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. 2015 Oct 2;593(22):4979–4994. doi: 10.1113/JP270876

Functional characterization of spikelet activity in the primary visual cortex

Benjamin Scholl 1, Sari Andoni 1, Nicholas J Priebe 1,✉
PMCID: PMC4650405  PMID: 26332436

Abstract

Key points

  • In vivo whole‐cell patch‐clamp recordings in cat visual cortex revealed small deflections in the membrane potential of neurons, termed spikelets.

  • Spikelet statistics and functional properties suggest these deflections originate from a single, nearby cell.

  • Spikelets shared a number sensory selectivities with the principal neuron including orientation selectivity, receptive field location and eye preference.

  • Principal neurons and spikelets did not, however, generally share preferences for depth (binocular disparity).

  • Cross‐correlation of spikelet activity and membrane potential revealed direct effects on the membrane potential of some principal neurons, suggesting that these cells were synaptically coupled or received common input from the cortical network.

  • Other spikelet–neuron pairs revealed indirect effects, likely to be the result of correlated network events.

Abstract

Intracellular recordings in the neocortex reveal not only the membrane potential of neurons, but small unipolar or bipolar deflections that are termed spikelets. Spikelets have been proposed to originate from various sources, including active dendritic mechanisms, gap junctions and extracellular signals. Here we examined the functional characteristics of spikelets measured in neurons from cat primary visual cortex in vivo. Spiking statistics and our functional characterization of spikelet activity indicate that spikelets originate from a separate, nearby cell. Spikelet kinetics and lack of a direct effect on spikelet activity from hyperpolarizing current injection suggest they do not arise from electrical coupling to the principal neuron being recorded. Spikelets exhibited matched orientation tuning preference and ocular dominance to the principal neuron. In contrast, binocular disparity preferences of spikelets and the principal neuron were unrelated. Finally, we examined the impact of spikelets on the principal neuron's membrane potential; we did observe some records for which spikelets were correlated with the membrane potential of the principal neuron, suggesting that these neurons were synaptically coupled or received common input from the cortical network.

Abbreviations

DSI

disparity selectivity index

Fo

mean Fourier amplitude

F1

first Fourier harmonic amplitude

FWHM

full‐width half‐maximum

ISI

inter‐spikelet interval

ODI

ocular dominance index

RF

receptive field

STA

spike‐triggered average

V1

primary visual cortex

VTA

voltage‐triggered average

Introduction

Action potentials are the primary currency used by neurons in the central nervous system to transmit information. Intracellular recordings have been an essential technique for elucidation of the subthreshold membrane potential fluctuations that lead to action potentials (Pel et al. 1991; Ferster & Jagadeesh, 1992) but also reveal the existence of smaller, short membrane potential deflections, called ‘spikelets’, – small unipolar or bipolar waveforms (Spencer & Kandel, 1961; Margrie et al. 2003). Spikelets are distinct from the larger action potentials because they are only detectable in subthreshold records, are less than 10 mV in amplitude, and do not depend directly on the somatic membrane potential. Spikelets could emerge from three distinct sources. One class of spikelets originates from dendritic mechanisms within the recorded neuron. Such spikelets are reported to be unipolar events and are hypothesized to be caused by voltage‐gated channels (Llinas & Nicholson, 1971; Golding & Spruston, 1998; Epsztein et al. 2010; Smith et al. 2013). A second class of spikelets originates from the direct electrical coupling between separate, but nearby, neurons. This coupling is mediated through gap junctions, membrane pores connecting the cytoplasm of two individual cells (Dermietzel & Spray, 1993). Gap junctions attenuate membrane depolarizations and action potentials from the nearby cell, producing spikelets in the principal neuron, which we define as the patched neuron (Spencer & Kandel, 1961; MacVicar & Dudek, 1981; Taylor & Dudek, 1982; Vigmond et al. 1997; Gibson et al. 1999). These spikelets are observed in the cerebral cortex (Gibson et al. 1999; Tamás et al. 2000; Margrie et al. 2003), thalamic reticular nucleus (Landisman et al. 2002; Landisman & Connors, 2005) and hippocampal interneuron network (MacVicar & Dudek, 1981; Taylor & Dudek, 1982; Vigmond et al. 1997). Finally, spikelets may arise from action potentials produced by nearby neurons, recorded extracellularly through the intracellular pipette (Martinez et al. 2014).

We used whole‐cell patch‐clamp recordings to measure spikelet responses in cortical neurons of cat primary visual cortex (V1). We observed a variety of spikelet shapes and found their waveforms were stable throughout the recording period. Spiking statistics, the brief kinetics of spikelets, and a lack of effect by polarization of the patched cell suggest these spikelets originate from a separate, nearby neuron recorded extracellularly. We demonstrate that spikelets in cat V1 neurons share similar response preferences to visual stimuli as the patched neuron – the principal neuron – but often have distinctly different receptive field properties. Orientation tuning, measured from drifting sinusoidal gratings, is similar between spikelets and membrane potential of the principal neuron. The eye preferences of spikelet and principal cell spiking responses are also matched, but disparity preferences are unmatched. Finally, we measured the impact of spikelets on membrane potential and observed that a subset of spikelets had a direct effect on the membrane potential of the principal neuron, suggesting synaptic connectivity or common synaptic input to both neurons.

