With this motif, the feedback loop involves only the inhibitory units and the two synapses that connect them. In contrast, all other feedback architectures involve additional units and synapses in the feedback loop. We studied the consequences of structural complexity of the feedback motif on the ability

of the model to compute steady-state responses to competing stimuli rapidly and reliably. We compared the performance of the reciprocal inhibition of feedforward lateral inhibition motif (Figure 4, circuit 2) with that of the next most structurally simple motif: feedback lateral inhibition by output units (Figure 7A, circuit 3). The parameter values for the circuit 2 model were chosen to be the same as those in Figure 5E. The parameter values for the circuit 3 model were chosen such that the circuit yielded output unit responses find protocol selleck inhibitor at steady state that were nearly identical to those from the circuit 2 model (Figures S5A–S5D). The quality of the match between the responses of the two circuits was particularly sensitive to the values of the parameters for circuit 3, with the best match occurring over a narrow range of values (Figures S5E–S5J). We measured calculation speed as the settling time, defined as the first time step after which responses did not change any further (Experimental Procedures). The time courses of the responses from the two models, calculated

for an RF stimulus of strength second of 9°/s and a competitor strength of 8°/s (relative strength = 1°/s), demonstrated that circuit 2 settled faster than circuit 3 (Figure 7E). This finding held true for all relative stimulus strength values (Figure 7F). Both models exhibited longer settling times as the relative strength between the competing stimuli decreased, consistent with the experimental observation that difficult discriminations take longer to resolve (Gold and Shadlen, 2007).

We assessed the reliability of the calculation as the consistency of the steady-state response. Gaussian noise was introduced into the calculation of the response for each unit at each time step. Consistency was quantified by calculating the Fano factor (Experimental Procedures), a metric that is inversely related to response consistency. The distribution of Fano factors at steady state was estimated using Monte Carlo analyses (Experimental Procedures). Comparison of the Fano factors from the two models for an RF stimulus of strength of 9°/s and a competitor strength of 8°/s (relative strength = 1°/s) showed that circuit 2 produced less variability (smaller Fano factor) than circuit 3 (Figure 7G; average Fano factors were 0.71 ± 0.01 and 0.78 ± 0.01, respectively; p < 10−4, rank-sum test). Circuit 2 exhibited superior reliability for all values of the RF stimulus from 1°/s to 9°/s (competitor = 8°/s), with the reduction in Fano factor being substantial (approximately 75%) when the RF stimulus was weaker or as strong as the competitor (Figure 7H).

With overtraining, rational

models of categorical choice are difficult to distinguish from simpler, habit-based accounts because highly-trained participants can produce a pattern of choices that resembles optimal responding by associating portions of the decision space with a particular action through extensive stimulus-response learning (Blair and Homa, 2003). Indeed, an influential framework suggests that model-free mechanisms, that capitalize on the extended learning Apoptosis inhibitor history to assign value to actions, may take precedence in control of action in stable, overlearned environments (Daw et al., 2005 and Dickinson and Balleine, 2002). It thus remains unknown (1) whether observers learn about the uncertainty associated with category membership (category variance), and use it to inform their decisions, and (2) which neural structures might encode category variability. The purpose of the current study was to address these questions. One important feature of signaling pathway unpredictable, fast-changing environments is that observers are obliged to distinguish between unexpected events that occur because of noise (i.e., an outlier) and those that occur

because of a state change in the environment (Yu and Dayan, 2005). For example, a bus might be late because of the vagaries of morning traffic (noise), or because new roadworks have introduced a fundamental delay that should be budgeted for when estimating subsequent journey times (a state change). When economic estimates change rapidly, new learning quickly becomes outdated, and so past category information should be discounted more steeply when choices are made

(Nassar et al., 2010 and Rushworth and many Behrens, 2008). Observers do indeed update their estimates of mean reward rate more rapidly when the environment is more volatile, a computation that has been associated with the anterior cingulate cortex (ACC) (Behrens et al., 2007). Model-based learning about the environment (e.g., explicitly encoding category uncertainty) will be most useful in a volatile world because it allows observers to distinguish optimally between outliers and those events that herald a change of state. On the other hand, in a volatile environment estimates of category variance will be of limited precision and expensive to compute. It thus remains unknown whether rational strategies will predominate during periods of environmental stability, or volatility. One efficient way of dealing with a volatile world would be to simply maintain the most recent information about each category in short-term memory—equivalent to updating category values in the frame of reference of the stimulus (rather than action) with a learning rate that equals or approaches one.

