Such models allow us to predict the spiking activity of each site in the

polytrode as a function of the previous spiking activity at all other sites. We fit models of the form: equation(Equation 4) xˆi(t)=x¯i+∑j=1N∑τ=1Tβi(τ,j)xj(t−τ)where xˆi(t) is the estimated response at recording site i   at time t  , x¯i is the baseline firing rate of that site, βiβi is a matrix of linear weights for the N   simultaneously recorded sites over each of T   time delays, and xj(t−τ)xj(t−τ) is the response at recording site j   at a given time in the past, (t−τ)(t−τ). T is the total number of time delays included in the analysis, and N is the total number of simultaneously recorded sites. We used delays up to 40 ms for each set of 14 simultaneously recorded sites. Those familiar with spectrotemporal receptive field (STRF) estimation will recognize this model as being essentially identical to a XAV-939 solubility dmso STRF ( Aertsen and Johannesma, EGFR inhibitor 1981, Theunissen et al., 2001 and Wu et al., 2006), with the difference being that neural activity is predicted

from other activity in the network rather than by a parameterization of the external stimulus. To solve for the VAR weights, we used ridge regression, which is less prone to overfitting than ordinary least-squares. Ridge regression, also known as L2-penalized or Tikhonov regularization, minimizes the mean squared error between the actual and estimated response while constraining the L2 norm of the regression weights. The strength of the L2 penalty is determined by the ridge

parameter, λ≥0λ≥0, where larger values of λλ result in greater shrinkage of the weights (Asari and Zador, 2009, Machens et al., 2002 and Wu et al., 2006). In ridge regression, we minimize the following error function: equation(Equation 5) E(βi)=‖xi(t)−xˆi(t)‖22+λ‖βi‖22where xi(t)xi(t) is the true response of site i   at time t   and the estimated response xˆ(t) is given by Equation 4. We estimated VAR weights using 80% of the data as a training MTMR9 set. Of the remaining 20% of the data, half was used for fitting the ridge parameter (10%) and half was used as a validation set to assess model performance (10%). The same recordings used in the Ising model were used in these analyses. Input to the model consisted of the binary spike trains binned at 2 ms for each of the channels on the polytrode. Separate models were fit for “light-on” and “light-off” trials. To find the optimal ridge parameter, we tested ten logarithmically spaced ridge parameters between 10−2 and 105 and then selected the value that resulted in the highest average correlation on the (ridge) test set across all sites on the polytrode and both “light-on” and “light-off” models. The same ridge parameter was used for both “light-on” and “light-off” models.

We wished to determine the extent to which genes required for initial regrowth were involved in growth cone formation. By 6 hr postaxotomy, between 40% and 60% of wild-type PLM axon stumps form growth cones; on average, axons with growth cones at 6 hr extend further than those without growth cones (Figure S1C), suggesting growth cones reflect growth rather than stalling. Among 50 mutants tested at 6 hr the fraction of growth cones positively correlated with regrowth (R2 = 0.11, p = 0.01, Figure S1D), suggesting many genes required for regrowth affect growth cone FG-4592 order formation. At 24 hr, the fraction of growth cones did not correlate with regrowth (data not shown), possibly reflecting

a more stochastic presence of growth cones in axon extension. However, mutants displaying increased regrowth at 6 hr (e.g., efa-6) did not display a higher fraction of growth cones than wild-type ( Figure S1D), suggesting the wild-type level of growth cone formation is a phenotypic ceiling. We conclude that growth cones correlate with early regrowth but

not with overall regrowth at later time points. We found genes affecting PLM regrowth among all structural and functional classes tested (Figures 1B and 1C and Table 1, Table 2, Table 3 and Table 4). When analyzed as nine gene classes (Figure 1C), genes promoting regrowth (i.e., those displaying reduced growth in loss-of-function mutants) were more frequent in the “cytoskeleton and motors” and “neurotransmission” classes. Genes inhibiting regrowth (i.e., increased regrowth in loss-of-function mutants) were concentrated Selleckchem Crenolanib in the “cell adhesion/extracellular matrix” class (Figure 1C). Here, for reasons of space limitations, the we describe our findings on selected genes among channels and transporters, neurotransmitters, and gene expression. Neuronal excitability can promote regrowth (Brushart et al., 2002), but can also act as an intrinsic negative signal via L-type voltage gated calcium channels (Enes et al., 2010). In C. elegans, neuronal excitability is generally influenced by the opposing action of