Methods

Physiology

Experiments were performed as previously described using female and male cats (n = 14, 2–5 kg) that were anaesthetized and subject to neuromuscular blockade (Scholl et al. 2013). Anaesthesia was induced with ketamine (5–15 mg kg−1) and acepromazine (0.7 mg kg−1), followed by intravenous administration of a mixture of propofol and sufentanil (Yu & Ferster, 2010). Once a tracheotomy was performed, the animal was placed in a stereotactic frame for the duration of the experiment. Recording stability was increased by suspending the thoracic vertebrae from the stereotactic frame and performing a pneumothoracotomy. Eye drift was minimized with intravenous infusion of vecuronium bromide (0.2 mg kg h−1). Anaesthesia was maintained during the course of the experiment with continuous infusion of propofol and sufentanil (6–9 mg kg h−1 and 1–1.5 μg kg h−1, respectively). Body temperature (38.3°C), electrocardiogram, electroencephalogram, CO2, blood pressure and autonomic signs were continuously monitored and maintained. Following neuromuscular blockade, electrocardiogram, electroencephalogram, and CO2 were carefully monitored to maintain a sufficient depth of anaesthesia during the course of the experiment. The nictitating membranes were retracted using phenylephrine hydrochloride, and the pupils were dilated using topical atropine. Contact lenses were inserted to protect the corneas. Supplementary lenses were selected by direct ophthalmoscopy to focus the display screen onto the retina. Experiments were terminated by euthanizing the animal with an overdose of pentobarbital (100 mg kg−1). All procedures were approved by The University of Texas at Austin Institutional Animal Care and Use Committee.

Whole‐cell recordings

Blind whole‐cell recordings were obtained in vivo (Pel et al. 1991; Ferster & Jagadeesh, 1992; Margrie et al. 2003). As a reference electrode, a silver–silver chloride wire was inserted into muscle near the base of the skull, and covered with 4% agarose in normal saline to reduce changes in the surrounding fluid and concomitant changes in associated junction potentials. The potential of the cerebral spinal fluid was assumed to be uniform and equal to that of the reference electrode. Pipettes (7–10 MΩ) were pulled from 1.2 mm outer diameter, 0.7 mm inner diameter KG‐33 borosilicate glass capillaries (King Precision Glass) on a P‐2000 micropipette puller (Sutter Instruments) to record from neurons 250–850 μm below the cortical surface. To record membrane potential and spike responses, pipettes were filled with (in mm) 135 potassium gluconate, 4 sodium chloride, 0.5 EGTA, 2 magnesium‐ATP, 10 phosphocreatine disodium, and 10 Hepes, pH adjusted to 7.3 with potassium hydroxide (Sigma‐Aldrich). Current clamp recordings were performed with a MultiClamp 700B patch clamp amplifier (Molecular Devices, Sunnyvale, CA, USA). Current flow out of the amplifier into the patch pipette was considered positive. Average resting membrane potential, input resistance, membrane time constant, and recording duration from whole‐cell records with spikelets are shown in Table 1. Acceptable whole‐cell recordings were required to have series resistances less than 120 MΩ (mean = 63.8 MΩ, SD = 25.9 MΩ), a baseline resting membrane potential less than −50 mV, and a stable baseline resting membrane potential for at least 15 min. No junction potential correction was made for these records. Records were digitized at 15 kHz or higher and saved to disk for offline analysis.

Table 1.

Average resting membrane potential, input resistance, membrane time constant and recording duration from whole‐cell records with spikelets

Resting Input Time Expt. Principal AP Principal AP Spikelet full Spikelet
membrane resistance constant duration amplitude minimum ISI amplitude minimum
potential (mV) (mΩ) (ms) (min) (mV) (ms) (mV) ISI (ms)
−67.5 ± 7.1 (20) 86.7 ± 47.4 (12) 15.0 ± 4.6 (12) 59 ± 41 (n = 20) 35.0 ± 14.8 (18) 3.2 ± 1.2 (18) 4.6 ± 2.0 (20) 3.3 ±1.6 (18)
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Values are means ± SD (n). The input resistance was measured in all neurons, but we only recorded data for 12 neurons. Only records with an inter‐spikelet interval (ISI) of 10 ms or less are reported. Two additional records had a minimum ISI greater than 50 ms.

Stimuli

Visual stimuli were generated by a Macintosh computer (Apple) using the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) for Matlab (The Mathworks, Natick, MA, USA) and presented dichoptically using two Sony video monitors (GDM‐F520) placed 50 cm from the animal's eyes. The video monitors had a non‐interlaced refresh rate of 100 Hz and a spatial resolution of 1024 × 768 pixels, which subtended 40 × 30 cm (44 × 34 deg). The video monitors had a mean luminance of 40 cd cm−2. Drifting grating stimuli were presented for 4 s, preceded and followed by 250 ms blank (mean luminance) periods. Spontaneous activity was measured with blank periods interleaved with drifting grating stimuli of the same duration. We characterized stimulus orientation, spatial frequency (0.20–1.0 cycles deg−1), spatial location, size (0.5–2 deg diameter), and eye preference best evoking a response. Upon isolating a neuron, stimulus parameters were coarsely mapped manually and then fine‐tuned after systematic measurements of orientation and spatial selectivity. Binocular stimuli were presented dichoptically using the preferred stimulus parameters at 2–4 Hz temporal frequency and 90% contrast. A mirror was placed directly in front of the contralateral eye to reflect receptive field locations onto a separate monitor. The angle and location of the mirror was adjusted to avoid occlusion of the field of view for the ipsilateral eye. To measure binocular interactions we systematically changed the spatial phase of one grating while holding the spatial phase of the other grating constant (Ohzawa & Freeman 1986 a,b). Relative phase disparities used ranged from −135 to 180 deg. All binocular and monocular stimuli were presented during the same block and pseudo‐randomly interleaved. One‐dimensional noise sequences were presented to measure linear and nonlinear receptive field components (Mohanty et al. 2012).

