Ethics statement

All experiments were approved by the Institutional Animal Care and Use Committee of the University of Buenos Aires.

Surgery

Adult canaries were acquired from a local breeder. The experiment was performed on 11 male canaries (Serinus canaria) either on the left HVC (N = 6) or the right HVC (N = 5). The morning of the experiment, birds were deprived from food and water 45 minutes prior to surgery. They were given injections in the pectoral muscle with 20% urethane (90–120μl total; Sigma, St. Louis, MO), administered in three 30–40μl doses at 30 minutes intervals. Immobilization of the bird was done with a stereotactic device. After topical application of xylocaine, the skull of the birds was exposed by sculp retraction. A craniotomy of 2mm x 1mm was made over the corresponding left or right HVC which was located stereotactically and the dura over the recording site was removed. Body temperature was maintained with an electric blanket.

Temperature manipulation

Two custom built cooling devices were used, one for placing over the left and the other over the right HVC. Their capabilities are based on a Peltier module, which provides a constant temperature decrease on one side while the other is kept at a constant temperature. Previously, a 2A current supply was used for obtaining a temperature decrease of 10°C [16, 24]. Such high currents circulating continuously near the recording site interfere with the electrophysiological signal about a minute after onset. To avoid this we increased the Peltier size four times and performed a pulsed current protocol with a period of 6 seconds (S1 Fig). To avoid neuronal damage due to abrupt temperature changes, currents were modified in steps of 0.25A and the temperature was allowed to stabilize for at least 3 minutes. We achieved almost constant fluctuating temperatures with a maximum decrease of approximately ΔT = −9°C and a maximum amplitude of fluctuation of less than 0.5°C. Calibrations of the devices were made on two supplementary animals not used for the electrophysiological recordings (S1 Fig).

We obtained between 4 and 10 different temperatures for 33 different recording sites with an average of 8.7 temperatures (30 sites with 7 or more temperatures). To rule out the possibility of permanent effects due to cooling, we made half the measurements increasing the current and then half going back to 0A. A metal part was designed to allow the most efficient heat transfer between HVC and the cooling device (S1 Fig). It has a pad of 1mm x 2mm surface and 1mm thickness that is placed on the surface of the brain right above HVC with a 0.5 mm diameter hole made in its center. Through that orifice we lowered the measuring electrode.

Electrophysiology

Extracellular recordings of spontaneous neural activity in HVC were performed using tungsten microelectrodes (Parylene-C-insulated, 0.8-1.2MΩ, MicroProbes). The electrodes were lowered between 200 and 800 μm beneath the surface of the brain, because HVC is approximately 1mm thick and is located superficially. Electrode entrance in HVC was identified by its neuron’s characteristic spontaneous bursting patterns. Signals were preamplified 10X near the electrode via a custom built, low power, low noise and battery powered instrumental amplifier [72]. Then this signal was fed into an Instrumentation Amplifier And Signal Conditioner (Brownlee Precision Model 440), where it was further amplified 100X, bandpass filtered between 300 Hz and 3 kHz and digitized at 20 kHz. The final output was partitioned in 5 to 10 contiguous files and stored on a computer through a data acquisition device (National Instruments M Series). Total recording times for each site range from 2 to 8 minutes (4.8 min in average, 31 sites above 4 min).

Data analysis

Computational modeling

We implemented single-compartment conductance-based biophysical models of neurons from the HVC following the ones developed by Daou et al. [67]. Simulations were performed in C using a Runge Kutta 4 algorithm for integration with 0.01ms steps, and the analysis of the model output was made in MATLAB. Models are of Hodgkin-Huxley-type with additional currents. The membrane potential of the neuron obeys the following equation: (1) where C_m is the membrane capacitance, I_K and I_Na are spike-producing currents, I_Ca−L and I_Ca−T are Ca²⁺ high-threshold L-type, and low threshold T-type currents, I_SK is a small-conductance Ca²⁺-activated K⁺ current, I_Nap is a persistent Na⁺ current, I_KNa is a Na⁺-dependent K⁺ current, I_A is an A-type K⁺ current, I_h is a hyperpolarization-activated cation current, and I_L is a leak current. All these ionic currents were found experimentally in slice electrophysiology experiments at room temperature in all three HVC neuronal types and detailed equations and parameters can be found in Daou et al. [67]. Here we briefly comment on the different currents whose relative weight change across neuron types, shaping their characteristic behavior at room temperature: HVC_X neurons show spike adaptation due to large I_SK and I_KNa currents and have a sag due to I_h current. They show rebound firing or depolarization due to large I_Ca−T current. HVC_RA neurons show lack of excitability in response to depolarizing pulses due to large I_SK, I_KNa and I_A currents, and a delay to spiking due to large I_A current. HVC_INT neurons show high firing frequency with no adaptation even to small pulses due to reduced I_SK, I_KNa and I_A, a prominent sag and hence a rebound firing due to large I_h current and also fire spontaneously with variety of patterns in slice preparations. Sodium I_Na and potassium I_K also vary among neuron types. The most interesting and novel observation in this description is the role that I_SK may play in the bursting behavior of HVC_RA and HVC_X in vivo, having a slow current dependent on the dynamics of the free intracellular Ca²⁺ concentration. Although bursting did not appear at room temperature, experiments in vivo where the temperature is around 40°C show clearly their bursting behavior [66].

