3–5,000 Hz) and unit activities (300–5,000 Hz). Raw ECoG signal was band-pass filtered (0.3–1,500 Hz) and amplified (2,000×). All signals were digitized online at 16.67 kHz using a Power 1401 analog-digital converter (Cambridge Electronic Design) and stored on a PC running Spike2 software (versions 6.08 and 6.09, Cambridge Electronic Design). GABAergic cell recordings lasted 15–105 min (typically ∼45 min). The juxtacellular recording mode (rather than, for example, a quasi-intracellular mode), was assured by only including for analysis neurons that (1) had

stable spontaneous firing rates/patterns and stable spike widths; (2) did not display any “injury discharge”; and (3) were recorded in the absence of spurious “baseline noise” or hyperpolarizing Ipatasertib mw shifts in the electrode potential. After recordings, neurons were selectively filled with Neurobiotin using Selleckchem MEK inhibitor juxtacellular labeling (Pinault, 1996).

Spike shape and amplitude were monitored throughout recording and labeling to ensure that the same neuron was recorded and labeled. In order to verify the location of the reference electrode, an extracellular Neurobiotin deposit was made in the dorsal CA1 (100 nA anodal current 1 s, 50% duty cycle for 20–30 min). Only data acquired before labeling and obtained from unequivocally identified cells were analyzed. All data were analyzed off-line using Spike2 built-in functions and custom scripts (Tukker et al., 2007). Spikes were detected with an amplitude threshold in the BLA unit channel. Occasionally, crotamiton additional smaller amplitude units were present in the recording. Spike2 clustering function supervised manually was used to isolate single units, and identity of labeled neurons was systematically ensured as described above. Spike sorting was always checked using autocorrelograms, which showed clear refractory periods (≥2 ms). Hippocampal theta oscillation epochs

were detected by calculating the theta (3–6 Hz) to delta (2–3 Hz) power ratio in 2 s windows of the dCA1 LFP (Csicsvari et al., 1999 and Klausberger et al., 2003). Ratio >4 in at least three consecutive windows marked theta episodes. We excluded from this analysis periods of noxious stimuli and the following 20 s. Every theta episode was visually checked. Selected periods always consisted of robust theta oscillations. They exclusively occurred during persistently activated brain state (Figure S9). After theta episodes detection, the dCA1 LFP was downsampled to 1.04 kHz, digitally filtered (3–6 Hz) and the troughs were determined (Spike2). Each spike was assigned an angle relative to surrounding theta troughs (Tukker et al., 2007 and Klausberger et al., 2003). The precision of our electrode placements (mediolateral and antero-posterior ranges ∼400 μm) ensured phase consistency between experiments (i.e., ∼8.5 degrees error, assuming a phase shift of 21°/mm; Lubenov and Siapas, 2009).

However, the neural system evolved along with the complex mechanical structures of the body; therefore, some of these computational mechanisms may even be encoded at lower levels such as in spinal circuitry (Bizzi et al., 2008). Although this review focuses primarily on the algorithmic part of sensorimotor control, we believe that the important open questions are where and how these computational algorithms

are implemented in the neural structures. This work was supported by the Wellcome Trust. “
“The acquisition and long-term retention of motor skills play a fundamental role in our daily lives. Skills such as writing, playing golf, or riding a bicycle are all acquired through repetitive practice. Motor skill learning refers to the process by which

movements are executed Fulvestrant more quickly and accurately with practice (Willingham, 1998). Our understanding of the neural substrates underlying the acquisition and retention of motor skills has been boosted in recent years, owing in a large part to technological and methodological advances in neuroimaging, EX527 as well as in noninvasive brain stimulation in humans, coupled with dramatic new insights emerging from animal studies both in vivo and in vitro, providing additional information about the recruitment of specific neuronal circuits during the various stages of motor skill learning. This work has overall demonstrated a strong link between see more acquisition of motor skills and neuronal plasticity at cortical and subcortical levels in the central nervous system that evolves over time and engages different spatially distributed interconnected brain regions. Here, we review novel findings reflecting functional and structural plasticity associated with the acquisition, consolidation, and long-term retention of motor skills in humans and experimental animals while identifying points of convergence and dispute.

