, 2009 and Britten, 2008) Correlated noise among pairs of neuron

, 2009 and Britten, 2008). Correlated noise among pairs of neurons was examined in two groups of animals: one group (“naive”) was only trained to fixate; the other group (“trained”) also learned to perform a fine heading discrimination task. Noise correlations were significantly weaker in trained than naive animals, whereas tuning curves, response variability, and discrimination thresholds of individual neurons were similar. Importantly,

training reduced noise correlations uniformly, regardless of tuning similarity between pairs of neurons. As a result, if all neurons contribute equally to perception, this change in correlated noise is unlikely to account for improvements in perceptual sensitivity with training. MG-132 price Monkeys were presented with two types of heading stimuli while maintaining fixation on a head-fixed target: inertial motion delivered by a motion platform in the absence of optic flow (vestibular

condition) and optic flow stimuli presented while the animal was stationary (visual condition, see Experimental Procedures for details). Consistent with previous findings (Gu et al., 2006 and Takahashi et al., 2007), many MSTd neurons were tuned to heading direction, and their responses mainly followed the Gaussian velocity profile of the stimulus (Figure 1A). We analyzed responses obtained during the middle 1 s of the stimulus period, during which neuronal activity was robust. Tuning curves of two simultaneously recorded cells are shown in Figures learn more 1B and 1C. The similarity of heading tuning between pairs of neurons was quantified as the

Pearson correlation coefficient of mean responses across all stimulus directions (“signal correlation”, rsignal). For this example pair Terminal deoxynucleotidyl transferase of neurons, rsignal = 0.83 and 0.79 for the visual and vestibular conditions, respectively. As in other cortical areas, the spike counts of MSTd neurons in response to an identical stimulus vary from trial to trial, as illustrated in Figure 1D (visual condition) and Figure 1E (vestibular condition). Each datum in these plots represents the spike counts of the two neurons from a single trial. Because heading direction varied across trials, spike counts from individual trials have been z-scored to remove the stimulus effect and allow pooling of data across directions (see Experimental Procedures). “Noise correlation” is then computed as the Pearson correlation coefficient of the normalized trial-by-trial spike counts, and reflects the degree of correlated variability across trials. For this example pair of cells, there was a weak positive correlation, such that when one neuron fired more spikes, the other neuron did as well (visual condition: rnoise = 0.29, p = 0.04, Figure 1D; vestibular condition: R = 0.14, p = 0.3, Figure 1E). We first examined whether correlated noise in MSTd depends on stimulus modality (Figure 1F).

We thank members of the Action Lab for fruitful discussions Fina

We thank members of the Action Lab for fruitful discussions. Finally,

we thank three anonymous reviewers of a previous version of this manuscript for their thoughtful comments and suggestions. “
“Cortical projections to the striatum form the front end of the cortical-basal ganglia-thalamo-cortical loops. The PLX4032 anatomy of these networks (Alexander et al., 1986, Haber et al., 2006, Middleton and Strick, 2000, Parent and Hazrati, 1995 and Parthasarathy et al., 1992) and the physiology of single neuron responses in areas of frontal cortex (Averbeck and Lee, 2007, Funahashi et al., 1991, Fuster, 2008, Histed et al., 2009, Miller and Cohen, 2001 and Pasupathy and Miller, 2005) and the striatum (Apicella et al., 2011, Barnes et al., 2005, Hollerman et al., 1998, Jin et al., 2009, Lauwereyns et al., 2002a, Lauwereyns et al., 2002b and Simmons et al., 2007) have been extensively studied. Yet the contributions of these networks to normal behavior are still unclear. There are numerous hypotheses, mostly specifying the striatal transformation of cortical inputs, including dimensionality reduction (Bar-Gad et al., 2003), reinforcement learning (Dayan and Daw, 2008, Doya, 2000,