A typical current is as follows: (2) where I_c is any of the currents described before, g_c its maximal conductance, V_c its reversal potential and x is a gating variable whose kinetics are governed by: (3) where x_∞(V) is a defined function of voltage and τ_x is the time constant, with 1/τ_x a factor that multiplies the rate constants (usually called k values) of opening and closing of the ion channel which for some currents has a membrane potential dependency τ(V).

For different simulations we used artificially applied external currents I_app, synaptic currents I_syn and some degree of current noise I_noise. The first is just a current clamp: constant input with a finite duration. The second needs to have a presynaptic neuron releasing neurotransmitter T. Equations used read as follows [75, 76]: (4) (5) (6) where a T(t) approximates a Heaviside function that turns on with the voltage of the presynaptic neuron V_pre. Parameters θ_X = −10mV, θ_RA = −20mV and σ_X = 7mV σ_RA = 5mV were selected after evaluating the amplitude and maximum voltage achieved by HVC_X and HVC_RA simulations in order to have neurotransmitter release at approximately the top 10% of the simulated presynaptic spikes. The rate constants of the gating variable s, α = 2.2mM/ms and β = 0.381/ms, and the maximal neurotransmitter concentration T_max = 1.5mM were selected as in Gibb et al. [70]. Typical values for V_rev are 0mv for excitatory synapses and -80mV to -100mV for inhibitory synapses, and here we modeled only excitatory synapses. g_syn = 3.0nS and 2.5nS for HVC_X and HVC_RA were selected to have slightly less than 100% effectiveness in eliciting a spiking response in an interneuron when noise is present and to be able to reproduce the in vivo recorded firing pattern at a single excitatory synapse as recorded by Kosche et al. [28].

We added to some simulations standard Gaussian white noise: (7) where n is a random number drawn at each time step from a normal distribution and its amplitude I_amp = 400pA. It was changed in 0.1ms steps.

Temperature control in the model was made by modifying the electrical properties most strongly affected by temperature: the maximal conductances and the rates of channel opening and closing [77]. To add these effects to the model we changed conductances g_c and time constants τ_x, using the usual Q₁₀ based formalism: (8) (9) where the parameter p is a conductance g or a rate constant 1/τ, T_ref is the temperature where the parameters were originally fitted with the model from measurements, Q is the scaling factor, T is the temperature and Q₁₀ describes temperature sensitivity. Typical values for Q₁₀ are for rate constants and between 1.5 and 3 for conductances [49, 50, 61, 77]. Exploration was made in these parameters with steps of 0.1 from 1.1 to 3.2 to obtain the best match between experimental and computational spike traces of each HVC neuron. We assumed a value of T_ref = 20°C which is mentioned by Daou et al. [67] as “room temperature”, however values up to 25°C do not modify our results. Temperature exploration with parameter T was made between 40°C and 20°C, with the former being the designated “normal” temperature of the bird’s brain. Other explorations were made in the range 40 − 30°C, the latter being almost the lowest temperature obtained experimentally. We also changed temperature in I_Ca−L that has an explicit T dependence in Kelvin units.

Temperature induced changes: Spike shape widening, spike rate reduction and inter-spike-interval modification

We first classified the SUs by their waveform shape. In Fig 1A we can see all the mean waveforms normalized and aligned to their lowest value. We could separate them into two groups by the mean of their peak to peak width. Widths that were less than 0.5ms were classified as Fast Spikers (FS) and the longer ones as Regular Spikers (RS). Usually these shapes are related to the neuronal type, with the former classified usually as interneurons, while the latter are usually designated as excitatory neurons (Fig 1B). The first easily visualized effect found in all neurons, was the spike shape widening of the extracellular potential (Fig 1C) as temperature decreased from normal at 40°C down to almost 31°C. Single units were isolated at each measurement site by means of spike detection and clustering techniques (See Methods). Mean waveforms were obtained after aligning all the events to the minimum voltage, and spike times were used to quantify activity and inter-spike-interval (ISI) distribution characteristics. In Fig 1C we can appreciate five widening examples, three FS and two RS. The widening effect reached up to almost a 2-fold increase from the original shape for FS. To properly assess the effect for the regular spikers, we pulled out from the 18 cell measured the 5 highest firing (RS_hf) and the five lowest firing (RS_lf) in an attempt to represent HVC_X and HVC_RA respectively. The rationale for this is the following. Since the wave shapes obtained from extracellular recordings of both types of cells are indistinguishable, we took advantage of the differences in their spike rate activity. Assuming that the probability of measuring any of both cells is equally probable, taking the lowest 5 and 5 highest firing cells gives a value p< 0.05 (p = 0.048) of not having selected all from the same type. This is given by the binomial cumulative density function at a value of 5 with 18 samples. This selection will prove to be appropriate after the delicate match with the model that we show below. The widening effect was lower in RS units than in FS units, and it was more pronounced in RS_hf compared to RS_lf, as we can see in Fig 1C. The increase was of 40% and 20% respectively (Fig 1D, S2A Fig shows not normalized values).

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Fig 1. Effects of temperature on HVC neurons.