We do not detect the OvHts isoform (1156 aa) that is present in nurse cells and oocytes ( Petrella et al., 2007; data not shown). We find that presynaptic expression of Hts-M using elav-GAL4 in the hts mutant background (DfBSC26/hts1103) rescues the presynaptic retraction phenotype ( Figures 4D and 4F; retraction frequency 7% compared to 54% in

mutant animals, n > 98 NMJ). Importantly, this presynaptic rescue assay allows us to visualize Hts-M protein that is present in the presynaptic nerve terminal because we only resupply the protein in the motoneuron, not in the muscle. Hts-M protein is present within the presynaptic nerve terminal where it localizes at or near the presynaptic membrane but is not present within active zones marked by Brp ( Figure 4E). In contrast, postsynaptic expression of Hts-M causes a slight, Anti-cancer Compound Library mouse though not significant, reduction in the frequency of synapse retraction. However, we Adriamycin find that ectopic expression of Hts-M in muscle severely disrupts muscle and NMJ morphology ( Figure S4). Thus, is it not possible to accurately quantify the postsynaptic contribution of Hts-M to NMJ stability when

it is expressed via UAS-GAL4. From these data, both RNAi-mediated Hts knockdown and transgenic rescue, we conclude that Hts is required presynaptically to stabilize the NMJ. We next analyzed synaptic transmission, comparing wild-type with the hts1103/DfBSC26 allelic combination and with animals expressing htsRNAi in presynaptic neurons. We find a significant increase second in the average quantal amplitude in the hts1103/DfBSC26 mutant

animals and a corresponding decrease in average quantal content ( Figures S5A–S5C). However, the increased mepsp amplitude was not observed in animals expressing htsRNAi presynaptically. This could be due to a less severe knockdown of Hts protein. Alternatively, the increased average mepsp amplitude could reflect a postsynaptic activity of Hts. This possibility is consistent with the prior demonstration that knockdown of postsynaptic spectrin causes a comparable increase in quantal size ( Pielage et al., 2006). We also find that synaptic transmission is considerably more variable in hts loss of function animals ( Figure S5D). We plotted average mepsp versus average quantal content for individual NMJ recordings. There is more variation both in mepsp amplitude and quantal content compared to wild-type. This is consistent with prior studies demonstrating highly variable recordings at NMJ undergoing retraction ( Massaro et al., 2009, Pielage et al., 2005 and Pielage et al., 2008). The observed increase in release variability is less severe than after knockdown of α-/β-spectrin, which might be accounted for by enhanced NMJ growth that is unique to hts mutant animals (see below).

Even when hearing is intact, the development of perceptual skills can be impaired by removing specific acoustic features from the rearing environment. For example, perceptual deficits are found in songbirds that have been deprived only of song exposure during the juvenile period. When evaluated as adults, birds reared in the absence of adult songs exhibit frequency discrimination deficits. Furthermore, birds reared without hearing sibling or adult vocalizations show poor frequency discrimination and song note recognition (Njegovan and Weisman, 1997 and Sturdy et al., 2001). These studies suggest that the maturation of auditory perception is not simply a matter

of hearing, but requires experience with specific features of the acoustic environment. If the loss of early BMN 673 molecular weight auditory experience degrades behavioral performance, then the opposite manipulation (augmented sound exposure) might be expected to improve perception. Studies that address this issue commonly expose developing animals to a specific acoustic environment, often for a prolonged period. However, the effects are usually assessed by recording from the nervous system (below), and the behavioral impact is not well understood. When developing rats are exposed to a single frequency for 3 weeks and then tested on a frequency discrimination task in adulthood, their perceptual skills selleck chemicals llc display an intricate set of changes. As adults, these animals actually display

poor discrimination at the exposed frequency, yet their discrimination of adjacent frequencies is significantly better than controls (Han et al., 2007). In contrast, when young animals are exposed to noise for days or weeks, behavioral measures reveal diminished or delayed capacities (Philbin et al., 1994, Zhang et al., 2008, Zhou and Merzenich, 2009 and Sun et al., 2011). Because the tone or noise levels used in these experiments appear to be too low to

injure the cochlea, the behavioral impact is probably attributable to central changes (below). A second approach to evaluate how acoustic stimulation influences development is to assess auditory learning. An exceptional series of studies by Gilbert Gottlieb, 1975a, Gottlieb, 1975b, Gottlieb, 1978, Gottlieb, 1980, Gottlieb, 1981 and Gottlieb, 1983) used a biologically relevant form of learning, called vocal imprinting, to examine the role of early MRIP auditory experience in behavioral responses to sound. Devocalized and isolated ducklings do not develop accurate sensitivity to maternal calls, but this perceptual skill is rescued by stimulating the ducklings with natural vocalizations. Preference for the natural call note repetition rate and frequency modulation must be induced or maintained by experiencing those acoustic features. However, merely hearing the right sound may not be sufficient to influence perceptual development; it is often gated by nonauditory factors, such as the state of arousal (Gottlieb, 1993, Sleigh et al.