voltage-gated calcium and potassium channels ( Goodman et al., 1998); the voltage-gated Ca2+ channel EGL-19 is required for regrowth of PLM neurons ( Ghosh-Roy et al., 2010). We tested 53 additional channels and associated proteins ( Figure S2A) and found a cluster of genes affecting both Ca2+ and Na+ ionic balance to be critical for regrowth, including the Ca2+ channel regulator UNC-80 ( Jospin et al., 2007), the Na+ pump NKB-1 ( Doi and Iwasaki, 2008), the stomatins UNC-1 and UNC-24 ( Sedensky et al., 2004), and the Deg/ENaC Na+ channel UNC-8 ( Tavernarakis et al., 1997). Among these genes, UNC-24 and UNC-1 interact with UNC-8 and with the mechanosensory channel complex, suggesting electrical activity regulated by mechanosensory channels could promote regrowth ( Bounoutas and Chalfie, 2007).

, 1997). If so, this would comprise a cogent example of glial “coselection.” It could be argued that there is strong selective pressure to keep their functions and cellular properties homogeneous, at least in terms of myelination. The issue of oligodendrocyte molecular and functional diversity remains an active area of debate in the field. Further elucidation of the precise

developmental pathways involved might resolve these issues. For example, several studies have indicated that production of OPCs occurs in several temporal-spatial waves, with the general trend of early production of OPCs in the ventral regions of the brain and spinal cord being Sonic hedgehog regulated and later waves of production being from the more dorsal regions

of spinal cord and brain (Rowitch and Kriegstein, 2010). It is possible that temporally distinct OPCs carry forward selleck kinase inhibitor different properties that could be evaluated in terms of migration, myelination potential, and ability to function in repair after injury (Young et al., 2013). As discussed below, an enhanced understanding of precise functions of OPCs and oligodendrocytes during development and disease will equip us to look afresh at the issue of diversity. In the following sections, we look forward to new areas of research in glial cell Ergoloid biology. We propose that moving forward most efficiently will require defining the genetics of conserved mechanisms of glial function and developmental biology in the most experimentally Panobinostat in vitro accessible systems—worm, fly, and vertebrate systems including zebrafish and mouse. At the same time, we must explore how glial functions have diversified beyond basic functions in more sophisticated mammalian brains. Such an approach should lead to the production of new tools for investigating broad aspects of glial cell development and function and lead

to a better understanding of the roles for glia in a variety of human neurological disorders. Future advances will rely heavily on the generation of new tools to study glial development. Invertebrate model organisms must be more heavily exploited to maximize progress in the field. Such preparations have been workhorses in pushing forward our understanding of the cell biology of the neuron, and their seminal contributions include defining the electrochemical basis of the axon potential, genetic characterization of mechanisms of neuronal cell fate specification, neural stem cell asymmetric cell division, specification of neuronal temporal identity, and axon guidance (this is by no means a complete list). Neuronal development and function is remarkably similar in worms, flies, mice, and humans.

The representations

did not vary when the analysis was restricted to path segments in different areas of the arena (i.e., along each of the four walls, or in the west half versus east half of the arena; not shown) and were stable from one session to the next (Figure S5). Self-motion rate maps for just under half the cells in PPC were more coherent (42 of 98 cells [43%]; Z = 41.6, p < 0.001) and more stable Alectinib (47%; Z = 45.7, p < 0.001; Figure 3B) than the 99th percentile of the distribution of shuffled data. To quantify how sharply cells were tuned to different movement types we measured firing field dispersion by calculating the mean distance (in centimeters) between the 10% of pixels in the rate map that had the highest firing rates. Cell “PPC 1” in Figure 2, for example, had a low mean dispersion since pixels with the highest firing rates were condensed around one location (in this case corresponding to forward motion to the right). Forty-two of 98 cells in PPC (i.e., 43%) showed less firing field dispersion than the lowest percentile of the shuffled distribution (Z = 40.6, p < 0.001; Figure 3B). This fraction was significantly larger than for grid cells (15.1% in MEC versus 43% in PPC, Z = 3.46, p < 0.001; Figure 3B). In addition, significantly more PPC cells had rate maps that exceeded BMN 673 datasheet the 99th percentile of the shuffled distribution for coherence