(A) Two different types of single units were recognized in terms of their spike shape. 12 Fast Spikers (FS, orange) and 18 Regular Spikers (RS, blue) (B) Classifications were made based on peak to peak wave width (p2p width). FS (orange circles) have a more variable and higher spike rate than RS (blue triangles are the five with the highest rates, and the blue rectangles the five with the lowest rates, the eight blue circles are intermediate rates cells). (C) Widening of the spike shape of normalized waveforms across temperatures for three example FS (top) and two RS (bottom). We see that RS_hf units show a bigger width change than RS_lf. Color scale shows temperature. (D) Normalized full width increase of the spike shape across temperatures. FS almost doubles the spike width, while RS_hf and RS_lf only increase ∼40% and ∼20% respectively at the lowest temperature. Points are mean ± s.e.m. (p* < 0.05, p** < 0.02 values for two tailed t-test made at each temperature) (E) Normalized spike rate decrease across temperature, where we also include Multiunits (MU). We do not observe significant differences between them (two tailed t-test p> 0.1). Points are mean ± s.e.m. (F) Patterns of inter-spike-interval (ISI) activity across temperature for the single units shown in (C), left to right corresponding to top to bottom. Three different types of histogram appear for all measured FS, where ISIs change non trivially (not only a distributional shift). The first one belongs to a FS with firing rate of 5.9Hz, measured over 8 minutes. ISI depletes at intervals higher than 20ms, but retain its lower than 10ms peak at lower temperatures. Second column is a neuron with a firing rate of 3.3 Hz measured for 4.8 min. This neuron shows no bursting behavior and the ISI shifts to the right and depletes. Third column is a neuron with firing rate of 2.4Hz measured for 6.4 min, where no changes are evident across temperatures. It lacks the second timescale from around 20ms. Last two columns show the behavior of the two RS, firing at 2.4Hz and 0.7Hz measured for 4 min. We see the evolution of a clear shift to the right of the distribution over the first temperatures, until they get depleted at lower temperatures. (G) Cumulative distribution of the ISI of the three types of fast spiking neurons. The only distributions that change significantly with respect to the one at normal temperature are FS 1 and FS 2, which have the second timescale (p* < 0.0005 Kolmogorov Smirnov test, ns: not significant, alpha value is strict to account for fewer counts at lower temperatures). We can see from FS 1 that this timescale starts to disappear at 36°C and at around 10ms of ISI. Bins are 1ms.

https://doi.org/10.1371/journal.pcbi.1005699.g001

In addition to the widening effect, we could see that the activity decreased substantially in all cases with temperature with no significant difference between cell types (Fig 1E, not normalized values in S2B Fig). However, the way that the activity changed differed for each firing pattern. In six of the FS units, most of the activity above an ISI of 10ms gradually disappeared with lower temperatures, retaining many occurrences below 10ms, as we can see in first column of Fig 1F (neuron FS 1). Other behavior observed in two FS units is depicted in the second column of Fig 1F (neuron FS 2). These neurons do not present bursts at normal temperature, and have ISIs which peak at around 20ms. As temperature decreases the distribution of ISIs depletes and shifts to the right. Finally, there were four neurons that did not present the longer timescale, and although decreasing the firing rate, they did not show changes in the ISI distributions (Fig 1F, neuron FS 3). The existence of different firing behaviors of HVC_INT (FS) was previously shown by Daou et al. [67]. The quantification of these two timescales and the disappearance of the second one is made evident with the cumulative distribution functions shown in Fig 1G. Second timescale disappears in neuron FS 1 and neuron FS 2, but not in neuron FS 3 where it was not originally present. To better quantify these timescales we did separate analyses below and above 20ms, which showed that median values in these ranges have different correlation with temperature, rising for above and decreasing for below 20ms (S2C and S2D Fig). This implies that although having an almost 3 fold reduction in firing rate in the range of temperatures studied, the timescale appearing below 10ms does not disappear (for the neurons that have it), and that the timescale around 20ms disappears with decreasing temperatures. In addition, only the neurons having the intermediate timescale showed differences with the cumulative distribution at normal temperature (S2E–S2H Fig) revealing non trivial modifications of the ISI distributions. For the two example RS units shown in Fig 1C, the evolution of the ISI distribution retains its structure, but loses it at times higher than 20ms. This is clearly seen in the third row of the last column of Fig 1F (RS lf), where the second peak in the distribution disappears. In all cases we can see that the first peak or left end of the distribution moves slightly to the right, to a higher ISI. We did not find differences in all these effects between the left and right HVC.

In what follows, we will study with the help of a computational model of the neurons in HVC, the possible explanations for the temperature effects in the spontaneous activity of its neurons.

Temperature manipulation of modeled neurons

Computational modeling of HVC neurons has proven to be successful for in vitro approaches [67]. In those studies, single compartment conductance descriptions of currents and voltages were obtained with great detail. Further work also explored non conventional complex input currents designed to activate most ionic channels and data assimilation procedures to better evaluate parameters [68]. These works show astonishing match between experiment and modeling, making them ideal to test temperature extrapolations. As mentioned above, the works done in the crab made a full exploration of the parameter space showing that a very small percentage of models will show the expected physiological behavior, but that they are nevertheless hundreds of parameter combination possibilities. This emphasizes that many models can be equivalent to explain a behavior, and that care has to be taken in restricting constraints.

Other modeling variations of HVC neurons include two compartments, soma and dendrites [66, 69], to better explain the absence of bursting from current injections in the soma of HVC_RA neurons in slices at 35°C [66]. However, dynamics of bursting within a single compartment neuron are expected to not differ from a two compartment one apart from a small delay coming from the electric couping constant between the two compartments (not more than ∼1ms). Current injections in the soma will elicit bursting since the I_K Ca dependent current mostly responsible for it is located in the same compartment as the currents responsible for spikes.