Frank, 2005 and Parush et al., 2011), habit formation (Graybiel, 2008), motor learning (Doyon et al., 2009), sequential motor control (Berns and Sejnowski, 1998, Marsden and Obeso, 1994 and Matsumoto et al., 1999), response vigor (Turner and Desmurget, 2010), action selection (Denny-Brown and Yanagisawa, crotamiton 1976, Grillner et al., 2005, Hazy et al., 2007, Houk et al., 2007, Humphries et al., 2006, Kamali Sarvestani et al., 2011, Mink, selleck compound 1996, Redgrave et al., 1999 and Rubchinsky et al., 2003), execution of well-learned, automated motor plans (Marsden, 1982), and the trade-off between habitual and goal-directed or cognitive action planning (Daw et al., 2005), also conceptualized as a trade-off between attention demanding cognitive and automatically

executed actions (Norman and Shallice, 1986). The action selection hypothesis, first proposed on the basis of monkey striatal lesion data (Denny-Brown and Yanagisawa, 1976), suggests that the cortex generates ensembles of possible actions and the striatum selects from among these actions (Houk et al., 2007, Humphries et al., 2006 and Mink, 1996). The action selected by the striatum, via the rest of the basal ganglia (BG) circuitry, disinhibits the thalamo-cortical and brainstem networks that lead to execution of the selected action and inhibits the networks that represent competing actions. Many experimental studies and models also suggest that the striatum is important for reinforcement learning (RL) or learning from feedback (Amemori et al., 2011, Daw et al., 2011, Frank et al., 2004, Histed et al., 2009, Kamali Sarvestani et al., 2011, O’Doherty et al., 2004, Pasupathy and Miller, 2005 and Samejima et al.

, 1989, Hudspeth and Gillespie, 1994, Ricci and Fettiplace, 1997 

, 1989, Hudspeth and Gillespie, 1994, Ricci and Fettiplace, 1997 and Ricci et al., 1998). BTK inhibitors Here, we used either apical perfusion or a local pipette to apply a 20 μM Ca2+ solution onto the hair bundle of rat OHCs and observed an increase in the peak current amplitude due to removal of a Ca2+ block of the channel (Figure 7A;

Pan et al., 2012 and Ricci and Fettiplace, 1998). We consistently found a large change in the resting open probability, as previously described (Figures 7A and 7B) (Beurg et al., 2008 and Beurg et al., 2010). In contrast to previous data from low-frequency hair cells (Ricci and Fettiplace, 1997 and Ricci et al., 1998), the shift observed in resting open probability was unaffected by intracellular Ca2+ buffering. With

no correction for baseline changes, our current-displacement plots underestimate the actual baseline shift, but nonetheless, reveal a large leftward shift (Figure 7B). Additionally, unlike in low-frequency cells, lowering external Ca2+ did not slow the adaptation time constants in OHCs; rather, the proportion of the slower time constant was increased (Figure 7C). Due to the difficulty of pulling the hair bundle with our stiff probe, we used the permeable blocker dihydrostreptomycin (DHS) to provide a better estimate of resting open probability by blocking the MET current in lowered external Ca2+ (Figures 7D and 7E). These results confirmed that large shifts in the resting open probability were independent of internal Ca2+ buffering. Taken together, these data suggest VEGFR inhibitor that external Ca2+ regulation of MET resting open probability is independent of adaptation and intracellular DNA ligase Ca2+ levels and is mediated by an external Ca2+ site. Long-standing theories, largely based on data obtained from turtle, frog, and mammalian vestibular hair cells, posit that Ca2+ entry through MET channels is required for adaptation (Corey and Hudspeth, 1983b, Crawford et al., 1991, Eatock et al., 1987, Peng et al.,

2011 and Ricci et al., 1998). Subsequent experiments identified two components of adaptation (Vollrath and Eatock, 2003 and Wu et al., 1999), each driven by Ca2+ entry, a fast component, where multiple mechanisms have been proposed (Bozovic and Hudspeth, 2003, Cheung and Corey, 2006, Choe et al., 1998, Crawford et al., 1991 and Stauffer et al., 2005) and a slower (motor) component, controlled by myosin isozymes (Gillespie and Cyr, 2004). Initial work from mammalian auditory hair cells suggested adaptation was faster but largely similar to that reported in other hair cell types, and mechanisms of mammalian auditory adaptation have remained largely unexplored (Kennedy et al., 2003). Our data challenge these views of adaptation by demonstrating that Ca2+ entry does not drive adaptation in mammalian auditory hair cells and that motor adaptation as described in other hair cell types has at best a limited role.