A variety of tasks and experimental paradigms have been used for studying motor skill learning, including juggling, visuomotor tracking, and isometric force-production tasks, to name a few. Of particular relevance to the current review are studies of tasks that require practice of sequential movements: tapping skills like typing or playing various musical instruments. Here, our main focus is on learning sequential motor skills that show lasting improvements beyond baseline performance over lengthy periods of time. Another model for studying motor learning, which does not necessarily involve the acquisition of a new skill, has been adaptation to externally induced perturbations, such as those induced by a force field (dynamic adaptation) or by visuomotor rotations (visuomotor adaptation). These perturbations are more commonly introduced while subjects execute simple motor tasks, for instance, point-to-point ballistic reaching movements (Krakauer, 2009, Shadmehr et al., 2010, Seidler, 2010 and Lalazar and Vaadia, 2008).

Recordings were made at various holding potentials (Vh = −100–0mV) to generate synaptic current-voltage (I–V) curves for every cell (Figures 6A and 6B). A cesium-based

internal solution containing QX-314 was used to block potassium, sodium, and GABA-B-R conductances (Monier et al., 2008). Only recordings with an initial series resistance (Rs) this website lower than 40 MΩ (mean, 25 ± 8 MΩ [SD], n = 21) and a Rin/Rs ratio higher than 3 (mean, 7.1 ± 4 [SD], n = 21) were analyzed (Figures S4A and S4B). This allowed us to compare cells under various conditions (see Experimental Procedures). Under all conditions we found linear relationships between the integrated currents over a 5- to 40-ms-poststimulus period and the Rs-corrected holding potentials (Vcs) (R2, control PW: 0.96 ± 0.02 [SD], n = 14; control SW: 0.95 ± 0.03 [SD], n = 17; DWE PW: 0.95 ± 0.04, n = 11; DWE SW: 0.95 ± 0.05, n = 12) (Figure 6B). This learn more indicates that NMDAR conductances had not or only minimally contributed to the responses (Manookin et al., 2008; Monier et al., 2008). Based on the I–V regression slopes and the synaptic reversal potentials, we calculated

the inhibitory (Gi) and excitatory (Ge) conductances over time (Figures 6B–6F) (House et al., 2011; Monier et al., 2008). Inhibitory (Ei) and excitatory (Ee) reversal potentials were estimated to be −100 and 0mV, respectively. Calculation of Ei was based on an estimated extracellular chloride concentration ([Cl−]e) of 180 mM, which we verified pharmacologically in a subset of the recordings (Supplemental Experimental Procedures; Figures S4C–S4G). The similarity between the derived and calculated reversal potentials indicates that the voltage clamps were rather accurate and that the calculated Gi and Ge were not greatly affected by a limited space clamp (Supplemental Experimental Procedures). Integrated conductances over a 40 ms period were used as a measure of the total Ge and Gi (Figures Flavopiridol (Alvocidib) 6C–6F). Compared to

control conditions, DWE had not significantly changed PW-evoked Ge and Gi (Ge: control, 153 ± 30 nS.ms; DWE, 157 ± 32; p > 0.9; Gi: control, 137 ± 31 nS.ms; DWE, 122 ± 25 nS.ms; p > 0.9) (Figures 6C and 6E). However, whereas DWE had left the SW-evoked Ge largely unchanged, it had reduced the SW-evoked Gi by more than 50% (Ge: control, 79 ± 12 nS.ms; DWE, 57 ± 11 nS.ms; p = 0.2; Gi: control, 79 ± 11 nS.ms; DWE, 37 ± 8 nS.ms; p < 0.01) (Figures 6D and 6F). The notion that the SW- and not the PW-mediated Gi had decreased on the same neurons indicates that DWE had mostly influenced the SW-associated pathway and that these effects were very unlikely to be accounted for by space-clamp limitations (see Experimental Procedures).

, 2011 and Younger et al., 2013). This observation was extended by recent work using the ENaC channel blocker benzamil to acutely disrupt presynaptic homeostasis. Benzamil was applied to the NMJ of animals lacking the muscle-specific GluRIIA glutamate receptor subunit, a perturbation that is persistent throughout development and induces Tyrosine Kinase Inhibitor Library solubility dmso presynaptic homeostasis. Benzamil erased the expression of presynaptic homeostasis, leaving behind a synapse with unpotentiated wild-type release and wild-type anatomy ( Younger et al., 2013). Together, these data demonstrate that presynaptic homeostasis is uncoupled from the

mechanisms that achieve anatomical and physiological NMJ growth. One possibility is that presynaptic homeostasis is only induced developmentally when a cellular set point differs from ongoing activity. If the set point is developmentally programmed to change along with the maturation