In our modeling we will use the parameters found by Daou et al. and a single compartment model. Our hypothesis is that there is a parameter range were the responses of our model are robust to changes. Therefore we selected the extrapolation parameters using constraints from previous in vivo studies. Our first step was then, starting from these previous results, to extrapolate them to in vivo brain temperatures. As is usual in most of the in vitro electrophysiological studies, temperature is mentioned as “room temperature”. This fact has consequences, like further studies in the field yielding results that differ from the previous ones, since they work at another “room temperature”. For the purposes of this work we did center our interest in dynamical regimes, rather than in absolute values, since they are expected as we showed in the previous section in the almost 10°C temperature range explored. Our studies focus in the global changes manifested and not the exact temperature at which they occur. Therefore we decided arbitrarily to assign a 20°C value to the temperature where the computational parameters were obtained. As explained in the Methods section, we modified the maximal conductances and the rates of channel opening and closing based on an already successful temperature model for neuronal activity [43, 49, 50, 61].

Search in parameter space was done with the premise that different Q10 values act on the maximal conductances and the rate variables, and the search for the proper parameters was done independently for the three neuronal types. We aimed at reproducing previous intracellular recordings that show bursting in excitatory cells and their specific voltage evolution [63, 66]. We decided to not separate each of the specific Q₁₀ values for gating and conductance variables (7 for rates and 10 for conductances), since finding a combination that reproduces the expected behavior should be robust under slight changes in the parameters [43]. Therefore, we made a two dimensional parameter search with the two Q10 parameters decoupled for the three neuronal types. (rate) and (conductance) were allowed to vary from 1.1 to 3.2 in since 1 means no effect by temperatures, and 3 is the usually biggest reported value. We explored the temperature range from T₀ = 20°C to the normal temperature of 40°C. We injected 16ms length currents with 3 varying steps. Criteria for selecting parameters was capability of bursting and number of spikes and measures of voltages between resting, maxima, minima and local minima. Finally, we assessed resemblance with voltage traces from the literature. First, criteria needed to be met at 40°C and then we checked for robustness across other temperatures.

The easiest search was made in HVC_RA, because it presents a narrow range of parameters where it presents bursting (Fig 2A). The selected value is (,) = (3.0,1.4). Second, HVC_INT (Fig 2B–2D) presents strong spiking at all parameters, and a region with strong firing and robustness across temperature was selected. Then we looked for the difference between minimum hyperpolarization after spike and resting membrane potential, as minimum difference was found in in vivo studies [63, 66]. As a last constraint, we selected a region were the peak to peak voltage was neither too high neither too low and about 60-80 mV. Values for the interneuron are (,) = (3.0,2.5). Third, we studied the behavior of HVC_X which needed further constraints to arrive to the selected values (Fig 1E–1H). Higher and more robust spiking across temperatures was found first and then we assessed interburst interval to be lower than 4ms. Peak to peak voltage inside a burst was chosen to be between 40 and 60mV, because we know it spans from about half to 75% of its 80mV peak to resting membrane amplitude. As a last measure, we evaluated the peak to peak width to be bigger than 0.6ms, as suggested by our measurements (see S2A Fig), and to be smaller than 2ms which is about the half of the interburst interval. Parameters chosen from the intersection of selected zones are (,) = (3.0,2,5). We did also explore other parameters as the resting membrane potential (S3 Fig). The only neuron to show a variability bigger than 1mV across all the parameter space and from 20 to 40°C was HVC_X. In that case, the variation in the range of experimental temperatures studied was of almost 4mV, and it increased at low temperatures. The other neuronal types also showed this increase, although it is of less than 1mV. This behavior is compatible with the measurements on hippocampal slices mentioned above.

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Fig 2. Exploration of model neurons behaviors in Q₁₀ parameter space and across temperatures.

Responses of neurons were tested with 16ms current injections at 3 different amplitudes and 20 different temperatures and the best response from the three was chosen, meaning the one eliciting the bigger number of spikes. Normal temperature (40°C) was given a bigger relevance in terms of neuronal responses. Neuronal voltage traces were also assessed for good resemblance with traces reported in vivo [63, 66]. (A) HVC_RA presents more than one spike at high and low . Black ellipse shows the only region that remains stable in a wide range of temperatures. Test currents were 480-1000-1500 nA. Selected value is (,) = (3.0,1.4) (B) HVC_INT fire strongly at all parameters. Ellipse shows a region with a high number of spikes which is more robust across temperatures. (C) Difference from minimum hyperpolarization potential after spike and resting potential of HVC_INT. Selected region shows the smallest difference, as previously reported. (D) Peak to peak Voltage for HVC_INT. Selected region shows voltages around 60 mV. Superposition of regions from B-D gave (,) = (3.0,2,5). Test currents were 250-500-1000 nA. (E) HVC_X higher spiking and more robust across temperatures is marked with an ellipse. (F) Interburst interval was selected to be lower than 4ms to be compatible with previous data. (G) Peak to peak voltage inside a burst is chosen to be between 40 and 60mV. Red zones represent spikes with no bursting, and blue zones spikes with very small bursting fluctuation. (H) Peak to peak width should be bigger than 0.6ms (see S2A Fig) and smaller than 2ms which is about the half of the interburst interval. Intersection of zones in E-H gives (,) = (3.0,2,5). Test currents were 540-1000-1500 nA. Lowest test currents for HVC_RA and HVC_X were set during a second iteration to the value that provided biggest resemblance to previously reported voltage traces.