Consistent with this idea, earlier

work has shown that lo

Consistent with this idea, earlier

work has shown that localized downregulation of dendritic A-type potassium channels can occur during induction of long-term potentiation (Frick et al., 2004). In both cases, downregulation of dendritic A-type potassium channels has been shown to require activation of NMDA receptors. Earlier work indicated that A-type potassium channels have a range of effects on dendritic integration CP-868596 cost in CA1 pyramidal neurons, acting to either linearize or suppress excitatory postsynaptic potential summation (Cash and Yuste, 1999; Hoffman et al., 1997). One of the most interesting findings in the paper is that the capacity of recurrent inhibition to reduce the amplitude of dendritic glutamate-evoked depolarizations that are subthreshold for generation of dendritic spikes is weaker in dendritic branches that generate

strong dendritic spikes. This result is even more surprising given that much of the recurrent inhibitory input recruited by stimulation of the alveus will be located at the soma. Application of GABA to these dendritic branches suggested that the difference in the impact of recurrent inhibition on different dendritic branches is not due to differences in the density of GABA receptors or the reversal potential for GABA. These data suggest that the number or release Depsipeptide ic50 probability of GABAergic inputs recruited during recurrent inhibition is lower in dendritic branches that generate strong dendritic spikes. How this occurs is unclear, but it may involve the release of a retrograde signal, possibly in response to generation of dendritic spikes.

Because the conversion of weak dendritic branch Thymidine kinase spikes to strong dendritic branch spikes did not influence the capacity of recurrent inhibition to reduce the amplitude of subthreshold glutamate-evoked depolarizations, this process presumably takes time to develop and occurs subsequent to downregulation of A-type potassium channels in these dendritic branches. Whether this is associated with similar, or perhaps opposite, changes in feedforward inhibition on these dendritic branches is unclear. Finally, it is worth commenting on the impact of the findings on the overall excitability of CAI pyramidal neurons. Earlier work has shown that pairing dendritic spikes with action potentials can convert weak dendritic spikes to strong dendritic spikes (Losonczy et al., 2008), thereby enhancing dendritic excitability. The current work by Müller and colleagues (Müller et al., 2012) adds to this data, showing that dendritic branches that generate strong dendritic spikes are also associated with weaker recurrent inhibition. This would be expected to further enhance dendritic excitability.

It will therefore be interesting

to investigate the autoi

It will therefore be interesting

to investigate the autoinhibitory effects of psychotropic drugs accumulated in synaptic vesicles on specific network activity profiles within cortical (Goto et al., 2010) and subcortical (Kellendonk, 2009) pathways as well as various neurotransmitter systems (Lisman et al., 2008), especially DA signaling, and differential effects of other classes of psychotropic drugs (Sulzer, 2011). All animal work was approved by the Kollegiales Leitungsgremium of the Franz-Penzoldt Zentrum, Erlangen and was conducted in conformity with the Animal Protection Law of the Federal Republic of Germany and the guidelines of the State of Bavaria. Hippocampal Raf phosphorylation neuronal cultures were prepared from 1- to 4-day-old Wistar rats. Briefly, newborn rats were sacrificed by decapitation. The hippocampus was removed from each brain and was transferred into ice-cold Hank’s salt solution, and the dentate gyrus was cut away. After digestion with trypsin (5 mg ml−1), cells were triturated mechanically and plated in MEM, DAPT mw supplemented with 10% fetal calf serum and 2% B27 Supplement (all from Invitrogen, Taufkirchen, Germany). If required, neurons were transfected on DIV3 (days in vitro) with spH (Sankaranarayanan et al., 2000)

under the control of a synapsin promoter with a modified calcium phosphate method by Threadgill et al. (1997). Experiments were performed between DIV25 and DIV30. N1E115 neuroblastoma cells were maintained at 37°C in 5% CO2 in DMEM (Invitrogen) with 5 g/l glucose and were supplemented with 10% fetal bovine serum (Biochrom, Berlin) Urease and 1% penicillin/streptomycin solution (Biochrom). Cells were trypsinized and plated in 3.5 cm dishes (Corning, Lowell, MA, USA). For recordings from NaV1.6r, N1E115 cells were transfected on the next day with 1 μg of cDNA for each dish and 0.5 μg of pEGFP-C1 (Mountain View, CA, USA) with Nanofectin (PAA, Pasching, Austria) according to the manufacturers’ protocol.