Analysis

To compare estimates of subthreshold membrane potential and suprathreshold spikes, raw records were low‐pass filtered with a cutoff at 100 Hz to remove spikes. Spikes and spikelets were identified on the basis of their sharp deflections in membrane potential, as measured with the first and second derivative. Spike and spikelet times were separated based on peak membrane potential deflection and waveform amplitude. Spikelet waveform amplitudes were required to be less than 10 mV, as defined empirically (see Fig. 1). Principal cell action potentials are distinct in this sense because they are initiated at membrane potential threshold. Spikelet times within 25 ms of a spike time were excluded. Membrane potential, spike and spikelet responses for each stimulus were cycle‐averaged across trials, following removal of the first cycle. The Fourier transform was used to calculate the mean (F 0) and modulation amplitude (F 1) of each cycle‐averaged response. Simple and complex cells were separated by computing the modulation ratio (F 1/F 0) for spiking responses to the preferred monocular stimulus; neurons with modulation ratios larger than 1 are considered simple. Peak responses were defined as the sum of the mean and modulation (F 0 + F 1). All peak responses are reported after subtraction of the mean spontaneous activity. Mean spontaneous activity for spiking activity and membrane potential fluctuations were measured during blank (mean luminance) periods. Error bars represent SEM unless otherwise indicated. Double Gaussian equation was used to fit orientation tuning data and define orientation tuning preference:

R(θ)=αe−(θ−θ pref )/(2σ2)+βe−(θ−θ pref +π)/(2σ2)+ spont

Figure 1. Spiking statistics of spikelets and principal cell action potentials .

Figure 1

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A, example waveforms of each spike type. B, distribution of full spikelet amplitude across cells. C, plot of spikelet and principal cell action potential amplitude across cells. D, distribution of amplitudes for depolarization and hyperpolarization phase of each spikelet waveform. E, same as in B for full‐width half‐maximum (FWHM). F, same as in C for FWHM. G, average spikelet waveforms at the beginning of each recording session (dotted line) and towards the end (continuous line). Each average is composed of 10–15 spikelet waveforms. Elapsed time between each average indicated for each record.

Here R(ϴ) is the response of the neuron to different orientations (ϴ), σ is the width of the tuning curve, spont is the mean spontaneous activity, α and β are peak amplitudes, and ϴpref is the orientation preference. The ocular dominance index was defined by (Scholl et al. 2013):

ODI =R contra −R ispi R contra +R ispi

Here R is the response of the neuron to each monocular visual stimulus. Binocular disparity tuning curves were fitted to a cosine function to determine phase preference:

R(φ)=α2ei(φ−φ pref )−e−i(φ−φ pref )+ spont

Here R(ɸ) is the response of the neuron to binocular phase differences (ɸ), spont is the mean spontaneous activity, α is the peak amplitudes, and ɸpref is the binocular phase difference preference.

Spatio‐temporal receptive fields were extracted from the membrane potential or spikelet responses to 1D noise stimuli (Park et al., 2013). Membrane potential and spikelet rate were binned at the frame rate of the stimulus, either 100 or 120 Hz, and the triggered average was computed by measuring μ:

μ=1N∑isiri

where μ is the spikelet‐ or voltage‐triggered average (STA and VTA, respectively), si is the stimulus at frame i, and ri is the spiking or subthreshold response to that frame. After projecting out the VTA or STA, the covariance matrix was constructed as the following:

C=1N−1∑i(si−μ)(si−μ)T

The covariance matrix was decomposed into its eigenvectors, and their significance was determined based on a bootstrap method of their eigenvalues.

Spikelet‐triggered membrane potential averages were computed by adding raw, unfiltered membrane potential at spikelet spike times. During bursting events, only the final spikelet time was used. Spikelet times occurring near principal cell action potentials were not used (± 5 ms). Shuffle corrections were made by shuffling trials of membrane potential responses for a given set of spikelet spike times. Mean shuffle corrections were subtracted from mean spikelet‐triggered averages.

The similarity between spikelet orientation or disparity preference with the principal cell was quantified by computing a circular‐correlation coefficient (Batschelet, 1981):

corr =1N∑cos(ψn−ζn)2+∑sin(ψn−ζn)2

Here ѱ is the individual spikelet preference, ζ is the principal cell preference, and N is the total number of neurons (n). The standard error was computed by bootstrapping and sampling with replacement (Sokal & Rohlf, 1995).

Results

To study the functional characteristics of spikelet activity we obtained whole‐cell patch‐clamp recordings in vivo from neurons in cat V1. In 76 intracellular records obtained from 14 cats we observed spikelet activity in 21 neurons (number of neurons per animal: 1.5 ± 0.94, mean ± SD, proportion of neurons with spikelets per animal: 0.24 ± 0.07, geometric mean ± SD). For 20 of those records we obtained enough data to extract functional responses of both the principal neuron and the spikelet. Here we define the principal neuron as the one patched. Records were considered acceptable based on their resting membrane potential, stability, evidence of elicited action potentials, and membrane biophysical properties (Table 1, see Methods). The average measured membrane time constant was 15 ± 4.6 ms (range = 6.3–23.1 ms).