(Z = 3.46, p < 0.001) and stability (Z = 4.4, p < 0.001). As a whole, the PPC cell population had self-motion rate maps with less firing field dispersion (D = 0.33, p = 0.001; Kolmogorov-Smirnov test), greater and coherence (D = 0.35, p < 0.001), and greater stability (D = 0.40, p < 0.001) than grid cells in MEC ( Figure 3B). Many PPC cells were also tuned to particular acceleration states (Figure 2, column 4) that often mirrored the cells' self-motion preferences. Thirty percent of the PPC cells expressed firing fields with less dispersion than the lowest percentile of the distribution of shuffled data (Z = 28.4, p < 0.001). Thirty percent

also expressed rate maps that were more coherent, and 34% had maps that were more stable than the 99th percentile of the distribution of shuffled data (Z = 28.4, p < 0.001 for coherence; Z = 32.5, p < 0.001 for stability). The degree to which individual PPC cells were tuned to acceleration and self-motion was strongly correlated (r = 0.60, p < 0.001 for firing field dispersion; r = 0.70, p < 0.001 for coherence; r = 0.74, p < 0.001 for stability). A large majority of cells that expressed tuning to acceleration (85%–90%) also showed tuning for self-motion. Compared to PPC, the proportion of grid cells in MEC showing acceleration tuning beyond chance levels was substantially smaller (Z = 3.43, p < 0.001 for rate map coherence; Z = 3.86, p < 0.001 for stability; Z = 3.43, p < 0.001 for firing field dispersion). The distributions of values for coherence (D = 0.33, p = 0.001; K-S test) and stability (D = 0.40, p < 0.

3, p > 0.5; βG: F2,40 = 0.2, learn more p > 0.5; RG: F2,40 = 0.4, p > 0.5). In the loss condition, we found a significant group effect for the reinforcement magnitude (RL: F2,40 = 3.2; p < 0.05), but not for the learning rate (αL: F2,40 = 0.0, p > 0.5) or choice randomness (βL: F2,40 = 0.6, p > 0.5). Post hoc comparisons using two-sample t tests found that, in the INS group, the RL was significantly reduced compared to CON (t32 = 2.3, p < 0.05) and LES (t21 = 2.3, p < 0.05) groups. Regarding HD patients, the same ANOVA revealed no significant group effect for any parameter estimate

in the gain condition (αG: F2,42 = 0.1, p > 0.5; βG: F2,42 = 0.1, p > 0.5; RG: F2,42 = 1.8, p > 0.1). In the loss condition, the only significant effect was found for choice randomness (βL: F2,42 = 4.2; p < 0.05), not for learning rate (αL: F2,42 = 0.6; p > 0.5) or reinforcement magnitude (RL: F2,42 = 1.4; p > 0.1). Post hoc t tests showed that, relative to the CON group, βL was significantly higher in both PRE and SYM groups, (t26 = 1.8, p < 0.05 and t26 = 2.7, p < 0.01). In the gain condition, the only significant difference was a higher RG in the PRE compared to the SYM group (t29 = 1.7, p < 0.05). In summary, the computational analysis indicated that the observed punishment-based learning deficit was specifically captured by a lower reinforcement magnitude (RL) parameter in the INS www.selleckchem.com/products/PD-0332991.html group

and by a higher choice randomness (βL) parameter in the PRE group. In order to statistically assess that the affected parameter depended on the site of brain damage, we ran an ANOVA with group (INS and PRE) as a between-subject factor and effect (reduction in RL and 1/βL relative to controls) as a within-subject factor. Crucially, we found a significant group by effect interaction (F1,26 = oxyclozanide 4.4, p < 0.05), supporting the idea that different computational parameters were affected in the INS and PRE groups. Here we tested the performance of brain-damaged patients with an instrumental learning task that involves both learning option values and choosing the best option. Behavioral results indicate

that both damage to the AI and degeneration of the DS specifically impair punishment avoidance, leaving reward obtainment unaffected. Computational analyses further suggest that AI damage affects the learning process (updating punishment values), whereas DS damage affects the choice process (avoiding the worst option). The instrumental learning task used to demonstrate this dissociation has several advantages. A first advantage is that money offers comparable counterparts for reward and punishments, contrary to the reinforcements used in animal conditioning, such as fruit juice and air puff (Ravel et al., 2003; Joshua et al., 2008; Morrison and Salzman, 2009). However, the well-known phenomenon of loss aversion (Tversky and Kahneman, 1992; Tom et al., 2007) suggests that financial punishment may have more impact than financial reward of the same amount.