The mNaV1.6 was a gift from E. Leipold and S. Heinemann (Leipold et al., 2006). The TTX-resistant variant mNaV1.6r Y371S was constructed with the use of the QuikChange II XL Site-Directed Mutagenesis Kit (Agilent Technologies, Santa Clara, CA, USA). Experiments were conducted at room temperature on a Nikon TI-Eclipse inverted microscope (60×, 1.2 NA water-immersion objective; Perfect Focus System). Fluorescence was excited by a Nikon Intensilight C-HGFI through excitation filters centered at 482, 561, and 628 nm using dichroic long-pass mirrors (cutoff wavelength 500, 570, and 660 nm, respectively). The emitted light passed emission band-pass filters ranging from 500 to 550 nm, 570 to 640 nm, and 660 to 730 nm (Semrock, Rochester, NY, USA) and was projected onto a cooled EM-CCD camera (iXonEM DU-885 or iXonEM DU-897; Andor). Coverslips were placed into a perfusion chamber.

The dose of CNO was chosen based on a previous study reporting hi

The dose of CNO was chosen based on a previous study reporting high in vivo efficacy at doses between 1 and 5 mg/kg on locomotor activity and stereotypy in a transgenic mouse expressing the hM3Dq receptor within the forebrain (Alexander et al., 2009). CNO reduced the firing rate of a substantial portion

of the MD units (Figures 2B–2D). To quantify this effect, we first INK1197 cost calculated the ratio of firing rates after saline and CNO for each unit; the distribution of ratios in the sample was significantly different than that expected by chance (p < 0.01, Wilcoxon signed-rank test) (Figure 2C). We next compared firing rates after saline and CNO injections for each neuron independently. CNO decreased firing rate significantly (p < 0.05 by paired t test) in 30 neurons (48%). In an additional 11 units (17%), CNO increased the firing rate. Using a more stringent Bonferroni correction (p < 0.0008) 16 (25%) and 6 (9.5%) units were inhibited or activated

by CNO, respectively. For both analyses neurons with decreased firing rate were overrepresented as a consequence of CNO treatment (Binomial test: p < 0.01, p < 0.05 for Bonferroni-corrected values). Importantly, CNO treatment did not completely silence MD neurons; the neurons with decreased firing rate showed an average decrease of 38.7% ± 5.3%. This decrease in firing rate was not related to changes in locomotor activity or to differences in the isolation of single units (Figures S2A–S2C). The CNO-mediated decrease in firing rate was not observed HIF-1 activation in wild-type mice that do not express hM4D, demonstrating its dependence on hM4D (Figure 2C, inset). While hyperpolarization of thalamic cells can induce a shift in the firing pattern from a tonic to a bursting mode due to the activation of T-type Ca2+ channels (Jahnsen and Llinás, 1984), we did not observe a significant change in the fraction of burst firing in vivo after CNO injection (Figure S2D). Due to the strong projections of the MD to the orbitofrontal cortex (OFC) (Figure 1D), we first tested whether decreasing MD activity affects reversal learning, a cognitive process of executive the function that is sensitive to orbitofrontal lesions (Schoenbaum et al., 2002). To address reversal

learning, we developed an operant-based reversal learning task for the mouse in which lever presses are rewarded in the presence of one visual cue (S+), but not in the presence of another visual cue (S−) (discrimination phase). After mice reached criterion the contingencies were reversed (reversal phase) (Figure 3A). The acquisition of the discrimination phase was not affected by decreasing MD activity (repeated ANOVA p = 0.17) though CNO-treated MDhM4D mice showed a tendency for a delay in learning during the first three days of acquisition (Figure 3B). In contrast, reversal learning was clearly impaired in CNO-treated MDhM4D mice when compared to the three control groups (repeated ANOVA followed by Bonferroni correction for group comparisons ∗p < 0.05, ∗∗p < 0.

We used our biophysical measurements to calculate the effect of p

We used our biophysical measurements to calculate the effect of proprioception on undulatory waves in surroundings with different viscosities and uncovered a compelling explanation for the adaptation of undulatory wavelength on external load. At low loads, the worm undulates with a long wavelength. At high loads, the worm undulates with a short wavelength. This dependence has an intuitive biomechanical explanation. As external viscosity increases, it takes longer for a posterior body region to bend in response to any curvature change in its anterior neighbor. Increasing the time scale of the

bending response increases the phase difference between the shapes of neighboring body segments, leading to a smaller undulation wavelength. The small size and experimental accessibility of the C. elegans motor circuit allows the possibility of modeling locomotion that integrates the dynamics of all neuronal and muscular components. Our OTX015 clinical trial results suggest Luminespib in vivo that a full model of C. elegans locomotion must integrate the biomechanics of undulatory movement with neuromuscular activity to properly incorporate the role of proprioception within the motor circuit. Wild-type, transgenic, and mutant worms were cultivated using standard methods (Brenner, 1974). Detailed strain information can be found in the Supplemental Information. The transgenic worms used in all optogenetic