Spiking statistics and biophysical properties of spikelets

Spikelet waveforms were unequivocal deflections embedded in membrane potential with distinct size and duration from action potentials elicited by the principal neuron (Fig. 1 A). For the example record shown in Fig. 1 A, the spikelet amplitude was 4.5 mV, whereas the principal neuron action potential amplitude was 45 mV. Across our sample population, principal neuron action potentials were larger than the full spikelet amplitude (Fig. 1 B and C; mean spikelet amplitude = 4.6 ± 2.0 mV, SD, n = 20, mean action potential amplitude = 35.0 ± 14.7 mV, SD, n = 18). Spikelet waveforms exhibited diversity with different amplitudes of depolarizing and hyperpolarizing phases (Fig. 1 G). In our sample population we consistently observed a dominant depolarizing phase, but a wide variety of hyperpolarizing phases (Fig. 1 D; also compare waveforms in Fig. 1 G; mean spikelet positive phase = 3.2 ± 1.4 mV, SD; mean spikelet negative phase = −1.4 ± 0.8 mV, SD, n = 20). The dominant depolarization phase was also evident in the ratio of phase amplitudes (depolarized/hyperpolarized) (geometric mean = 2.4, n = 24). The duration of a spikelet was very short, with a width less than 1 ms at half‐maximum amplitude (Fig. 1 E and F; mean spikelet full‐width half‐maximum (FWHM) = 0.26 ± 0.11 ms, SD, n = 20; mean action potential FWHM = 1.19 ± 0.73 ms, SD, n = 18). Spikelets also had a stereotyped time course and amplitude throughout the duration of each recording, lasting upwards of 100 min (Fig. 1 G).

The fidelity of spikelet waveforms suggests that in each recording spikelets had a common source, similar to the extracellular waveforms from a single unit. If spikelets do indeed originate from a common neuronal source there should be a spikelet refractory period. To determine whether a spikelet refractory period exists, we measured the distribution of inter‐spikelet intervals (ISIs) (Fig. 2 A, left) and identified the minimum ISI or absolute refractory period for each recording (Fig. 2 A, circle). We found that for all spikelets examined, there existed an absolute refractory period lasting between 1.5 and 10 ms (Fig. 2 B, ordinate). For records containing enough action potentials to generate an ISI distribution, we also identified the absolute refractory period for comparison (Fig. 2 B). The absolute refractory periods for principal cell action potentials and their spikelet counterparts were comparable (3.2 ± 1.2 ms and 3.3 ± 1.6 ms, respectively). There also exists a relative refractory period in which spiking is less likely than expected from a Poisson distribution, as indicated by the non‐exponential distribution of ISIs (Fig. 2 A) (Berry & Meister, 1998). Another characteristic of action potentials from a single neuron is a decline in spike amplitude in spike pairs separated by short ISIs or the absolute refractory period, attributed to inactivated sodium channels. For short ISIs, we examined the amplitude of the spikelet immediately following a previous spikelet with less than a 2.5 ms delay. In those records we observed a small decrease in amplitude in the second spikelet (mean amplitude difference = −0.15 ± 0.31 mV, mean percentage decrease = 5.0 ± 5.7%, n = 7), but this was not significant (P = 0.14, Wilcoxon's signed‐rank test). The fast kinetics of spikelet waveforms, a hyperpolarization phase (Fig. 1), and slight decrease in spikelet amplitude during quick bursts are characteristics of spikelets being sourced from a single neuron.

Figure 2. Absolute refractory period of spikelets and principal cell action potentials .

Figure 2

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A, inter‐spikelet interval (ISI) distribution for an example cell. Minimum ISI (absolute refractory period) indicated by circle. Note that the minimum ordinate value is 1. B, distributions of minimum ISI for principal cell action potentials (abscissa) and spikelet (ordinate) from each recording.

Spikelets from a separate, nearby neuron could be recorded through either a passive mechanism, such as gap junctions (MacVicar & Dudek, 1981; Taylor & Dudek, 1982; Vigmond et al. 1997; Gibson et al. 1999) or as an independent extracellular signal (Martinez et al. 2014). To distinguish between these possibilities, we injected pulses of hyperpolarizing current (−100 to −240 pA, 50–200 ms) into the patched neuron to determine if such hyperpolarization affects the spontaneous spikelet activity (Fig. 3 A). Hyperpolarizing current in the patched cell caused large negative deflections in recorded membrane potential, and during periods without current injection, spontaneous membrane potential fluctuations occasionally eliciting action potentials were observed (Fig. 3 A, inset). Spikelets were evident both during hyperpolarizing epochs and during rest periods (Fig. 3 A, inset, asterisk). We then quantified spikelet rate during hyperpolarization compared to the resting membrane potential. In a subpopulation of recordings with hyperpolarizing current injections (n = 11, Fig. 3 B) we observed no change in spikelet activity (hyperpolarized mean rate = 3.5 ± 2.7 spk s−1, V rest mean rate = 2.7 ± 2.2 spk s−1; P = 0.92, Wilcoxon's signed‐rank test). One potential caveat to these data is that the gap junction resistance may be very high. Hyperpolarizing the principal cell would then only weakly alter the membrane potential of the other neuron. While such gap‐junction gated hyperpolarization might be weak, even weak hyperpolarization of the coupled neuron should dramatically alter the spikelet rate given the known nonlinear relationship between membrane potential and spike rate (Carandini & Ferster, 2000; Priebe et al. 2004). Altogether, the fast kinetics, refractory period, stereotyped waveform, and lack of effect from current injection suggest that spikelets originate from a separate cell recorded extracellularly.

Figure 3. Spikelet activity unaffected by hyperpolarization of principal cell .

Figure 3

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A, example recording depicting principal cell action potentials, membrane potential, and negative current injection to hyperpolarize membrane potential. Current pulses illustrated below trace (50 ms duration, −210 pA amplitude). Spikelets were evident both at rest and when membrane was hyperpolarized (shaded region, inset at right). Pipette access resistance was not bridge‐balanced in this record. The membrane input resistance for this example cell was 61 mΩ. Asterisks indicate presence of a spikelet. B, average spikelet activity and standard error at rest and during hyperpolarization for each recording.