of cell fate, then a developmental change in cellular function could occur without the induction of homeostatic plasticity. Veliparib clinical trial In the mammalian CNS, homeostatic and developmental plasticity coexist. This is nicely documented in a binocular region of visual cortex after monocular deprivation (Mrsic-Flogel et al., 2007). When cells receive input predominantly from an open eye, deprived eye input is diminished, consistent with classical synaptic competition. However, when cells receive input predominantly from the deprived eye, these inputs are strengthened, consistent with homeostatic plasticity. Binocular deprivation also induces Phenibut homeostatic synaptic strengthening. Although these processes coexist, it remains unclear whether homeostatic plasticity normally participates in ocular dominance independent of an experimental perturbation such as eye suturing. In other examples, cell-autonomous suppression of neural

activity has been shown to induce changes in synaptic connectivity as well as homeostatic plasticity, but the effects are separated in time (Burrone et al., 2002). There are emerging molecular links between homeostatic plasticity and neurological disease. The schizophrenia-associated gene dysbindin was isolated in a forward genetic screen for mutations that block presynaptic homeostasis ( Dickman and Davis, 2009). Homer and mGluR signaling are implicated in mouse models of fragile X syndrome ( Ronesi et al., 2012), as is retinoic acid ( Soden and Chen, 2010). Others have speculated the involvement of disrupted homeostatic signaling in posttraumatic epilepsy ( Houweling et al., 2005), Rett syndrome ( Ramocki and Zoghbi, 2008 and Qiu et al., 2012), and autism spectrum disorders ( Bourgeron, 2009). A wealth of information is emerging regarding rare de novo mutations with strong effects in autism spectrum disorders and it is possible that further associations with homeostatic plasticity will emerge ( Murdoch and State, 2013).

In control slices preexposed to vehicle (0.2% DMSO), perfusion of SKF 81297 significantly enhanced AP generation as expected (Figure 5A, top). Interestingly, preexposure of slices to dynasore strongly inhibited this response (Figure 5A, bottom). This inhibitory effect of dynasore on D1 receptor-mediated AP firing was robust across experiments. Importantly, dynasore did not affect basal firing (Figure 5B). The time course of increased AP firing observed in selleck products vehicle-perfused slices is consistent with that of D1 receptor-mediated signaling in this preparation and, after accounting for perfusion

lag time, closely paralleled that of acute cAMP signaling measured in dissociated MSNs. We further verified that dynasore did not alter the basic firing properties of MSNs in this preparation (Figure S5) using a previously established method (Hopf et al., 2010). We next investigated the mechanism by which endocytosis promotes acute D1 receptor-mediated signaling. One possibility is that endocytosis-dependent augmentation of cAMP accumulation might require subsequent receptor recycling. This would be predicted if endocytosis mediates a function in D1 receptor signaling akin to resensitization of other GPCRs. We imaged SpH-D1R insertion events with high temporal resolution MK-2206 using TIRF microscopy and rapid dequenching of fluorescence upon exposure to the neutral extracellular milieu. Vesicular insertion events delivering

SpH-tagged receptors appear as “puffs” of transiently increased surface fluorescence intensity, detectable Diflunisal by rapid (10 Hz) serial imaging (Yudowski et al., 2006). Such insertions were observed immediately after DA washout (Figure 6A and Movie S3), even after prolonged exposure of cells to the protein synthesis inhibitor cycloheximide (data not shown). This indicates that D1 receptors can indeed undergo rapid surface delivery. Insertion events were also observed in the continued presence of agonist, but this required distinguishing insertion events (Figure 6B)

from the dimmer and longer-lasting receptor clusters representing clathrin-coated pits (Yu et al., 2010, Yudowski et al., 2006, Yudowski et al., 2007 and Yudowski et al., 2009). Integrated fluorescence intensity measurements (Figure 6C) and a conventional flow cytometric assay (Figure 6D) further verified recovery of the surface pool of receptors within several minutes after agonist washout. To specifically examine recycling of the internalized receptor pool, we analyzed surface recovery of FD1R initially labeled in the plasma membrane of MSNs using a previously described method (Tanowitz and von Zastrow, 2003). Figure 6E depicts the experimental schematic. Representative images of the conditions used to quantify D1 receptor recycling are shown in Figure 6F. Recycling determinations averaged across multiple neurons and experiments are shown in Figure 6G. The majority (89.4 ± 1.

