with pyramidal cell apical dendrites (approximately 500 μm·ms−1; Stuart et al., 1997a) and is also lower than the estimates for basal dendrites (approximately 200 μm·ms−1; Antic, 2003 and Nevian et al., 2007). We also observed pronounced distance-dependent next broadening of bAPs, which manifested in a decrease of the maximal rate of rise of bAPs (δV/δt, Figure 1G) together with an increase in bAP half width (Figure 1H). We next studied how bursts of action potentials back-propagate into granule cell dendrites. In our recordings of bAPs during repetitive firing induced by prolonged somatic current injections, we had already observed that the amplitudes of individual bAPs stayed constant during trains of action potentials (Figure 1C, see red dendritic voltage recording). This is in contrast to pyramidal cells, in which a pronounced amplitude reduction during a train of action potentials due to slow inactivation of dendritic voltage-gated Na+ channels was described (Colbert et al., 1997, Jung et al., 1997 and Spruston et al., 1995).

Reaction times and their correlations are learn more not necessarily due to processes that support visual-motor performance

and could be due to several other factors, including processing bottlenecks (Pashler, 1984), common sensory inputs (Lee et al., 2010), and nonspecific influences such as motivation or arousal (Boucher et al., 2007). Recent behavioral and computational modeling work, however, indicates that as saccade and reach movements are dissociated in time, correlations in RTs decay rapidly. RT correlations cannot be fit by a family of models featuring nonspecific interactions and are best fit by models invoking specific interactions between movement representations (Dean et al., 2011). Consequently, saccade and reach RT correlations may be due to interactions that form an effectively shared movement representation. We provide convergent evidence that beta-band signals reflect movement preparation shared between saccades and reaches, which may be sufficient for generating RT correlations and could ultimately

influence movement initiation. The relationship between coordination and RT correlations is likely to involve areas in addition to PPC. PPC works in concert with other areas that prepare and initiate movements, including click here areas in the frontal cortex and basal ganglia (Hanes and Schall, 1996 and Requin and Riehle, 1995). PPC also contains direct connections to the cerebellum (Prevosto et al., 2010), MycoClean Mycoplasma Removal Kit a structure that has been implicated in the timing of coordinated movements (Miall and Reckess, 2002). If the RT selectivity of beta-band activity we observe is also present in other areas, this aspect of beta-band activity may reflect processing across a network of areas that work together to control the timing of movements and coordinate saccades with reaches. Several other lines of convergent evidence support the hypothesis that beta-band activity reflects distributed processing. Correlated beta-band LFP activity is present across long-range circuits (Rougeul et al., 1979) and could

underlie long-range communication between brain regions (Roelfsema et al., 1997, Brovelli et al., 2004, Bressler et al., 1993 and Donner and Siegel, 2011). Beta-band activity may be involved in bottom-up/top-down influences (Buschman and Miller, 2007) and maintaining a motor state (i.e., the status quo) (Engel and Fries, 2010), thus leading to slower response. Beta-band activity is widely modulated during movement tasks (Sanes and Donoghue, 1993) and could be related to attended motor behavior (Bouyer et al., 1987) and sensory-motor integration (Murthy and Fetz, 1992). Beta-band LFP activity in the human and monkey motor cortex may work to influence processing of visual cues and targets (Reimer and Hatsopoulos, 2010, Rubino et al., 2006 and Saleh et al., 2010).

So in mice where we did not record from PV cells we used this range of light intensity, i.e., light intensity was set to 0.05–0.1 mW/mm2, and increased until change in the activity Pyr cells was observed. The population response of the visual cortex to visual stimuli was monitored using local field potential recordings during this process. Light intensities never exceeded 1 mW/mm2. When recording from PV cells while photo stimulating Arch or ChR2 (Figure 2)

cortical illumination started before the visual stimulus buy Tariquidar (to monitor the effect on spontaneous activity) and ended before the end of the visual stimulus (to determine the kinetics of recovery to visually evoked firing rates). Spontaneous spike rate was calculated as the average firing rate during a 0.5 s period before the presentation of the stimulus. The visual response to a given stimulus was the average

rate over the stimulus duration or over the period when both cortical illumination and visual stimulus occurred (1–2 s). Orientation selectivity index (OSI) was calculated as 1 − circular variance (Ringach et al., 1997). Responses to the 12 grating directions were fit with orientation tuning curves i.e., a sum-of-Gaussians (Figure 1, Figure 3 and Figure 4). The Gaussians are forced to peak 180 degrees apart, and to have the same tuning sharpness (σ) but can have unequal height (Apref and Anull, to account for direction selectivity), and a constant baseline B. The tuning sharpness was measured as Ergoloid σ (2 ln(2))1/2, Panobinostat molecular weight i.e., the half-width at half height (HWHH). Direction selectivity index (DSI) was calculated as (Rpref – Rnull) / (Rpref + Rnull), where Rpref is the response at the preferred direction and Rnull is the response 180 degrees away from the preferred direction.