Functional characterization of spikelets

We characterized the functional response properties of spikelets and the corresponding principal neuron by presenting drifting gratings in the principal neuron's receptive field. In V1, both membrane potential and spiking responses of simple cells are strongly modulated by drifting gratings of preferred orientation, spatial frequency and ocular preference (Movshon & Thompson, 1978). Spikelet records could also be modulated by drifting gratings (Fig. 4 A). To quantify the degree of response modulation for these records, we measured the modulation ratio, defined as the Fourier amplitude at stimulus temporal frequency divided by the mean (see Methods) (Skottun et al. 1991; Priebe et al. 2004). Simple cells, due to strong modulation by a grating's temporal frequency, have a ratio greater than 1, while complex cells, which have unmodulated responses, have a ratio less than 1. In this example both the principal neuron spikes and spikelets had modulation ratios greater than 1 (F 1/F 0 = 1.80 and 1.54, respectively; Fig. 4 A and B) indicating that both are classified as simple. Simple cells in cat V1 are typically found in layer 4 and receive direct thalamocortical input, whereas complex cells are primarily found in superficial layers of cortex and thought to receive inputs from simple cells (Hubel & Wiesel, 1962; LeVay & Gilbert, 1976; Reid & Alonso, 1995; Ferster et al. 1996; Chung & Ferster, 1998; Usrey et al. 1999; Alonso et al. 2001; Hirsch et al. 2003). Modulation of spikelet and principal neuron responses is also evident when comparing trial‐averaged responses of spike rate, membrane potential, and spikelet rate (Fig. 4 B, middle), which fluctuate at the stimulus temporal frequency.

Figure 4. Functional characterization of simple cell spikelets .

Figure 4

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A, simple cell spikelets paired with a simple principal cell. Spikelets are embedded in the membrane potential and amongst larger action potentials elicited by the principal neuron (left, inset). B, trial‐averaged spike, membrane potential and spikelet responses to preferred sinusoidal grating show modulation to the stimulus temporal frequency. C, one‐dimensional noise stimulus reveals separated on and off subregions in both voltage and spikelet‐triggered averages. D, same as in A for simple cell spikelets paired with a complex principal cell. E, same as in B for simple cell spikelets paired with a complex principal cell. Trial‐averaged grating response of membrane potential and spikes shows no modulation at the stimulus temporal frequency, unlike the spikelet responses. F, same as in C for simple cell spikelets paired with a complex principal cell. Spikelet‐triggered average revealed separated receptive field on and off subregions, while complex cell membrane potential covariance has two prominent nonlinear filters.

To further test simple cells, we measured receptive fields (RFs) using a one‐dimensional noise stimulus (see Methods) (McLean & Palmer, 1989; DeAngelis et al. 1993; McLean et al. 1994; Conway & Livingstone, 2003; Priebe, 2005; Mohanty et al. 2012; Park et al. 2013). Simple cells are characterized by linear filters, whereas complex cells exhibit dominant nonlinear filters. In this example, only the linear voltage‐triggered average and spikelet‐triggered average displayed significant structure, whereas nonlinear filters from covariance measurements yielded no structure (Fig. 4 C). The dominant linear filters with segregated on and off subregions matched our classification based on grating responses; membrane potential and spikelet RFs contained segregated on and off subregions, characteristic of classic V1 simple cells (Hubel & Wiesel, 1962; LeVay & Gilbert, 1976; Reid & Alonso, 1995; Ferster et al. 1996; Chung & Ferster, 1998; Usrey et al. 1999; Alonso et al. 2001; Hirsch et al. 2003). Both records also exhibited direction selectivity, which is evident in the slanted spatiotemporal receptive field (Priebe, 2005). These records demonstrate an example of matched selectivity between the principal neuron and the spikelet.

We also observed examples in which membrane potential and spikelets were characterized by different receptive field properties, evident both in response modulation to gratings and in noise mapping. For example, principal neurons that were characterized as complex cells could be associated with spikelet activity characteristic of a simple cell (Fig. 4 D). As in the previous example, spikelets were modulated by the grating, and yet the recorded membrane potential and accompanying spike rate showed no phase sensitivity (Fig. 4 E). This was evident when comparing the modulation ratio of action potentials of the principal neuron with spikelets (F 1/F 0 = 0.65 and 1.69, respectively). Similarly, one‐dimensional noise mapping revealed prominent nonlinear filters from voltage‐triggered covariance in the membrane potential, while structure only existed in the linear spikelet‐triggered average (Fig. 4 F). In this example, the principal neuron is therefore characterized as complex, while the spikelet activity is simple.

All combinations of principal neuron and spikelet RF properties were observed in our example records. Specifically, these included a simple cell with spikelets from a complex cell (Fig. 5 A–C) and a complex cell with spikelets from another complex cell (Fig. 5 D–F). A comparison of modulation ratios of spikelets and spikes from principal neurons across our population revealed a majority of records were simple‐to‐simple coupling (n = 8/16; Fig. 6). Simple cell spikelets observed in complex cells were the second most common (n = 4/16), followed by complex cell spikelets observed in simple cells (n = 3/16). Note that only those records for which there were a minimum of 20 action potentials and spikelets to the preferred stimulus were included in this analysis, as a low number of spikes systematically alters the F 1/F 0 ratio (Hietanen et al. 2013).

Figure 5. Functional characterization of complex cell spikelets .

Figure 5

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A–C, complex cell spikelets paired with simple principal cell. Same organization as in Fig. 2. D–F, complex cell spikelets paired with complex principal cell.

Figure 6. Relationship of principal neuron and spikelet simple and complex classifications .

Figure 6

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The modulation ratio (F 1/F 0) of spiking responses in principal neuron are plotted with spikelet response modulation ratio in the same record. A modulation ratio of greater than 1 is defined as a simple cell, while values less than 1 are complex cells.