https://doi.org/10.1371/journal.pcbi.1005699.g002

Temperature extrapolation makes excitatory neurons burst naturally

The obtained factor for rate constants was the same for the three neuronal types, and the evolution of Q with temperature can be seen in Fig 3A. It represents probability of channel opening and closing effects and is the typical value measured in the bibliography and used in models [49]. On the contrary, the temperature sensitivity factor was different for each type of neuron. This is not surprising, as it should depend on the type of channel that predominates in each neuronal type. The evolution of the Q values are shown in Fig 3B for these parameters. The predominant conductance I_A in HVC_RA is bigger compared to its I_h and the contrary happens for HVC_X, so relative values coincide with conductance measurements made in the crab [49], where values for the I_A conductance are lower than 2 and near to 3 for the I_h. Because our modeling does not discriminate between individual , we expected the relative incidence of these two conductances to dominate above the other less representative. The value of 2.5 for HVC_INT is similar to the in vivo relative current values of HVC_X. The final selection of parameters was assessed by finding the closest fit between modeled and experimental traces at in vivo temperatures (See Fig 3C). Not trivially, yet not surprisingly as was expected from the recently described dynamics of Ca²⁺-dependent K⁺ current (I_SK) in HVC_RA and HVC_X, we found bursting behavior at normal bird temperatures of 40°C for these two neuronal types. We used external applied currents I_app of durations close to 10ms, which is the typical burst duration measured in vivo.

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Fig 3. Effects of temperature on modeled neurons: Bursting emergence.

(A) Correction parameter Q with temperature sensitivity that multiplies every constant rate in the model. Processes are three times faster at 30°C than at 20°C where parameters were obtained by experiments in slices [67]. At 40°C rates are three times faster than at 30°C. (B) Maximal conductances are also changed by parameter Q. Dotted line corresponds to a , used for HVC_X and HVC_INT, and full line to which modeled HVC_RA. (C) Behavior of neurons in response to an applied current of 12ms for HVC_X and HVC_INT and 16ms for HVC_RA. At 40°C there is a remarkable similarity with intracellular measurements in vivo, where HVC_X and HVC_RA have bursting behavior (Long et al. [66]). Decreasing temperature affects the interspike interval, which widens in all cases, leading to a reduced number of spikes. At 20°C HVC_RA fails to spike. (D) Exploration of longer (30ms) and higher current inputs show bursts that terminate solely by intrinsic current properties at normal temperature for HVC_X and HVC_RA having the same duration as real neurons. The latter retains bursting up to 30°C and fails to spike at 20°C. HVC_X loses bursting offset before, at around 36°C. The short pulses have the same duration as in (A) 50ms after long pulse for HVC_X and HVC_INT and 100ms after for HVC_RA, show a spike number reduction at this short latency compared with (A). Bars beneath traces show duration of pulse.

https://doi.org/10.1371/journal.pcbi.1005699.g003

Once the temperature control variables and were set, we studied the behavior between 30°C and 40°C. We found that for the same duration and strength of current stimulations, the number of spikes per burst decreased and interspike-burst-time increased, and also interspike-interval increased for HVC_INT (Fig 3C). For room temperatures at which the original studies were performed, the bursting behavior disappeared for HVC_RA and changed drastically for HVC_X, showing only two spikes. Another interesting behavior shown was the delay onset for the first spike in HVC_RA bursts, that may have specific implications in explaining previous temperature manipulation experiments [16, 24, 25, 33] which we will discuss in the next section.

To test if continuous spiking was just a response to applied current, we used a longer stimulus of 30ms (Fig 3D). Interestingly, spiking terminated at approximately the in vivo durations found experimentally. As it was reported by Daou et al. [67] we did not find bursting behavior at the lowest temperature, and we were able to find a range between 30°C and 40°C where HVC_RA keeps its bursting offset and between 36°C and 40°C where HVC_X does. HVC_INT just decreased their interspike times. We looked for post bursting effects and applied the short current pulses described above after a brief time gap from the long pulse. The behavior of bursting neurons showed a reduced number of spikes, even with increased current. To elicit bursting we needed a 100ms refractory period for HVC_RA, while only 50ms was needed for HVC_X, which is compatible with in vivo bursting recordings. On the other hand, HVC_INT never showed bursting and interspike interval was sensitive to current values, resulting in higher spike rates at higher currents.

Single neuron model accounts for spike widening

After fixing the model parameters of temperature control, we explored single spike features of neurons. Spike width increased with temperature decrease for the three neuronal types, as can be seen in Fig 4. Computational traces show the intracellular voltage evolution of a spike that increases time duration. In Fig 4A we focus on a single spike for several temperatures for the three neuronal types for the current injected in Fig 3A. Since the computational model generates intracellular voltage evolution, to compare it correctly with the extracellular measurements (S2A Fig), we selected a width value for each neuron type corresponding to the one measured experimentally. For excitatory neurons, we searched at 40°C peak to peak threshold % to obtain a width of 0.6ms. This resulted in a 60% for HVC_RA and a 55% for HVC_X. For HVC_INT we decided to include the hyperpolarization peak which has strong influence in the extracellular potential. We then made a threshold selection in the uprise of the spike, giving a value of 30% to give a width at 40°C of 0.35 ms. Results are shown in Fig 4B, where the resemblance of the evolution of the widths across temperature is impressive. Following, we computed the relative increase in spike width compared to the one at 40°C (Fig 4C). The increase in spike width was more than 100% for HVC_INT, 35% for HVC_RA and 50% for HVC_X for the temperature range studied. At values above 31°C they have a remarkable match with the one that was measured in experimental extracellular traces (Fig 1D). Behavior of the model matches the one present in our data and is sufficient to explain the waveform widening using a single neuronal model. This means that this effect can be explained with the temperature incidence in the temporal channel subunit dynamics, which increases their characteristic time constant with the factor 1/Q (see Methods).