5 mM glutamine. All experiments

examining colocalization in fixed neurons, as well as all experiments with clathrin:GFP, were performed in DIV15–DIV18 neurons. All transport assays were performed in DIV6–DIV8 neurons, as thicker dendrites in older neurons precluded reliable imaging. All animal studies were carried out in accordance with University of California guidelines. All assays were performed 4–6 hr posttransfection, on low expressers, except when noted in the figures. For each group, we analyzed ∼100–200 vesicles from 10–15 dendrites, and each experiment was repeated in at least two to three separate sets of cultures. Just before imaging, neurons were transferred to Hibernate-E-based “live imaging” at 35°C–37°C (Roy et al., 2012). Distal region of the primary dendrite or the secondary find more dendrites (first-order branch) were selected for imaging. All time-lapse movies were acquired using an Olympus IX81 inverted epifluorescence microscope with a Z-controller (IX81, Olympus), a motorized X-Y stage controller (Prior Scientific), and a fast electronic shutter (SmartShutter). Images were acquired using an ultrafast light source (Exfo exacte) and high-performance charge-coupled device cameras (Coolsnap HQ2, Photometrics).

http://www.selleckchem.com/products/ch5424802.html Image acquisitions were performed using MetaMorph software (Molecular Device). Simultaneous imaging of two spectrally distinct fluorophores was performed using a “dual cam” imaging system (Photometrics), a device that splits the emission wavelengths into separate (red/green) channels. Fluorescence intensity was attenuated to 50% to minimize photobleaching. Imaging parameters were set at 1 frame/s, total 200 frames and 200–400 ms exposure with 2 × 2 camera binning, totaling to ∼2,000 s of total imaging time for each group, suitable to capture the infrequent transport

events in dendrites. For transport analysis, kymographs were generated in MetaMorph, and segmental tracks were traced on the kymographs using a line tool and individual lines were out saved as “.rgn” files, and the resultant velocity data (distance/time) were obtained for each track as described in Tang et al. (2012). Frequencies of particle movements were calculated by dividing the number of individual particles moving in a given direction by the total number of analyzed particles in the kymograph. For cotransport assays, dual cam videos were separated using “split view” menu and kymographs were generated for each channel (red/green). Segmental tracks were traced as mentioned above and individual lines were compared manually for each pair of kymographs for one particular video. Vesicles (red/ green) were considered cotransported/colocalized if the traced lines merged when kymographs were overlaid. Neurons were cotransfected with desired constructs, and cells were fixed after 6–12 hr using 4% paraformaldehyde/4% sucrose.

Hence, the AIS is also an energetically favorable site for AP initiation. Furthermore, the small capacitance of the AIS favors rapid changes in membrane potential, as occurs during the upstroke of MK-8776 chemical structure the AP (dV/dt = I/C). Finally, it is worth noting that having a single site of AP generation

provides neurons a single locus where inhibition can gate AP initiation. One of the consequences of initiation of APs in the AIS, followed by backpropagation to the soma, is that from a somatic point of view the temporal relationship between synaptic input and AP initiation is distorted. As a result AP threshold is more depolarized at the soma than in the AIS (Kole and Stuart, 2008), and somatic AP threshold shows increased variability compared to that in the AIS (Yu et al., 2008). The geometry of the AIS BIBW2992 purchase (degree of taper and diameter), as well as the location, density, and properties of Na+ channel in the AIS, influences the capacity of APs

initiated in the AIS to propagate back to the soma (Hu et al., 2009, Mainen et al., 1995 and Moore et al., 1983). It might be expected, therefore, that the location of Na+ channels in AIS will influence somatic AP voltage threshold. Consistent with this, the location of Na+ channels in the AIS is thought to underlie differences in somatic AP threshold between hippocampal dentate granule and CA3 pyramidal neurons (Kress et al., 2010). The precise location and density of Na+ channels in the AIS can also influence the fidelity of AP PDK4 initiation. Initiation of APs further from the soma, taking advantage of the electrical isolation of this region, is a strategy used in some neurons to increase their capacity to discriminate the arrival time of different synaptic inputs. In neuronal pathways associated with hearing this helps determination