experiments were cultivated in the dark at 20°C on NGM plates with Escherichia coli OP50 and all-trans retinal. We performed all experiments using adult hermaphrodites within a few hours after their final molt. Custom microfluidic devices were fabricated in PDMS using soft lithography techniques. In the pneumatic microfluidic device, the channel was flanked by two chambers that could be alternatively enough pressurized and depressurized with a valve system under computer control using custom software written in LabVIEW (National Instruments, Austin, TX). We loaded each microfluidic channel with NGM buffer

or dextran solution (∼20% dextran in NGM [wt/vol] in most cases). An individual worm was flowed into the inlet of each microfluidic channel and worm position within each channel was manually controlled by syringes connected to polyethylene tubing. Experiments were performed on Nikon microscopes (TE2000 or Eclipse LV150) under 4× magnification with dark-field illumination. Image sequences were taken by a CCD camera (Imaging Source) and recorded on a computer at 30 Hz using IC Capture software (Imaging Source). Image analysis was performed using custom software written in MATLAB (MathWorks, Inc. Natick, MA) following methods described in (Fang-Yen et al., 2010). We imaged calcium dynamics within muscle cells of worms partially trapped in microfluidic channels, using methods similar to those described in (Chen, 2007). GCaMP3 and RFP were excited by LEDs filtered at 448–492 nm and 554–572 nm, respectively, using Semrock single-bandpass filters.

, 2010) By demonstrating a regulatory role of DNA demethylation

, 2010). By demonstrating a regulatory role of DNA demethylation in cognitive function (Rudenko and Tsai, 2013), these studies provide the rationale to further study the role of the Tet proteins in the nervous system. In the current work, we show that the expression of a number of genes is dysregulated in the cortex and hippocampus of Tet1 knockout (Tet1KO) mice. Interestingly, the most prominent Selleck Vismodegib category of downregulated genes is comprised of multiple neuronal activity-regulated genes that include Npas4, c-Fos, Arc, Egr2, and Egr4 ( Loebrich and Nedivi, 2009 and Ebert

et al., 2013). We also found that while Tet1KO mice display normal memory formation, they showed specific impairments in extinction Selleck TSA HDAC learning. Moreover, we show that while hippocampal long-term potentiation was intact in Tet1KO animals, they had abnormally enhanced long-term depression compared to controls. We performed methylation analysis of a key upstream neuronal activity-regulated gene, Npas4, and found hypermethylation

of the promoter region in Tet1KO animals compared to controls, both in naive mice and after extinction training, which could lead to the reduced expression of Npas4 and its downstream targets. Our study identifies an important role for Tet1 in regulating the neuronal activity-regulated genes, hippocampal synaptic plasticity, and memory however extinction. Reports of high levels of 5hmC in the CNS genome (Kriaucionis and Heintz, 2009 and Szulwach et al., 2011) prompted a search for potential functions for the Tet1 methylcytosine dioxygenase in the mouse brain. We utilized a previously characterized Tet1 knockout (Tet1KO) mouse strain in which exon 4 of Tet1 is deleted, leading to an out-of-frame fusion of exons 3 and 5 and creating a Tet1 null allele ( Dawlaty et al., 2011). Loss of Tet1 mRNA was confirmed by real-time quantitative PCR in cortex and hippocampus ( Figure S1A available online). We also quantified

all three Tet mRNA levels in hippocampal and cortical tissues from wild-type mice and found that all three Tets are expressed in both hippocampus and cortex ( Figure S1B). The presence of all Tet proteins in the CNS may lead to potential compensatory effects caused by the loss of a single Tet family member. Since Tet proteins are responsible for the conversion of 5mC to 5hmC, we wanted to determine how Tet1 ablation affects 5mC and 5hmC levels in the brain. Global genomic 5mC and 5hmC contents in the hippocampi and cortices of 4-month-old Tet1KO and control Tet1+/+ mice were assessed by immunohistochemistry (Figure 1A) and quantified by liquid chromatography combined with tandem mass spectrometry using multiple reaction monitoring (LC/MS/MS-MRM).