Contrast-response curves were fit with the hyperbolic ratio equation ( Albrecht and Hamilton, 1982): R(C) = Rmax cn / (C50n + cn) + Roffset, where c is contrast, C50 is the semisaturation contrast, and n is a fitting exponent that describes the shape of the curve, Rmax determines the gain, and Roffset is the baseline response. To obtain the threshold-linear fit, we first computed a summary of Pyr cell responses in layer 2/3. The tuning curves of all cells were aligned to the same preferred orientation, a nominal value of 0 degrees and the maximal response was scaled to a nominal value of 100% (Figure 4A). We then plotted the median Pyr cell response measured during the suppression or activation of PV cells against the median response measured in the control condition (Figure 4B). The bootstrapped distribution of median responses was used to calculate errors bars in OSI, DSI, and HWHH. Please see Supplemental Experimental Procedures for more details. The membrane potential tuning, or net depolarization, as a function of orientation, θ, was modeled as: ΔV(θ)=gLRL+gE(θ)RE+gI(θ)RIgL+gE(θ)+gI(θ)−Vr gx=gmin+(gmin−gmax)e−θ22σ2.

We have defined an empty varicosity as any varicosity that was labeled by Alexa-594 but contains no ApNRX-GFP (see Experimental Procedures). Such empty varicosities represent 45.6% ± 5.5% (11.0/25.0 varicosities, n = 15) of the total varicosities. When cells were reimaged 24 hr after 5-HT treatment, 46.4% ± 7.4% (5.6/11.0

varicosities) of the empty varicosities were filled with ApNRX-GFP. There was little change in the distribution of ApNRX-GFP over a 24 hr in control cultures that were not treated with 5-HT. We quantified the distribution of ApNRX-GFP enrichment in the total this website population of sensory neuron varicosities. We found that 5-HT treatment that leads to LTF results in a net increase in the percentage of varicosities highly enriched in ApNRX-GFP (75%–100% enrichment group: before 5-HT, 9.1% ± 2.3% versus after 5-HT, 16.1% ± 3.1%, n = 15, p < 0.05) and a net decrease in the percentage of varicosities containing little or no ApNRX-GFP (0%–25% enrichment group: before 5-HT, 67.3% ± 4.1% versus after 5-HT, 51.4% ± 5.8%, n = 15, p < 0.01). In contrast, there were no significant changes in control groups that were not treated with 5-HT (75%–100% enrichment group: before 5-HT, 11.6% ± 3.1% versus after 5-HT, 8.3% ± 1.2%, n = 10, p = 0.17 and 0%–25% enrichment group: before 5-HT, 58.2% ± 6.6% versus after 5-HT, 65.6% ± 6.1%, n = 10, p = 0.22) (Figure 4C). These results indicate that a

subcellular redistribution of ApNRX accompanies the synaptic remodeling AZD2281 chemical structure and growth that is induced by 5-HT in Aplysia sensory-to-motor neuron cocultures. To investigate the consequences of depleting ApNLG mRNA in sensory-to-motor neuron cocultures, we used antisense oligonucleotides to ApNLG to investigate the consequences of depleting ApNLG mRNA in the motor neurons of sensory-to-motor neuron cocultures (Figure S3). Three hours after initial measurements of EPSPs and injection of the antisense oligonucleotide to ApNLG (50 ng/μl) in the postsynaptic motor neuron, we treated cultures