of interaural timing differences (ITD). In nuclueus laminaris (NL) neurons in birds the distance of the AIS from the soma, as well as its length, depends on the characteristic frequency of presynaptic inputs the neuron receives (Kuba et al., 2006 and Kuba and Ohmori, 2009). Na+ channels in the AIS are located more distally from the soma in neurons that have high characteristic frequencies (>2 kHz) compared to neurons tuned to low characteristic frequencies (≤1 kHz). Modeling indicates that the more distal location of the AIS in neurons that received inputs with high characteristic frequencies increases their capacity to detect ITDs. This occurred for two reasons. First, the passive cell body of NL neurons acts as a leak decreasing the membrane time constant and reducing the filtering of synaptic input frequencies (Ashida et al., 2007). Second, the distal position of Na+ channels in the AIS reduces steady-state inactivation, increasing the number of Na+ channels available for activation (Kuba et al., 2006).

This overlap is easily explained by the fact that Cav2 channels are physically and functionally tightly associated with exocytotic sites. Not surprisingly, the Cav2 proteome also contains many PSD proteins since in that study no separation of pre- and postsynaptic compartments was attempted. Considering the high purity of our docked synaptic vesicle fraction, with proteins from other organelles (except mitochondria) being virtually absent, the identification of 30 hitherto uncharacterized proteins suggests that many of them are indeed constituents of the presynaptic active zone and adjacent

areas. While further work will be needed to clarify which of them is involved in presynaptic function, we have used in silico-based analyses for a preliminary characterization (Table S4). Accordingly, 16 proteins possess one or more predicted transmembrane domains. Twenty-six proteins appear to be conserved buy NVP-BKM120 between vertebrates, among these 12 are also conserved in invertebrates. Noteworthy, 18 proteins appear to be well expressed

in the mammalian brain based on standardized in situ hybridization (Allen Mouse Brain Atlas, http://mouse.brain-map.org). Thus, we consider it highly probable that at least some of these proteins will turn out to be constituents of the presynaptic membrane and/or the vesicular release apparatus. We extended our study to investigate the differences between glutamatergic versus GABAergic docking complexes by a slight modification of our original protocol. We this website find that, except of the transmitter-specific transporters and enzymes, only very few proteins are selectively enriched in glutamatergic and GABAergic docking complexes. These results confirm and extend our previous observation that glutamatergic and Idoxuridine GABAergic synaptic vesicles have

a largely identical protein composition. Two major conclusions can be drawn from these findings. First, the release machineries of glutamatergic and GABAergic synapses are very similar if not identical. In particular, we did not detect any major difference between the expression levels of SNAP25 and SNAP23 in the two types of synapses, arguing against a specialization of these SNAREs for glutamatergic versus GABAergic release as suggested previously (Garbelli et al., 2008; Verderio et al., 2004). Obviously, the overall similarity between the populations does not exclude major variations in the composition of the docking and release apparatus between individual synapses. However, such variations do not appear to correlate with the neurotransmitter phenotype. Second, it is only the biosynthetic enzymes and the transmitter transporters (particularly the vesicular transporters) that define the neurotransmitter phenotype of glutamatergic and GABAergic synapses. Taken together, we have made significant progress toward the aim of establishing a “parts list” of the presynaptic docking and release machinery and of the presynaptic membrane.

This is probably because Obeticholic Acid at high rates of spiking, the fraction of time that the MC membrane potential is close to threshold (but

not firing) is small. Stimulating AON axons in vivo in the intact brain led to an increase in firing probability of MCs/TCs in a brief time window of a few milliseconds, as predicted by our in vitro studies. This remarkable effect was not anticipated by previous work, which has emphasized feedback innervation of GCs. Our slice experiments indicate that the excitation is particularly effective when MCs have moderate activity. It is intriguing that MCs are spontaneously active in vivo, particularly in awake animals (Rinberg et al., 2006). Feedback activation, therefore, could elicit precise synchronous spikes in a population of MCs, perhaps creating functional cell assemblies transiently. Synchronous activity in MCs, observed at different time scales (Kashiwadani et al.,