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Fig 4. Spike widening in single neuronal model.

(A) Intracellular voltage evolution of a single spike for the three modeled neurons across temperatures shows an increase in its width with the same current input as in Fig 3C. The time window used to plot is identical to the one used in Fig 1A and 1C for the extracellular measurements, and the positive peak is aligned to 1ms and waveforms are shifted (less than 5mV) to match at peak amplitude. (B-C) Width increase for the three neuronal types was computed for the first spike of the traces in Fig 3C. Width is calculated as the spike width above a threshold of 55 and 60% for HVC_X and HVC_RA and from the hyperpolarization to a uprising threshold of 30% for HVC_INT. These were selected to match the measured experimental width at 40°C (S2A Fig). In (C) we show the same normalized to the width at 40°C. We see that the widening effect is of more than 100% for HVC_INT and about 50 and 35% in HVC_X and HVC_RA respectively for the 10°C range explored.

https://doi.org/10.1371/journal.pcbi.1005699.g004

Single synapses explain song stretching and spike rate reduction

On the other hand, the maximal channel conductance changes that are modified with the Q factor may be responsible for other effects. Since the Hodgkin Huxley type of modeling used represents an excitable system, we expect that this maximal conductance reduction will delay the neuronal synaptic inputs to situate the postsynaptic neuron voltage above the threshold, thus producing a spike delay. To analyze this effect, we explored the voltage evolution of neurons for the current injections of Fig 3C. A detailed evolution of bursts is shown in Fig 5A, where voltage traces are aligned to the start of the stimulus. Here it is easier to recognize that the interspike interval within a burst increases when lowering the temperature. Also, for HVC_X and HVC_INT, there is no delay in the onset of firing, which changes for HVC_RA. For this reason, we made the input current to HVC_RA longer, in order to have a three spike burst across all explored temperatures. This adds a distortion effect on the last spike at low temperatures, but does not alter the results. We can see that there is close to a 5ms delay in the onset of firing for the same current applied when temperature is decreased 10°C. These can directly affect the firing pattern of neurons targeted in RA, which in turn innervate the brainstem nuclei (nXII for syringeal muscles and Ram for respiratory muscles) in charge of sending output motor activities to elicit song, or sending back further synapses through a bottom up recurrent network. Although the type of input we are providing is not physiological, it proves that temperature is capable of changing an important timing property of at least one neuronal type, HVC_RA.

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Fig 5. Temperature effects from a single synaptic input produce delays.

(A) Voltage traces for the three neuronal types in red, and synaptic input in blue across different temperatures are aligned to 0ms at current injection start. Single bursts can be seen for the excitatory types and a similar number of spikes for the inhibitory HVC_INT. All neurons present interspike interval increases and HVC_RA also shows a spike onset delay with decreasing temperatures. Vertical line is a reference aligned to the peak of the first spike in each trace. (B) Current inputs in blue from a single synaptic model fed with the voltage traces of HVC_X and HVC_RA. Evolution of HVC_INT voltage in red shows a single spike elicited after the third synaptic current peak. Latencies increase when lowering temperature. Time origin and vertical line are the same as in A. (C) Delay for spike onset for the five traces in A and B relative to the timing of the first spike at normal temperature. HVC_RA and HVC_INT only have slight delays for the constant applied current, while HVC_RA displays an almost 5ms delay to burst onset at 30°C. For the synaptic model HVC_INT has delays that go up to 6ms and 12ms for the two excitatory input types. (D) Maximum absolute synaptic current elicited decreases in a similar fashion for the two excitatory inputs and decreases more than twofold in the range of temperatures explored. The change in slope of the HVC_RA to HVC_INT curve at around 34°C is due to the slight distortion of last spike in HVC_RA. (E) Spiking pattern of HVC_INT at 40°C for decreasing current at steps of -200pA shows the characteristic sensitivity of the inhibitory interneurons to input currents. (F) Interspike interval (ISI) of HVC_INT for different currents and temperatures. We see that it is more sensitive to currents than temperatures. Inset panels show that at 1000 and 2000pA ISI changes less than 0.3ms for different temperatures, and shows changes above 1ms only below 500pA. Currents were generated with 25ms pulses decreasing at -100pA steps in a single simulation with 200ms intervals between pulses. ISI was computed only where there was at least two spikes, which happened below -300pA.

https://doi.org/10.1371/journal.pcbi.1005699.g005

For the purposes of this work we will concentrate on the HVC_INT temperature changes, since there is still much debate on the connectivity of HVC projecting neurons, as mentioned above in the Introduction, and because it will restrict the connectivity scenarios we can use to assess the changes in their ISI distributions, as we explain in the next section. Therefore, we used the voltage profile of the excitatory neurons HVC_RA and HVC_X to provide a more realistic input to HVC_INT. Using a simple model of synaptic current also modified with temperature (see Methods), we assessed the evolution of the HVC_INT neurons. The parameter we had to explore in this single synapse model was the maximal conductance g_syn (see Eq 6). To match physiological behavior, we tried to reproduce the recently measured in vivo-like activity shown by Kosche et al. [28] of interneurons in HVC while measuring and eliciting the excitatory input activity in HVC_RA neurons. The authors showed that reliable spiking occurs in interneurons when HVC_RA neurons fire three consecutive spikes at 100-300Hz, at a room temperature of 23°C. The single interneuron spike occurs after the third spike in the “artificially elicited burst”, and shows a short delay.