with five pulses of 5-HT (10 μM) and measured EPSPs again 24 hr after 5-HT treatment. Injection of the antisense oligonucleotide to ApNLG leads to a significant reduction of LTF at 24 hr, but the injection else of sense oligonucleotide did not have any significant effect on LTF (Figure 5A; % increase in EPSP amplitude: 5-HT 87.6 ± 13.4, n = 16; 5-HT + sense 95.9 ± 18.5, n = 8; 5-HT + antisense 32.0 ± 10.0, n = 28, p < 0.01 versus 5-HT). Basal synaptic transmission also was not affected by the oligonucleotide injections (% increase in EPSP amplitude: no injection –11.4 ± 7.4, n = 17; antisense alone –15.9 ± 10.5, n = 10; sense alone 6.7 ± 10.0, n = 6). Next, we treated cultures with one pulse of 5-HT (10 μM) for five minutes, 12 hr after oligonucleotide injections into the motor neurons, to induce short-term facilitation (STF) and measured EPSPs again 5 min after the 5-HT treatment (Figure 5B; % increase in EPSP amplitude: no injection –4.5 ± 7.

08, df = 69) in knockout mice (percent area = 29% ± 2.5%) compared to control mice (22.9% ± 1.3%). Similarly in the track, CA3 place fields were 22.9% larger (p = 0.033, t = 2.19, df = 47) in knockout mice (percent area = 33.7% ± 2.35%) compared to control mice (27.4% ± 1.55%; Figure 3B, right). Given the small contribution of HCN1 to CA3 neuron properties, the difference in place field size in the CA3 region of knockout compared to control mice is likely attributable to a change in input from the entorhinal cortex, where HCN1 deletion leads to an increase in grid field size and spacing Anticancer Compound Library manufacturer (Giocomo et al., 2011). By contrast the finding

that the change in place field size is about twice as great in CA1 versus

CA3, (box: p = 0.043, t = 2.04, df = 152; track: p = 0.037, t = 2.10, df = 117), likely reflects the difference in expression levels of HCN1 in these regions. We did not find any significant difference between peak firing rates of place cells in control mice (Figures 1A, 1B, 2A, and 2B) versus knockout mice (Figures Roxadustat mw 1C, 1D, 2C, and 2D). This is also evident from four representative 3D plots (Figure 1 and Figure 2) from each group of mice in CA1 and CA3 regions. HCN1 is also expressed in inhibitory basket cell interneurons in the hippocampus (Aponte et al., 2006). We therefore examined whether interneurons play a role in regulating place field size. Properties of interneurons cannot be analyzed by the approach used to characterize second place cell firing, as the interneurons do not have well defined firing rate peaks or complex spike bursts. Hence we looked at the spike-timing of interneurons and place cells in both CA1 and CA3. We analyzed the intrinsic spike frequencies

of theta modulated place cells and interneurons of CT and KO mice by calculating the spike-time autocorrelation histogram. It has been well established in previous studies that the intrinsic spike frequencies of a cell become slower if place fields expand and faster if place fields shrink (Maurer et al., 2005). The intrinsic spike frequencies of pyramidal neuron place cells were slower in KO mice compared to CT mice in both CA1 (p = 0.006, t = 2.78, df = 155) and CA3 (p = 0.034, t = 2.14, df = 118) regions of hippocampus, consistent with the larger place fields in the KO mice. In contrast, there was no change in intrinsic spike frequencies of CT and KO interneurons in either CA1 or CA3 regions (Figure S3). This indicates that changes in interneuron firing may not contribute to the change in place field size observed in the HCN1 KO mice. The increase in place field size upon HCN1 deletion is somewhat surprising given the enhanced spatial learning and memory observed in the KO mice (Nolan et al., 2004). We therefore examined the influence of HCN1 on stability of the place fields by comparing the place fields from session 1 with those recorded 24 hr later during session 2.

Currently, little is known about the quantitative aspects of mRNA localization and translation in neurons. For example, how many RNA molecules are needed to provide a functionally significant amount of protein? How many proteins are synthesized from a single mRNA? One might speculate that some

classes of proteins, such as cytoskeletal, would be translated much more than others—such as receptors or channels—and transcript abundance could reflect this difference. In theory, just a few new channel or receptor proteins could be sufficient to alter signaling characteristics within a neuronal microdomain. In addition, a low abundant transcript could be stable and translated with high efficiency. Thus, low-abundance transcripts could exert a significant physiological effect and should not be overlooked in profiling analyses. This Ibrutinib ic50 also raises the intriguing question of whether translation from check details monosomes, rather than polysomes, may be more common in distal neuronal compartments where there could be demand for a few highly localized proteins. New high-resolution single molecule detection methods (Cajigas et al., 2012 and Park et al., 2012) and live-imaging methods for translation (Chao et al., 2012) will