1999; Doucette et al., 2011), could carry information that is readily find more decoded by downstream circuits (Luna and Schoppa, 2008; Davison and Ehlers, 2011). A recent study noted that synchronous spikes in MCs may be context dependent (Doucette et al., 2011); this could involve top-down modulation from the AON, providing brief excitation. We did not find any evidence of rapid excitation triggered by AON activation during odor-evoked responses. There could be several reasons for this absence. First, even under the controlled conditions of slice experiments, we observed excitatory effects on spike activity in half the cells. Similarly, excitatory effects

Calpain on spontaneous activity in vivo were also observed in only half the cells. It is possible that, by chance, all the cells in which odor-evoked responses were obtained fell in the nonresponsive half. A second, more likely, reason could be that the higher firing rates during odor responses masked any excitatory responses triggered by AON stimulation. Indeed, AON stimulation in slices caused much weaker excitatory effects on MCs at higher firing rates. Excitatory effects were observed in vivo when cells were firing spontaneously (6.9 ± 1.6 Hz), but not during odor responses, when the firing rates averaged 21.5 ± 4.0 Hz. The excitatory effects in M/T cells caused by AON axon activity are followed by a strong inhibitory effect. This inhibition of spiking occurred soon after light stimulation, and lasted for a few hundred milliseconds. The time constant of recovery of firing was remarkably similar to the time constant of the slow component of inhibition recorded in vitro (189 versus 135 ms), suggesting that a brief synchronous activation of AON axons can suppress the output of the OB for a period that is governed by the time course of OB interneuron activity. AON neurons in vivo often respond in bursts of two to five spikes at 20–50 Hz locked to respiration, with maximal firing at the transition of inspiration-expiration (Lei et al., 2006; Kikuta et al.

, 2009). Importantly, whereas the levels of ERK1/2 activation in the pDMS did not differ between Sham and Ipsi groups, F (1, 21) = 0.414, p = 0.527, a significant increase of activated MSNs was observed in the group Contra, F (1, 21) = 4.565, p = 0.045 (Figure 5E). Moreover, we detected very few phospho-ERK1/2 neurons in the DLS (Figure 5F), in line with the more critical role of the pDMS relative to the DLS in the context of goal-directed action (Shiflett et al., 2010). These data suggest that the expected decrease of Pf glutamatergic input to the pDMS had a direct effect

on the activity of CINs but did not produce a similar effect on MSNs. Rather, it resulted in an increase in MSN activity, most likely due to the loss of the general inhibitory effect of CINs on striatal MSNs. The effect of Pf lesions on MSN activation reported here supports the recently described neuromodulatory nature of

these specific projections (Ellender et al., 2013) Selleckchem Crizotinib and points to the importance of the Pf-CIN synapses in controlling striatal processes (Ding et al., 2010; Threlfell et al., 2012). In a separate group of rats, we investigated whether the impairments we observed after Pf-pDMS disconnection were specific to the posterior DMS, or whether disconnection of the Pf from anterior DMS (aDMS) would produce a similar effect. It buy CP-868596 is well known that the Pf projects to both the aDMS and pDMS (Deschênes et al., 1996), and a previous study observed an increase in acetylcholine in aDMS as rats learned new stimulus-outcome associations in a place task (Brown et al., 2010). The Pf-aDMS pathway, however, appears not to

be required to learn new action-outcome contingencies; we found that rats with contralateral Pf and aDMS lesions showed intact initial learning (Figure S1) and, unlike the pDMS disconnection, also showed intact outcome devaluation (Figure 4G) and outcome-specific reinstatement (Figure S1) after the reversal of the action-outcome contingencies (Figure 4G; Figure S1). Statistical analysis showed that the lesion had no effect on reversal training (F < 1) and, on test, that there was an effect of devaluation (nondevalued > devalued), F (1, 13) = 8.69, p = 0.011, but no group × devaluation interaction, F < 1. The results of this experiment suggest, therefore, that the thalamostriatal pathway Tolmetin connecting the Pf and aDMS does not play a role in either initial learning or the acquisition of new goal-directed actions and confirm, therefore, that the findings following disconnection of the Pf-pDMS pathway on new learning are specific to that pathway. This is consistent with the argument that the Pf alters the functional role of cholinergic interneurons specifically in the pDMS to enable the encoding of new action-outcome associations. The observed effects of Pf lesion on CIN function in pDMS suggests that the observed behavioral effects of bilateral Pf and contralateral Pf-pDMS lesions are most likely regulated by alterations in CIN function in pDMS.