Once the conductance parameter was fixed, we computed the evolution of an interneuron voltage following the different excitatory inputs of Fig 5A. The full current and the voltage evolution are shown in Fig 5B for both cases. We can notice that the interneuron spike has a much wider delay relative to the position of the first spike in the excitatory bursts (vertical line). This is due to the accumulation of time resulting from the widening of spikes and interspike intervals, and from the effect of synaptic delays. The delays relative to the first spike in each condition in Fig 5A and 5B are depicted in Fig 5C. Hamaguchi et al. have recently measured in HVC at 32°C an increase in excitatory neuron delays to spiking onset of almost 2.8ms compared to 40°C, using presynaptic neuronal stimulations [33]. This agrees with the HVC_RA delay shown here.

Following the analysis, the maximal postsynaptic currents in this model were computed, and in Fig 5D we can see that they decrease from the original absolute values of around 600-700pA to close to 300pA for the two excitatory inputs on HVC_INT. This remarkable change can help us explain the decrease in spontaneous activity that we witnessed in our experiment. For this, we explored the response capabilities of the HVC_INT to different input currents at 40°C, as we see in Fig 5E. Here we applied currents starting from -100pA down to -800pA with steps of 100pA. We see that the modified Hodgkin-Huxley model used for this neuron is particularly sensitive to the current applied, showing an increasing spiking frequency, and can be classified as a class 2 excitable system, since it starts spiking at low frequencies with small currents. To see the incidence of temperature and current in the ISI interval elicited in the HVC_INT we constructed Fig 5F. There, we explored the full temperature range, and currents from 300pA to 2300pA. The first interesting result is that the ISI has almost no sensitivity to temperature when high currents are applied. Only below 500pA we can see a change higher than 1ms for the whole temperature range. It follows that temperature starts to play a role in ISI behavior at low input currents, which happens below 36°C, as we know from Fig 5D. For the lower current of 300pA, the ISI can change up to 5ms. In conclusion, there are two parallel effects acting on the synaptic model: one is the decrease in the maximal conductance with temperature, and the other is the sensitivity to input current of the HVC_INT. These two effects may act together in reducing the spiking of these neurons at lower temperatures. Nevertheless, we cannot yet explain the different shapes in the ISI histograms of the experiment, for which we refer to the next section.

Realistic synaptic input is needed to explain ISI distribution changes

To account for different neuronal activity characteristics shown by HVC_INT, we decided to explore realistic synaptic inputs. A priori, we cannot rule out the possibility that the HVC_INT have subpopulations with dissimilar channel conductance properties, however, the existence of substantial diversity has not been reported in the interneurons measured previously [67]. Following this line, and to simplify our exploration, we expanded the single synaptic model to multiple excitatory inputs on a single interneuron. We based our analyses in the synaptic connectivity description of HVC microcircuitry where it is known that HVC_INT only receive input from HVC_RA and HVC_X [64]. We connected synaptically excitatory neurons to a single interneuron whose firing pattern is under study. The hypothesis we want to test is that only with Poissonian inputs into the interneurons we can account for the changes observed, ruling out any connectivity structure, at least during the non-singing anaesthetized state. We fixed the spike rate of excitatory neurons as reported previously at 0.6 Hz and 1.5 Hz for HVC_RA and HVC_X respectively [63]. Each input consists of a presynaptic voltage pattern eliciting a synaptic current, as shown in the previous section (Fig 6A–6C). We used the patterns of Fig 5A and made two new ones, reducing the stimulus pulse, which have a single spike or a burst with two spikes (Fig 6A–6C, waveform shapes). These are intended to mimic real input for canaries that reported that about 9% of a bursting cell’s activity corresponds effectively to bursts and that they average around 2.7 spikes per burst. We therefore used for each synaptic input a fixed ratio between number of spikes. Events with one spike ranged from 70% to 100% in 10% steps, and the remaining were assigned 2 or 3 spikes per burst, retaining the desired overall spike rate. Conductance parameters were the same as the ones studied in the section above, which means that single spike events cannot elicit a spike in the interneuron. In order to be more realistic, we added noise following Eq (9). In addition to the temperature change of all parameters in the computational model, we changed the synaptic input spike rate of the excitatory neurons following the decrease shown in Fig 1E, which is of 5.6%/°C.

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Fig 6. Changes in model ISI distributions from poisson excitable inputs.

(A-C) Different input combinations (top) and evolution of the intracellular potential for HVC_INT (red) and synaptic current elicited (blue) for 40°C (middle) and 32°C (bottom). Many presynaptic inputs do not elicit spikes and spiking can happen in a “burst” like manner. (A) Input of 20 HVC_RA neurons and 60 HVC_X neurons. Firing rate at 40°C is 5.6 Hz, matching FS 1 unit in Fig 1F. To achieve this behavior 30% of the input was made of bursts of 3 spikes, while the rest consisted of single spike events. (B) Input of 80 HVC_X neurons produces a firing rate at 40°C of 2.9 Hz with no “bursting” events, matching closely FS 2 unit in Fig 1F. To achieve this behavior, 100% of the input was made of single spikes. (C) Input of 60 HVC_RA neurons and 20 HVC_X neurons. Firing rate at 40° is 2.4 Hz, matching FS 3 from Fig 1F. 10% of the input was made of 3 spike bursts, 20% of 2 spike bursts while the rest consisted of a single spike. (D) ISI distributions across temperatures for connectivity in A shows a big peak at times below 10ms. (E)ISI distributions across temperatures for neuron in B shows the absence of the peak below 10ms. (F) ISI distributions across temperatures for connectivity in C shows a big peak at times below 10ms. (G-I) Cumulative distribution of the ISI of the three connectivities. The distributions with the peak below 10ms change significantly with respect to the one at normal temperature (p* < 0.0005 Kolmogorov Smirnov test, ns: not significant, alpha value is strict to account for fewer counts at lower temperatures). This is due to the slight shift to the right of this peak at lower temperatures. (J) Evolution across temperatures of the spike rate of the simulated neurons explored for different balances of HVC_RA neurons and HVC_X neurons, with a fixed total of 80. Percentage of single spike input varied in steps of 10% from 70% and double and triple spike bursts also varied in 10% steps. (K) Evolution of spike rate, but with the input rates of the excitatory neurons inversed. (L) Evolution of spike rate, with synaptic inputs stronger in 0.5nS. Data points are mean values and bars are s.e.m. ISI distributions are normalized for each temperature which is color coded in °C. Simulations were made of a duration of 4 minutes.