be valuable when answering these sorts of questions. With the advent of TRAP (translating affinity purification) technology (Heiman et al., 2008) it will be possible in the future to answer this question in specific neuronal compartments of specific subsets of neurons. For example, cell-type specific Cre-driver lines can be crossed with the RiboTag mouse (Sanz et al., 2009), which expresses HA-tagged endogenous ribosomal protein (Rrl22), thereby generating mice with specific neurons

expressing HA-tagged ribosomes. These can be isolated from mouse brains by immunoprecipitation at different ages and under different conditions (and diseased), and RNA-Seq analysis can identify the ribosome-protected, and therefore, actively translating transcripts. This will be of huge importance in characterizing and understanding the translatome of neuronal compartments. Thus, current technology now offers the exciting possibility of being able to discover differences in the Cediranib (AZD2171) dendritic or axonal translatome of diseased (e.g., autosomal models) individuals. How does the spatial morphology of the dendrite, axon, or spine contribute to or constrain protein synthesis? It was recently shown that spines enhance the cooperative interaction among multiple inputs (Harnett et al., 2012). These observations suggest that the amplifying and coordinating properties of dendritic spines have an effect on neuronal input processing and may influence information storage by promoting the induction of clustered forms of synaptic and dendritic plasticity among coactive spines.

It has long been recognized that a simple theoretical solution to this instability problem is to endow neurons with homeostatic plasticity mechanisms that keep neuronal firing HTS assay rates within a set point range (Miller and MacKay, 1994), but whether neuronal firing in the intact CNS is homeostatically regulated remains a critical and untested prediction of the neuronal homeostasis hypothesis. Here we used a monocular visual deprivation (MD) paradigm to ask whether neurons within primary visual cortex (V1) homeostatically regulate their firing rates back to a set point value during a prolonged sensory perturbation. Visual deprivation paradigms followed by ex vivo

measurements in V1 have identified several forms of Hebbian and homeostatic plasticity that are expressed in a layer- and cell-type-specific manner

and are activated with distinct temporal profiles (Kirkwood et al., 1996, Rittenhouse et al., 1999, Desai et al., 2002, Maffei et al., LY2835219 cost 2006, Maffei et al., 2010, Maffei and Turrigiano, 2008, Kaneko et al., 2008 and Lambo and Turrigiano, 2013). Because of this complexity, the net effect of visual deprivation on activity within V1 is difficult to predict based on ex vivo measurements alone. Attempts to measure activity homeostasis in the intact visual cortex have not so far been definitive; in vivo calcium or intrinsic signal imaging in anesthetized animals revealed that MD first reduced and then increased visual drive (Mrsic-Flogel et al., 2007 and Kaneko et al., 2008), but average visual drive was not well conserved during this process (Mrsic-Flogel et al., 2007). Visually driven activity in anesthetized animals may not be the best probe for firing rate homeostasis for a number of reasons; most critically, because

homeostatic plasticity operates over a timescale of many hours (Turrigiano, 2008), it presumably normalizes some metric of average activity that will include both visually driven and spontaneous (or internally driven) spikes. We therefore set out to chronically monitor firing in V1 of freely viewing almost and behaving rodents over many days so that we could sample all spikes regardless of origin and directly determine whether average V1 firing rates are restored to baseline during MD. We used a classic MD paradigm (lid suture) to perturb visual drive in juvenile rats during a developmental period (postnatal days 27–32 [P27–P32]), when this perturbation is known to induce both Hebbian and homeostatic forms of plasticity within V1 (Smith et al., 2009, Turrigiano, 2011 and Levelt and Hübener, 2012). We obtained chronic multielectrode recordings as described (Jones et al., 2007, Sadacca et al., 2012 and Piette et al., 2012) from both hemispheres of monocular V1 in freely behaving animals, recorded several hours of activity during the same circadian period each day for 9 days, and separated units into putative PV+ fast-spiking basket cells (pFS) or regular-spiking units (RSUs, ∼90% pyramidal).