This gating system can only function if LNvs and non-LNvs have di

This gating system can only function if LNvs and non-LNvs have differently phased neuronal activity. However, most Drosophila clock neurons have similarly phased molecular clocks. We propose that molecular clocks in different clock neurons regulate divergent sets of output genes to generate distinct

phases of neuronal excitability. This would be analogous to the mammalian circadian system, in which molecular clocks in different tissues drive tissue-specific outputs (e.g., Storch et al., 2002). In summary, our genetic dissection of a circadian neural circuit reveals an unexpected PI3K activity and essential role for inhibitory signals from non-LNvs (E cells) in shaping activity profiles at dawn and a mechanism for how clock neurons couple together to promote robust

rhythms. For a complete list of fly stocks used in this paper, see Supplemental Experimental Procedures. For LD experiments, larvae were entrained to 5 days of 12:12 LD cycles at 25°C and tested on the sixth day as third-instar larvae. For DD experiments, larvae were entrained to 12:12 LD at 25°C for 3–4 days and tested on the second or third day in DD. Larvae were removed from LD or DD immediately prior to testing. Approximately 15 larvae were placed in the middle of an 8.5-cm-diameter agar-filled Petri dish, and the number of larvae in the light and dark was recorded after 15 min as in Mazzoni et al. (2005), with the following this website minor modifications: (1) to speed up scoring, any larvae visible through the lid of the plate were recorded as being on the light side even if crossing the midline; (2) because larvae could be found on the walls and lid on both the light and dark sides

of the plate, 17-DMAG (Alvespimycin) HCl they were included in the scoring; (3) light intensity was reduced by moving the light source away from the plate rather than by adding filters; and (4) the light source used was a circular fluorescent 22 W GE Cool White bulb. Data are plotted as percentage of larvae in the dark. Each data point is the average of three or more experiments, with each experiment consisting of ∼45 larvae on three plates assayed simultaneously, except when insufficient larvae of the required genotype were obtained from individual crosses. In this case, data from separate experiments were added in chronological order to reach a total of ∼45 larvae. All experiments on larvae in LD were carried out between ZT3 and ZT6 and in DD between CT11.5 and CT13 (CT12) and CT23.5 and CT1 (CT24). For TrpA1 experiments, larvae were entrained to LD cycles at 20°C for 7 days, then moved to DD and tested on the second day in DD. Larvae were at 26°C for only the duration of the assay.

All marker positional data were filtered

using the same f

All marker positional data were filtered

using the same filter level reported by Brice et al.3 Positional data were then used in conjunction with direction cosines to determine the three-dimensional coordinate data for the centre of the hammer’s head. These positional data were used to calculate hammer linear velocity (calculated speed) and cable force (calculated force).3 All calculated and measured force data were normalised for hammer weight to account for the fact that males use a heavier hammer than females. Two regression models were developed that allowed speed to be predicted from measured find more force data (predicted speed). The calculated speed data and calculated force data were used to develop these regression models. All calculated speed data used in the regression model development were squared due to the mechanical relationship that exists between centripetal force and linear velocity squared (Equation (1)). The first regression model was derived

from the square of the calculated speed and the calculated CFTR activator force (non-shifted regression). While the second model was derived from the square of the calculated speed and a time shifted calculated force (shifted regression). The shifted regression model was developed because earlier work showed a phase lag between speed and cable force3 and it was thought that accounting for the phase lag in the model development may lead to a model that would produce speed data that were more accurate. As the magnitude of this phase lag varies

depending on turn number, throw, and athlete, it is not possible to Ketanserin apply the same time shift to every throw. It was therefore decided to time shift the calculated force such that for each throw the final peaks in the calculated force and calculated speed coincided. This time shift was applied to ascertain if removal of the phase lag resulted in a more accurate regression. As only the final peaks were aligned, there was no change in the frequency of the force data. The calculated speed and calculated force data used to calculate the shifted regression were also trimmed as the final peak in the calculated force data occurred prior to release whereas the final peak in speed occurred at release. The calculated force data were trimmed so that the final peak was the final data point and the calculated speed data were trimmed by the same amount at the start. This was done so that both data sets were the same size. A shifted and non-shifted regression equation was developed for each of the participant’s 10 throws and all data points of each throw were used to develop these equations. The MATLAB software suite (The Mathworks, Natick, MA, USA) was used to determine the regression equations and the y intercepts for both were also forced through (0,0) since Equation (1) predicts zero speed for zero force. Averages of the gradients of the two linear regression equations were determined for the cohort.