, 2004 and Marder, 2011). Experimental evidence for this type of variation includes the demonstration Roxadustat datasheet that anatomically identical cells can have similar firing properties that are

driven by diverse combinations of underlying current densities and synaptic weights (Swensen and Bean, 2005, Schulz et al., 2006, Schulz et al., 2007, Andrásfalvy et al., 2008, Goaillard et al., 2009 and Temporal et al., 2012). Mechanistically, it is clear that altered ion channel transcription is involved in the homeostatic rebalancing of ion channel expression (Schulz et al., 2007 and Bergquist et al., 2010). Interesting data from the lobster stomatogastric system has shown that neuromodulators influence the transcription of ion channels in a coordinated fashion (Khorkova and Golowasch, 2007 and Temporal et al., 2012). These data not only highlight the importance of neuromodulation but provide insight into how the homeostatic rebalancing SCR7 solubility dmso of ion channel expression might be constrained. Another idea is that ion channel translation could also be a key modulatory step, downstream of the terminal selector. For example, a homeostatic change in sodium channel expression after chronic manipulation of synaptic activity requires

the translational regulator pumillio, a mechanism that is conserved in both flies and mice ( Driscoll et al., 2013). Finally, it is also well established that extrinsic factors can influence cell phenotype, one example being neurotransmitter switching ( Dulcis et al., 2013). It remains possible that ion channel rebalancing reflects a similar phenotypic switch, albeit more complex. Ultimately, even though we are gaining information about how a cell rebalances ion channel expression, a clear model for how the genome KLK8 defines a cell-type-specific set point for neural activity remains elusive. How cells detect a change in neural activity to initiate homeostatic

plasticity remains unknown. Homeostatic signaling can be induced cell autonomously (Goold and Nicoll, 2010 and Burrone et al., 2002) and through focal application of TTX to the soma (Ibata et al., 2008). These data are consistent with a somatic sensor of cell-wide activity. As expected, calcium-dependent signaling is essential. For example, both synaptic upscaling and downscaling have been shown to require the activity of CamKK and CamKIV (Goold and Nicoll, 2010 and Ibata et al., 2008). But the link between altered activity and the induction of a homeostatic response still remains unclear. Many experiments utilize dramatic activity alterations, either blocking activity with TTX or inducing seizure-like network activity, which will invoke changes in calcium-dependent signaling and transcription. However, there are examples in which moderate and graded changes in neural activity and muscle depolarization are detected.

, 2009; check details Gupta et al., 2010). Rather, reactivation during SWRs seems best suited to provide downstream areas with information about possible paths through the environment. In particular, coding of paths extending from the current to remote locations, similar to what we observed during SWR reactivation, is an efficient and rapid way to represent possible options to reach a goal (Johnson and Redish, 2007; Carr et al., 2011). Reactivation during SWRs has also been linked to the consolidation of memories (Girardeau et al., 2009; Nakashiba

et al., 2009; Dupret et al., 2010; Ego-Stengel and Wilson, 2010), suggesting that reactivation could contribute simultaneously to memory retrieval and to the storage of the retrieved memories. Previous results have established that SWR reactivation is strongest in novel environments and becomes less prevalent as the environments become more familiar. (Foster and Wilson, 2006; Cheng and Frank, 2008; Karlsson and Frank, 2008; Fulvestrant O’Neill et al., 2008). Additionally, we have shown that receipt of reward also enhances reactivation and that reward-related reactivation is strongest when animals are learning (Singer and Frank, 2009). Here we controlled for immediate reward history by examining outbound trials that always followed a rewarded inbound trajectory. We found that SWR reactivation reflects

both novelty and trial-by-trial variability related to the upcoming decision on that trial. Coactivation probability during SWRs preceding correct trials was high when the environments were novel and the animals performed poorly. Coactivity probability remained high as animals learned the

task and only dropped once animals reached >85% asymptotic performance. In contrast, while coactivation probability preceding incorrect trials was also high when the track was novel and animals performed poorly, this coactivation probability dropped once animals achieved >65% NET1 correct performance and remained lower on these trials throughout the remainder of the training. Taken together, these findings link the strength of SWR reactivation to the engagement of hippocampal circuits in learning and decision-making processes. Thus, strong reactivation in novel environments probably reflects a consistently high level of hippocampal engagement related to ongoing learning about the environment. Similarly, strong reactivation before or after individual trials probably reflects shorter timescale periods of engagement related to receipt of reward, task learning, and decision making. Rapid learning of the W-track alternation task requires an intact hippocampus, but animals with hippocampal lesions eventually learn the task (Kim and Frank, 2009). Similarly, SWR disruption impairs learning on this task (Jadhav et al., 2012), but animals can still learn to perform at above chance levels. Similarly, we find SWR reactivation is increased preceding correct trials only during early learning.