, 2009). This suggests a more flexible learning mechanism often referred

to as model-based reinforcement learning than a simple, model-free reinforcement learning (Sutton and Barto, 1998, Daw et al., 2005, Daw et al., 2011, Pan et al., 2008 and Gläscher et al., 2010). In the present study, we found that the proportion of the neurons encoding the signals related to actual and hypothetical outcomes was similar for DLPFC and OFC. For actual outcomes, this was true, regardless of whether the signals differentially modulated by the outcomes from specific actions were considered separately or not. By contrast, for hypothetical outcomes, DLPFC neurons were more likely to encode the hypothetical outcomes related to specific actions. The effect size of the signals Veliparib related to both actual and hypothetical

Gamma-secretase inhibitor outcomes were larger in the OFC than in the DLPFC, suggesting that OFC might play a more important in monitoring both actual and hypothetical outcomes. Nevertheless, the difference between these two areas was less pronounced when the activity modulated differentially by the outcomes from different actions was considered separately. In particular, the effect size of the signals related to the hypothetical outcomes from specific choices was not different for the two areas. Thus, the contribution of DLPFC in encoding actual and hypothetical outcomes tends to focus on outcomes from specific choices. The bias for DLPFC to encode hypothetical outcomes from specific actions is consistent

with the previous findings that DLPFC neurons are more likely to encode the animal’s actions than much OFC neurons. This was true regardless of whether the chosen action was determined by the external stimuli (Tremblay and Schultz, 1999 and Ichihara-Takeda and Funahashi, 2008) or freely by the animal (Wallis and Miller, 2003, Padoa-Schioppa and Assad, 2006 and Seo et al., 2007). In addition, DLPFC neurons often encode the specific conjunction of the animal’s actions and their outcomes (Barraclough et al., 2004 and Seo and Lee, 2009). Nevertheless, the interplay between DLPFC and OFC is likely to contribute to multiple aspects of decision making. For example, neurons in the OFC tend to encode the information about the animal’s action and expected outcomes during the time of feedback, and might play an important role in updating the values of different actions (Tsujimoto et al., 2009 and Sul et al., 2010). The results from the present study suggest that signals related to the actual and hypothetical outcomes might be combined with those related to the animal’s actions, not only in DLPFC but also in OFC.

, 2001, Jones et al., 2002, Seriès et al., 2003, Ozeki et al., 2009 and Adesnik et al., 2012), and this suppression is more pronounced when using natural surround images than when using their phase-scrambled versions Metformin devoid of complex structure (Guo et al., 2005). Visual circuits are thus particularly sensitive to integrating salient image components across natural scenes, which may contribute to contour integration and “pop-out” phenomena at the perceptual level (Knierim and van Essen, 1992). Concomitantly, surround modulation by natural images alters the firing

rate distribution of individual neurons, whereby their responses become more selective and sparse (Vinje and Gallant, 2000, Vinje and Gallant, 2002 and Haider et al., 2010). Sparse codes are considered efficient, because they are able to transfer more information with fewer spikes (Olshausen and Field, 2004). Taken together, surround modulation contributes to contextual processing of sensory information and increases

the efficiency of neural representations for natural scenes (Sachdev et al., 2012). How do neural circuits Sunitinib become specialized to integrate and efficiently represent information from complex natural scenes, which contain image features that extend beyond the RF of any individual neuron? Neurons in V1 are feature selective already at eye opening (Hubel and Wiesel, 1963, Blakemore and Van Sluyters, 1975, Chapman and Stryker, 1993, Krug et al., 2001, White et al., 2001, Rochefort et al., 2011 and Ko et al., 2013). However, little is known about the development of surround modulation and its dependence on early sensory experience, and how this impacts the ability to encode complex natural scenes. Surround

modulation is mediated by excitatory and inhibitory interactions at different stages of the mature visual pathway, including the retina (Olveczky et al., 2003 and Solomon et al., 2006) and visual cortex (Stettler et al., 2002, Angelucci and many Bressloff, 2006, Girardin and Martin, 2009, Ozeki et al., 2009, Haider et al., 2010, Adesnik et al., 2012, Nienborg et al., 2013 and Vaiceliunaite et al., 2013). Since both excitatory and inhibitory circuits refine after eye opening (Frégnac and Imbert, 1984, Katagiri et al., 2007, Kuhlman et al., 2011 and Ko et al., 2013) and are susceptible to changes in visual experience (Ruthazer and Stryker, 1996, Zufferey et al., 1999, White et al., 2001, Chattopadhyaya et al., 2004 and Maffei et al., 2004), the effectiveness of surround modulation may be expected to change during postnatal development. The extent to which this may improve the processing of full field natural scenes is, however, unknown. In this study, we show that circuits mediating surround modulation require sensory experience to become preferentially sensitive to natural stimulus statistics across the RF and its surround.