The drifting gratings and noise stimuli we used to classify principal neurons and spikelet activity as simple or complex were optimized for the orientation preference of the principal neuron, and thus the robust spikelet activity observed with this single stimulus suggested similar orientation preferences. We directly compared the orientation preference between spikelet activity and the membrane potential of the principal neuron by varying the orientation of the presented gratings. Spikelet activity and membrane potential fluctuations to each drifting grating were cycle‐averaged to compute the peak responses (F o + F 1; see Methods). The orientation preference of membrane potential and spikelets are tightly correlated, as shown in an example record where both are tuned to 240–270 deg (Fig. 7 A). Spiking responses of principal neurons were more narrowly tuned than membrane potential, as reported previously (Finn et al. 2007). Across the population of records there existed a strong relationship between orientation preference of membrane potential and spikelets (Fig. 7 C) (circular‐correlation = 0.98 ± 0.01, SEM, n = 10, bootstrapped standard error) (Batschelet, 1981; Sokal & Rohlf, 1995). This relationship was also evident in the distribution of orientation preference difference between principal neuron membrane potential and spikelets (Fig. 7 D; mean |∆pref| = 11.0 ± 7.3, SD).

Figure 7. Orientation selectivity of principal cells and spikelets .

Figure 7

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A, example orientation tuning measured from principal neuron and spikelets. Mean peak responses from principal cell spikes (top, black), principal cell membrane potential (middle, gray), and spikelets (bottom, blue) are shown. Double Gaussian fits are shown to characterize tuning. Error bars indicate standard error. B, same as A for another example. C, relationship of orientation preferences of spikelets and principal cell membrane potential. D, distribution of orientation preference difference between spikelet and principal cell membrane potential.

The surprisingly tight relationship in orientation preference between spikelets and membrane potential prompted us to explore whether other response properties were also shared, in particular, binocular response properties. Using a dichoptic presentation of sinusoidal drifting gratings, we measured ocular preference with monocular stimulation and disparity sensitivity with binocular stimulation (Scholl et al. 2013). Disparity preferences were measured by systematically shifting the binocular phase differences between the two drifting gratings (Ohzawa & Freeman, 1986 b; DeAngelis et al. 1991; Cumming & Parker, 1997; Cumming & DeAngelis, 2001). Unlike orientation preference, disparity preference was not shared between the principal neuron and spikelets. As shown in an example record (Fig. 8 A), the membrane potential is tuned to a binocular phase difference of −180 deg, while the spikelets are well tuned for −45 deg. Since spiking responses from the principal cell are rectified versions of membrane potential, differences in tuning between principal cell spikes and spikelets were even greater (Priebe, 2008). In several rare cases we did observe identical tuning across spikes, membrane potential and spikelets (Fig. 8 B). Across our population, however, we found a very weak relationship in disparity preference between membrane potential and spikelets (circular‐correlation = 0.27 ± 0.13, SEM, n = 20, bootstrapped standard error) (Batschelet, 1981; Sokal & Rohlf, 1995). This lack of a relationship was also evident in the differences in disparity preference for individual records (mean |∆pref| = 103 ± 47.7 deg, SD). Similarity in orientation preference strongly suggests spikelets originate from nearby neurons in the same orientation column as the principal neuron (Hubel & Wiesel, 1963; Essen & Zeki, 1978; Bosking et al. 1997; Ohki et al. 2005, 2006; Yu & Ferster, 2013). Like the organization of orientation preference, ocular dominance columns are a well‐established feature of binocularity in cat V1 (Hubel & Wiesel, 1962; LeVay et al. 1978; LeVay & Voigt, 1988; Katz & Crowley, 2002). We then compared eye preferences of spikelets and principal neuron spikes, as measured by the ocular dominance index (ODI; see Methods) (Gordon & Stryker, 1996; Scholl et al. 2013). In 80% of our records, we found spikelets and the principal neuron shared eye preference (Fig. 8 D, grey shading), reflecting our observations of orientation preference. The dissimilarity in binocular disparity preference suggests that the columnar organization of binocular disparity preference differs from columnar organization for orientation and ocular dominance (Kara & Boyd, 2009).

Figure 8. Binocular disparity tuning of principal cells and spikelets .

Figure 8

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A, example disparity tuning measured from principal neuron and spikelets. Mean peak responses from principal cell spikes (top, black), principal cell membrane potential (middle, grey), and spikelets (bottom, blue) are shown. Both responses to binocular (circles) and monocular stimuli (squares) are shown. Cosine fits are used to characterize tuning. Error bars indicate standard error. B, same as A for another example. C, relationship of disparity preferences of spikelets and principal cell membrane potential. D, relationship of eye preference or ocular dominance index (ODI) for spikelets and principal cell membrane potential.