, 1997; Izumi and Zorumski, 2009). In brain slices treated with 4-CIN (100 μM), application of 10 mM [K+]ext significantly increased extracellular lactate (81.0 ± 6.4 μM, n = 5; + 4-CIN: 121.7 ± 7.0 μM, n = 7 p < 0.001; Figure 5A), suggesting that when neuronal lactate uptake is inhibited by 4-CIN, more lactate is free to diffuse out of the brain slice into the superfusate. Previous studies have demonstrated that when extracellular glucose levels are reduced, lactate this website is produced by astrocytes and provided to neurons to promote neuronal viability (Aubert et al., 2005; Izumi et al., 1997; Wender et al., 2000). Furthermore, aglycemia is associated with alkalinization (Bengtsson et al., 1990; Brown et al., 2001), which could subsequently

activate sAC. Therefore, we tested the hypothesis PF-06463922 mw that aglycemia recruits sAC to initiate the astrocyte-neuron lactate shuttle. We first examined whether aglycemic condition induced astrocyte alkalinization. We used two-photon laser scanning microscopy to image the pH-sensitive dye BCECF/AM to monitor astrocytic intracellular pH change in aglycemic condition. We found that applying aglycemic solution induced a gradual alkalinization of intracellular pH in astrocytes (Figure S7). Next, we examined the

effect of aglycemia in brain slices on the production of cAMP. We detected increased cAMP in slices when exposed to aglycemic aCSF (control; 10 mM glucose: 4.4 ± 0.4 pmol/ml, n = 6; 0 glucose: 6.2 ± 0.3 pmol/ml, n = 7, p < 0.01; Figure 5B) and this increase was significantly inhibited by 2-OH (5.3 ± 0.1 pmol/ml, n = 7, p < 0.05; Figure 5B) and DIDS (4.3% ± 0.3%, n = 7, p < 0.01; Figure 5B), indicating bicarbonate-sensitive sAC is activated by glucose-free condition. We further tested the hypothesis that sAC was responsible for coupling aglycemia to glycogen breakdown in astrocytes

and for the production and release of lactate. Depleting extracellular glucose for 30 min significantly reduced glycogen levels in brain slices (control: 100%, n = 11; 0 glucose: 43.0% ± 6.6%, n = 12, p < 0.01; Figure 5C). This effect was prevented by sAC inhibition with KH7 (85.5% ± 9.4%, n = 7, p < 0.01; Figure 5C) and NBC antagonist DIDS (93.1% ± 11.8%, n = 7, p < 0.01; Figure 5C). Treating with 4-CIN enough in the absence of glucose significantly increased extracellular lactate (98.5 ± 3.6 μM, n = 4) compared to glucose deprivation alone (56.5 ± 5.7 μM, n = 4, p < 0.001; Figure 5D), an effect that was partially inhibited by 2-OH (79.0 ± 2.6 μM, n = 6, p < 0.001; Figure 5D) or oxamate (76.3 ± 2.9 μM, n = 3, p < 0.001; Figure 5D), suggesting sAC and LDH involvement, respectively. To further explore whether this sAC-dependent lactate shuttle has functional consequences to the maintenance of neuronal activity when the supply of glucose is compromised, we recorded field excitatory postsynaptic potentials (fEPSPs) in the stratum radiatum of the CA1 region during aglycemia in the presence or absence of 2-OH.

Picrotoxin (50 μM) did not significantly alter the baseline firing rate of DA neurons between the nicotine and saline pretreatments. However, in the presence of picrotoxin (50 μM), ethanol no longer inhibited DA neuron firing after nicotine pretreatment (red circles compared to dotted line, Figure 4E). In the presence of picrotoxin, nicotine and saline pretreatment

groups showed a similar increase in firing rate in response to ethanol (group × time: F(9,144) = 0.30, p > 0.05). These results, combined with the increase in spontaneous and evoked GABA IPSCs, indicate that nicotine pretreatment increased ethanol-induced GABA transmission onto DA neurons, thereby reducing DA neuron excitability. To examine whether the effect of nicotine was selective to the actions of ethanol, we recorded from DA neurons and measured sIPSCs induced by other drugs of abuse after nicotine or saline pretreatment. Bath-applied nicotine (1 μM) increased the sIPSC frequency this website similarly in both treatment groups (saline pretreatment, 156% ± 24% of basal; nicotine pretreatment, 155% ± 25% of basal; n = 5–6, p > 0.05), indicating no causative effect of the nicotine pretreatment. Because our data suggest adaptations in GABA transmission, we also tested diazepam, a benzodiazepine that positively modulates