https://doi.org/10.1371/journal.pcbi.1005699.g006

We explored at normal temperature the number of inputs necessary to keep the spike rate of the interneurons between 2 and 8Hz (measured experimentally). It resulted with the parameters used that we needed 80 excitatory units. Then we allowed the number of different excitatory types to vary in the range to see if different input characteristics can shape the quality of the response. In Fig 6A–6C we show an arrangement and the typical traces of intracellular potential showed by the HVC_INT and its corresponding synaptic current, for three different arrangements (top) of input neurons at 40°C (middle) and at 32°C (bottom). We can see the intracellular excitatory post synaptic events in the trace and that some events get grouped in three and two spike “burst”. This “bursting” behavior is due to the way that we fixed the synaptic input in the previous section and to the intrinsic characteristics of the interneuron. Consequently, when a presynaptic event is a burst, there is a higher chance that another input from another neuron near it will elicit more spikes. This can be seen in Fig 6D where there is a dense occurrence below 10ms. This type of pattern is reminiscent of FS 1 unit described in Fig 1F.

On the other hand, when we take out the bursting input type, we cannot find the “burst” arrangement in the intracellular trace (top of Fig 6B) and we recover a pattern without the first peak in the ISI distribution, as shown in Fig 6E. In Fig 6C we show the trace and in Fig 6F the ISI distribution of another neuron corresponding to a different balance of HVC_RA and HVC_X input events. In all three cases, what determines the type of ISI distribution is not the type of input neuron, but the balance between the number of 3-, 2- 1-spike inputs. We found that the only possible way to avoid the grouping of events below 10ms was by preventing bursting input to the neuron. On the other hand, once the balance was established, changing the ratio of excitatory input neuron types only changed the spike rate, without changing qualitatively the ISI distributions.

In Fig 6G–6I we show the quantification of the distributions by means of their cumulative density function. In Fig 6G we see that although the structure below 10ms is retained, significant differences exist between low temperatures and the normal temperature. This is due probably to the very deterministic nature of the bursting input imposed into the cell that makes the interneuron interspike interval to increase as the interburst interval of the input does (S4A Fig). More explorations using a bigger percentage of bursting events made this differences disappear and the distribution under 10ms to be slightly broader. However, when studying the firing rate decrease across temperatures for these inputs with more bursting input, it was not possible to match the firing rate decrease found in the experiment. In Fig 6H we see a resemblance to FS 2 type from Fig 1F in that the slope at small timescales of the distribution decreases at lower temperatures, but in this case no significant differences were found with respect to 40°C at times above 20ms (S4B Fig). In Fig 6I we show a third combination of inputs that matches FS 3 behavior. Again in this situation as in Fig 6G, significant differences are found for smaller ISI values (S4C Fig).

Then we studied the evolution across temperature of the normalized spike rate for all the measured temperatures and balance of neurons and spiking inputs. We can see in Fig 6J (in S4D Fig we show not normalized values) that the decreasing tendency which better resembles the measured data (Fig 1E) is the one corresponding to all the inputs as HVC_X. We have imposed a lower spiking rate on HVC_RA relative to the one of HVC_X, which in the simulations with a higher ratio of HVC_RA neurons led to a firing rate that was at the lower end of the range of the ones measured experimentally. We conclude that for the physiological range of firing rates we simulated, their decrease can be explained by the amount of the stochastic input received. Then we asked if it was more important the specific type of input that a cell provides or if it was the firing rate at which it excites the interneuron. For that purpose, we inverted the firing rate between HVC_RA and HVC_X inputs and rerun the simulations. The result can be seen in Fig 6K (S4E Fig), and we obtained an exact inversion of the original result, meaning that it is more important the rate of the input than its detailed characteristics. The only difference was the case where all 80 inputs were HVC_X, in this scenario at a low firing rate of 0.6Hz, where the firing rate of the interneuron increases at low temperatures. This can be explained by the fact that HVC_X input was slightly less strong than the one from HVC_RA and that at low firing rates it cannot elicit spiking easily, whereas at low temperatures it shows a bigger amplitude of spike and HVC_INT a slightly bigger membrane resting potential (S4I Fig). Nevertheless, firing rate of the interneuron in that case was far below any of the values measured experimentally (Fig 1B). We also studied what happens if the synaptic inputs are stronger (+0.5nS for each synaptic input) and found that no matter all the combinations that we explored, the interneuron does not reach the rate decrease observed experimentally (Fig 1L and S4F Fig). Finally, we took out the noise present and rerun again the simulations, and we saw that results do not change regarding the firing rate (S4G and S4H Fig). The sole difference was that the spread of the ISI distribution was narrower.