Ine current injections of diverse frequenciesTo assess dynamic resonances in tectal neurons (Tan and Borst,) we injected them with cosineshaped currents of unique frequencies (Figure H). Spikes have been detected in MATLAB and verified visually, equivalent to stepinjections. The number of spikes as a function PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/3072172 of wave period (Figure J) was fit using the formulan max; exp abexp acd where T stands for wave period, even though ad are optimization parameters. From this match, the optimal wave period in ms was estimated (variable , ‘Spiking resonance’). Mean number of spikes averaged across waves of all frequencies was reported as ‘N spikes, cosine.’ The time continual of spikeoutput buildup with cosine injection period increase (parameter b from the formula above) was reported as a IQ-1S (free acid) site measure of spikenoninactivation in response to slowfrequency currents (‘Spiking resonance width’). For the highest frequency of cosine present injections (Hz) quantity of spikes in response to each and every present wave, taken as a function of wave quantity ns(x), was fit together with the formulans aexp bcd e exactly where x is really a continuous independent variable interpolating integer wave numbers, and ae are fit Stattic web parameters (Figure K). From this match we inferred properties of spiking activation and inactivation in every cell, and reported the wave number that was anticipated to generate highest spiking in this set depending on the fit (, ‘Wave buildup’), and the speed of spike quantity adaptation, provided by the c parameter in the formula above (, ‘Wave decay’). Cosine present injections of distinctive frequencies had been also made use of to estimate the index of spiking unpredictability, or jitter (, ‘Jitter’). To estimate this value, unique spike trains generated in response to cosine current injections (Figure I) had been represented by dfunctions inside a kHz trace, convolved using a Gaussian (s ms) and normalized. Pairwise scalar items (correlations) have been calculated for each and every appropriate pair of traces; these correlations have been then averaged across all sweep pairs. The resulting worth represented a measure of spiketime consistency, since it would be equal to for identical trains, and method for completely unmatched trains. To move from a ‘consistency index’ to a ‘jitter index’ we inversed the worth, and calculated a organic logarithm of it. The final formula could therefore be expressed asJitter ln mean calarProduct i ; aj whereCiarleglio et al. eLife ;:e. DOI.eLife. ofResearch articleNeuroscienceaj conv pikeTrainj ; gaussian msSynaptic stimulation protocolFor responses to synaptic stimulation (Figure J), we removed stimulation artifacts, averaged trials together with the similar interstimulus intervals (ISIs), and calculated total synaptic charge for each and every with the ISIs. The total charge Q as a function of ISI (Figure M) was fit with a functionQ aexp bc d e exactly where t stands for the ISI worth in ms, and ae are match parameters. From this match we reported estimated optimal interstimulus interval to create maximal synaptic input as ‘Synaptic resonance’ (ms); the parameter c in the fit formula as ‘Synaptic resonance width’ (the measure of sharpness of synaptic frequency tuning), as well as the maximal total synaptic charge made within a cell as ‘Synaptic charge’ (pA). We also calculated the ratio among the maximal total synaptic charge observed in every cell along with the projected total charge in response to infinitely slow stimulation (max Qe in the match formula above). We dubbed this variable ‘Synaptic PPF’ and interpreted it as a measure of nonlinear sy.Ine current injections of different frequenciesTo assess dynamic resonances in tectal neurons (Tan and Borst,) we injected them with cosineshaped currents of various frequencies (Figure H). Spikes had been detected in MATLAB and verified visually, equivalent to stepinjections. The number of spikes as a function PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/3072172 of wave period (Figure J) was fit using the formulan max; exp abexp acd exactly where T stands for wave period, although ad are optimization parameters. From this match, the optimal wave period in ms was estimated (variable , ‘Spiking resonance’). Mean variety of spikes averaged across waves of all frequencies was reported as ‘N spikes, cosine.’ The time continual of spikeoutput buildup with cosine injection period improve (parameter b in the formula above) was reported as a measure of spikenoninactivation in response to slowfrequency currents (‘Spiking resonance width’). For the highest frequency of cosine current injections (Hz) variety of spikes in response to every single existing wave, taken as a function of wave quantity ns(x), was match with all the formulans aexp bcd e where x can be a continuous independent variable interpolating integer wave numbers, and ae are fit parameters (Figure K). From this match we inferred properties of spiking activation and inactivation in every cell, and reported the wave quantity that was anticipated to make highest spiking within this set based on the match (, ‘Wave buildup’), and also the speed of spike number adaptation, provided by the c parameter in the formula above (, ‘Wave decay’). Cosine current injections of distinctive frequencies have been also used to estimate the index of spiking unpredictability, or jitter (, ‘Jitter’). To estimate this value, distinctive spike trains generated in response to cosine existing injections (Figure I) had been represented by dfunctions inside a kHz trace, convolved using a Gaussian (s ms) and normalized. Pairwise scalar goods (correlations) had been calculated for every single suitable pair of traces; these correlations have been then averaged across all sweep pairs. The resulting worth represented a measure of spiketime consistency, because it would be equal to for identical trains, and strategy for completely unmatched trains. To move from a ‘consistency index’ to a ‘jitter index’ we inversed the worth, and calculated a natural logarithm of it. The final formula could thus be expressed asJitter ln mean calarProduct i ; aj whereCiarleglio et al. eLife ;:e. DOI.eLife. ofResearch articleNeuroscienceaj conv pikeTrainj ; gaussian msSynaptic stimulation protocolFor responses to synaptic stimulation (Figure J), we removed stimulation artifacts, averaged trials together with the exact same interstimulus intervals (ISIs), and calculated total synaptic charge for every in the ISIs. The total charge Q as a function of ISI (Figure M) was match using a functionQ aexp bc d e exactly where t stands for the ISI value in ms, and ae are fit parameters. From this match we reported estimated optimal interstimulus interval to make maximal synaptic input as ‘Synaptic resonance’ (ms); the parameter c in the fit formula as ‘Synaptic resonance width’ (the measure of sharpness of synaptic frequency tuning), as well as the maximal total synaptic charge developed in a cell as ‘Synaptic charge’ (pA). We also calculated the ratio amongst the maximal total synaptic charge observed in each and every cell and also the projected total charge in response to infinitely slow stimulation (max Qe in the fit formula above). We dubbed this variable ‘Synaptic PPF’ and interpreted it as a measure of nonlinear sy.
was reported as ‘N spikes, cosine.’ The time continual of spikeoutput buildup with cosine injection period increase (parameter b from the formula above) was reported as a IQ-1S (free acid) site measure of spikenoninactivation in response to slowfrequency currents (‘Spiking resonance width’). For the highest frequency of cosine present injections (Hz) quantity of spikes in response to each and every present wave, taken as a function of wave quantity ns(x), was fit together with the formulans aexp bcd e exactly where x is really a continuous independent variable interpolating integer wave numbers, and ae are fit Stattic web parameters (Figure K). From this match we inferred properties of spiking activation and inactivation in every cell, and reported the wave number that was anticipated to generate highest spiking in this set depending on the fit (, ‘Wave buildup’), and the speed of spike quantity adaptation, provided by the c parameter in the formula above (, ‘Wave decay’). Cosine present injections of distinctive frequencies had been also made use of to estimate the index of spiking unpredictability, or jitter (, ‘Jitter’). To estimate this value, unique spike trains generated in response to cosine current injections (Figure I) had been represented by dfunctions inside a kHz trace, convolved using a Gaussian (s ms) and normalized. Pairwise scalar items (correlations) have been calculated for each and every appropriate pair of traces; these correlations have been then averaged across all sweep pairs. The resulting worth represented a measure of spiketime consistency, since it would be equal to for identical trains, and method for completely unmatched trains. To move from a ‘consistency index’ to a ‘jitter index’ we inversed the worth, and calculated a organic logarithm of it. The final formula could therefore be expressed asJitter ln mean calarProduct i ; aj whereCiarleglio et al. eLife ;:e. DOI.eLife. ofResearch articleNeuroscienceaj conv pikeTrainj ; gaussian msSynaptic stimulation protocolFor responses to synaptic stimulation (Figure J), we removed stimulation artifacts, averaged trials together with the similar interstimulus intervals (ISIs), and calculated total synaptic charge for each and every with the ISIs. The total charge Q as a function of ISI (Figure M) was fit with a functionQ aexp bc d e exactly where t stands for the ISI worth in ms, and ae are match parameters. From this match we reported estimated optimal interstimulus interval to create maximal synaptic input as ‘Synaptic resonance’ (ms); the parameter c in the fit formula as ‘Synaptic resonance width’ (the measure of sharpness of synaptic frequency