GABAA receptors (Tan et al., 2010). Nicotine pretreatment increased the sIPSC frequency induced by diazepam by approximately 63% compared to the saline pretreatment response (n = 6, 7/group, Sermorelin (Geref) p < 0.01), indicating that nicotine pretreatment specifically altered the GABAergic responses to drugs such as ethanol and diazepam. The interaction Cilengitide concentration between nicotine and ethanol could potentially alter

the excitatory glutamatergic signals that regulate VTA DA neurons (Xiao et al., 2009). We tested this possibility by performing whole-cell patch-clamp recordings of sEPSCs before and after ethanol application to the bath. The basal sEPSC frequency was not different between the saline pretreatment control cells (1.8 ± 0.3 Hz) and the nicotine pretreatment cells (1.2 ± 0.2 Hz; n = 7, p > 0.05). Subsequent application of ethanol increased the sEPSC frequency to a similar degree in both groups (saline pretreatment control, 165% ± 7%; nicotine pretreatment, 169% ± 8%; n = 7, p > 0.05). To understand how nicotine and ethanol interact and impinge on the DA and GABA systems, we considered several possible mechanisms. A functional alteration in nAChRs by nicotine is not likely to contribute to the nicotine-ethanol interaction because the DA release (see Figure 2C) and the sIPSCs induced by nicotine were unaffected by nicotine pretreatment. Moreover, the recovery from desensitization is more rapid than 15 to 40 hr (Lester and Dani, 1995 and Wooltorton et al., 2003). Nicotine can enhance glutamatergic synaptic plasticity onto DA neurons (Gao et al., 2010, Mansvelder et al., 2002 and Saal et al.

Analysis of partial loss-of-Chinmo phenotypes has been particularly informative in the determination of the physiological significance of the graded Chinmo expression in the specification of multiple MB temporal cell fates ( Zhu et al., 2006). To delineate the underlying pathological changes, we determined whether and what temporal cell-fate transformation(s) had occurred in the absence of Chinmo through analysis of chinmo mutant GMC clones made at different temporal positions within the adPN lineage. Mutant adPNs born outside the Chinmo-required windows reliably innervated the correct glomerulus Buparlisib concentration and acquired the wild-type pattern of axon arbors ( Figure S2;

data not shown). For example, the one supposed to be the only DM3-targeting adPN that breaks the otherwise continuous stretch of Chinmo requirement into two blocks never deviated from its wild-type control (compare Figures 2C and 2H). By contrast, chronologically inappropriate morphologies, as evidenced in both dendritic PF-02341066 solubility dmso targeting and axonal arborization, were seen in various offspring that precede as well as follow the DM3 adPN ( Figure S2).

Interestingly, mutant GMCs made in the window encoding the DM4, DL5, and VM3(a) fates gave rise to DM3-like adPNs ( Figures 2F and 2G versus wild-type controls shown in Figures 2A–2C; VM3a fate transformation shown in Figure S2), whereas those derived in the period of making the VM3(b), DL4, DL1, DA3, and DC2 adPNs produced D-like adPNs which were followed by the normal D-targeting adPNs ( Figures 2I and 2J; others in Figure S2). The chinmo mutant adPN with the prospective fate of DM4, DL5, or VM3(a) might

exclusively innervate the DM3 glomerulus or occupy its prospective glomerular target as well as DM3 glomerulus, reflecting some hybrid temporal identity (e.g., Figures 2F and 2G). The acquisition of chimeric morphologies suggesting a partial temporal fate transformation was also evident in their axon arborization in the LH region. For instance, the mutant adPN with DM4 prospective fate consistently acquired the axon morphological features characteristic of both the wild-type DM4 and DM3 adPNs PI-1840 that extend arbors toward the ventral and dorsal domains of LH, respectively (compare Figure 2F3 with Figures 2A2 and 2C2). The partial cell-fate transformation probably occurred due to the presence of residual chinmo messages in mutant GMCs born right after the mitotic recombination. By contrast, a complete temporal fate transformation from DM4, DL5, and VM3(a) to DM3 can be inferred in the chinmo mutant NB clones, in which the adPN innervation of the DM4, DL5, and VM3(a) glomeruli was undetectable, and an enlarged DM3 glomerulus was noted.