Correlations between spikelets and membrane potential

While it is clear that there are functional relationships between the principal neuron and spikelet records, it is unclear whether there is a direct link between spikelets and the synaptic input onto the principal cells. To reveal a possible relationship between spikelets and membrane potential, we computed the cross‐correlation or spikelet‐triggered membrane potential average (Yu & Ferster, 2013). Since spikelets are embedded in membrane potential, we carefully extracted spikelet times during visual stimuli or spontaneous periods (see Methods, examples from individual trials are shown in Fig. 9 A, left). Given the large, ongoing fluctuations of membrane potential, individual spikelet‐related responses appear noisy. Averaging membrane potential surrounding spikelets reveals systematic deviations from the mean membrane potential (blue trace, inset in Fig. 9 A, middle). Deviations could be the result of stimulus‐evoked responses, so we isolated correlated activity by shuffling trials from which spikelets were derived and computed a shuffled spikelet‐triggered average (dashed blue trace, inset Fig. 9 A, middle). This shuffled average was subtracted from the raw spikelet‐triggered average to reveal changes in membrane potential surrounding spikelets (Fig. 9 A, middle). This shuffle‐corrected spikelet‐triggered average displays three features: (1) a fast component due to the spikelet waveform centred around 0 ms lag, (2) a slower component with a positive cross‐correlation lag (∼3 ms) (Fig. 9 A, middle, asterisk), and (3) a very slow component that appears common network input to the spikelet and principal cell. The second component possesses a peak amplitude of about 2 mV, exhibiting an effect of depolarization following the spikelet. We also examined this relationship during spontaneous activity to be sure this correlation is unrelated to the visual stimulus. This revealed a similar, small positive lag following the spikelet in the shuffle‐corrected cross‐correlation (Fig. 9 A, right) and an even larger slow component that may arise from common network input. A similar signature was observed in another example (Fig. 9 B), although here the spontaneous activity was less pronounced. Across our spikelet records we observed small depolarizations with a positive cross‐correlation lag in only a subset of cells (n = 4/20). Depolarization amplitudes were small for both stimulus‐driven activity (mean = 0.78 ± 0.51 mV, SD) and spontaneous activity (mean = 0.95 ± 0.73 mV, SD). Peak depolarization lag times were also greater than 0 (stimulus: mean = 5.4 ± 3.24 ms, SD; spontaneous: mean = 5.8 ± 4.0 ms, SD). Data collected from stimulus‐driven activity and spontaneous activity were not significantly different for either depolarization amplitude or peak lag time (P = 0.56 and 0.67, respectively, Student's paired t test).

Figure 9. Correlation of spikelet activity and membrane potential .

Figure 9

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A, individual trials of raw membrane potential with spikelet events shown (left). Stimulus‐evoked spikelets aligned and averaged together reveal a depolarization lagging the spikelet waveform after shuffle correction (middle). Inset depicts raw, spikelet‐triggered average (continuous lines) and shuffled average (dotted lines). Trials with action potentials from the principal neuron have been removed to avoid contamination. Spontaneous spikelets have also been isolated and averaged, revealing a similar signature following the spikelet waveform (right). Grey shading in triggered averages is standard error. Asterisks highlight depolarization following spikelet waveform. B, same as in A for another example. C, example record with a large, stimulus‐independent, correlated network event occurring simultaneously with the spikelet. Same layout as A. D, example of spikelet displaying no effect on principal cell membrane potential during visually evoked or spontaneous activity. Same layout as A.

In other spikelet records we observed large, slow deflections in membrane potential occurring around the time of the spikelet, which could be upward of 10–15 mV. As shown in an example (Fig. 9 C), the spikelet‐triggered membrane potential average began before the spikelet (∼25 ms) and persisted long after. We observed these types of large, coordinated bumps of activity in a subset of neurons which did not exhibit the faster changes in membrane potential shown in the previous examples (n = 4/20). Unfortunately, these network events mask smaller correlations between spikelet activity and the principal neuron membrane potential, so we cannot extract any possible subtle depolarization. In the remaining proportion of spikelet records (12/20) we found no correlation between spikelet times and membrane potential, as represented by another example record (Fig. 9 D). In this recording, there appears to be no depolarization or hyperpolarization following the spikelet waveform, either during the stimulus period or for spontaneous activity.

Given that some spikelet‐triggered averages show signatures of synaptic connectivity and others do not, we wondered whether these groups also differed in shared functional selectivity. We found no difference between groups in the similarity of orientation and disparity preferences. In records with a depolarization following the spikelet and those without a depolarization, orientation preferences between principal neuron and spikelet were tightly coupled (mean |∆pref| = 15.7 ± 11.4 deg, SD, n = 3; mean |∆pref| = 12.7 ± 11.6 deg, SD, n = 7; respectively). These groups were not significantly different from one another (P = 0.52, Mann–Whitney test). The same was found for disparity selectivity, where spikelets and the corresponding principal neurons did not share similar preferences in records with and without cross‐correlation signatures (mean |∆pref| = 121.4 ± 76.1 deg, SD, n = 4; mean |∆pref| = 128.0 ± 80.0 deg, SD, n = 12; respectively). These groups were also not significantly different from one another (P = 0.95, Mann–Whitney test).

Discussion

Spikelets are observed in intracellular recordings, and yet characterization and a functional analysis of their receptive field properties have rarely been undertaken (Spencer & Kandel, 1961; Margrie et al. 2003; Epsztein et al. 2010, though see Martinez et al. 2014). We demonstrate that a number of properties indicate spikelets originate from single, nearby visual cortical neurons, distinct from the principal neuron. Spikelet waveforms are extremely uniform within a recording session and exhibit refractory periods expected from a single neuronal source. Other biophysical and functional aspects were quite distinct from the principal neuron and indicate that spikelets have a separate origin. First, spikelets are characterized by very short waveforms that are less than 2 ms, a time course that is too short to be the result of active processes within dendrites or passive polarization through gap junctions (Gibson et al. 2005). Second, spikelet activity is unaffected by current injection into the patched cell, strongly suggesting they are an independent, extracellular signal. Third, spikelets could be classified as simple or complex, independent of the classification of the principal neuron (Fig. 6). Finally, we found that the disparity preferences of spikelets were not related to the disparity preferences of the corresponding principal neurons (Fig. 8). Alongside these differences, however, spikelets shared response properties with the principal neuron, including orientation selectivity and eye preference (Figs 7 and 8), likely to be a product of the columnar organization in cat V1.

A source for spikelets

One interpretation of the spikelet waveforms we observed is they result from electrical connections between neurons, formed via gap junctions (MacVicar & Dudek, 1981; Taylor & Dudek, 1982; Vigmond et al. 1997; Gibson et al. 1999; Gibson et al. 2005). However, several aspects of our data are inconsistent with direct electrical coupling. First, because these waveforms are stereotyped and exhibit a refractory period, gap junctions would be required to exist only between pairs of neurons and not between larger groups of neurons. Second, we would expect that the high resistance of an electrical connection would greatly attenuate action potentials and elongate their time course (Gibson et al. 2005), whereas here we observe very short waveforms. Finally, injecting negative current into a neuron electrically connected with its neighbours should cause a hyperpolarization of membrane potential in those neurons resulting in a reduction in spiking activity, while here we observe no effect of hyperpolarization on spikelet activity.

An alternative interpretation is these waveforms originate from a neuron nearby our patch electrode and are recorded as an extracellular signal (Martinez et al. 2014). Consistent with this interpretation, spikelet waveforms appear to have action potential characteristics: they are extremely rapid and uniform, and have refractory periods consistent with cortical neurons. It is unclear, however, how spikelets were measured through our whole‐cell patch‐clamp electrode. During an attempt to achieve a whole‐cell patch, the membrane of the principal neuron could be ruptured and fused with a neighbouring neuron, thereby providing a means for spikelet waveforms to be measured on the intracellular pipette. This seems unlikely given that hyperpolarization of the principal neuron did not cause changes in spikelet activity (Fig. 2). Alternatively, the whole‐cell patch pipette could record the extracellular signals of a neighbouring neuron. In this case, spikelets would be purely an extracellular signal, similar to a loose‐cell attached or high impedance extracellular single unit recording.

The original intent of our recordings was not to measure spikelet activity, and we were surprised to find spikelets in such a high percentage of our measurements (21/76, 27%) when we performed post hoc examinations of our records. We hypothesize that the use of a higher sampling frequency than our previous records allowed us to extract these waveforms despite their short time course and small amplitude (Fig. 1). While we found no relationship between access resistance and the presence or absence of spikelets, aspects of the configuration used for blind whole‐cell recordings may contribute to the ability to measure spikelets from intracellular records.

Spikelet‐membrane potential correlation

We found that spikelets exhibited three distinct effects of influence on the membrane potential of principal cells. In some cases, it appears that spikelets are directly linked to a depolarization, presumably via synaptic transmission (Fig. 9 A and B). In other cases, there exists a spikelet‐locked depolarization, but the depolarization is slow and extended, over a time scale that is unlikely to be related to synaptic transmission. Instead, this prolonged depolarization may be a result of common input driving both the spikelet and principal neuron (Yu & Ferster, 2010, 2013). For some records we find no relationship between spikelet activity and membrane potential in the principal neuron. In none of these cases was spikelet activity associated with a hyperpolarization of membrane potential. Finally, although our sample population is small, none of these different patterns of linkage between spikelet and principal cell activity are related to any of the functional linkages measured here.

Functional similarity between spikelet and principal neuron

In comparing the functional selectivity of spikelets and the neuron recorded, we found orientation preference to closely match (Fig. 7 C and D). We interpret this match as the consequence of both neurons being co‐localized within the same cortical orientation column. Since orientation preference in cat V1 systematically changes across the cortical surface but is similar throughout the layers of cortex at a single point, a match would be expected if signals originated from nearby neurons (Hubel & Wiesel, 1963; Essen & Zeki, 1978; Bosking et al. 1997; Ohki et al. 2005, 2006; Yu & Ferster, 2013). Likewise, because of ocular dominance columns (Hubel & Wiesel, 1962; LeVay et al. 1978; LeVay & Voigt, 1988; Katz & Crowley, 2002), eye preferences should also match if signals are from nearby neurons, a feature we observed in our records (Fig. 8 D). Further, although we did not directly measure spatial frequency, gratings and one‐dimensional noise used to visually stimulate spikelets and principal neurons were of a single spatial frequency or bar width. Therefore, strong activation of both signals with a single stimulus suggests both neurons have similar spatial frequency preferences, another visual feature organized in cat V1 (Issa et al. 2000). In contrast, we found disparity preference is not matched between spikelets and principal neurons. This is surprising given a recent report of a columnar organization for disparity selectivity (Kara & Boyd, 2009). The discrepancy between orientation and disparity matches could potentially reflect a different form of columnar organization for these two functional properties. As yet it is unclear how separate maps for orientation, disparity and other functional properties, such as spatial frequency and receptive field position, are maintained along the two‐dimensional cortical surface in cat V1, but our results suggest that the columnar organizations for these functional properties are distinct.

In addition to a lack of functional similarity for disparity preference, there was a lack in functional similarity for simple and complex cell classification (Fig. 6). Ever since these V1 cell classes were first described by Hubel & Wiesel (1962), a hierarchy has been proposed in which multiple thalamic relay neurons provide drive to simple cells, and subsequently multiple simple cells converge on a complex cell. If spikelets reflect this hierarchal flow of information, it is surprising to find spikelets characterized as complex associated with principal cells characterized as simple (Fig. 5 A–C). One explanation for this association is that inhibitory complex cells, untuned for orientation (Hirsch et al. 2003), provide a gain control signal to simple cells.

The functional differences between spikelet and principal cell pairs is surprising given the known large‐scale architecture in V1. Whether these records reflect gap junctions or simply nearby neurons, the presence of distinct receptive field properties could reflect a problem of representing many stimulus dimensions, including orientation, direction, spatial frequency, ocular dominance, and disparity on a two‐dimensional surface (Miller, 1996). High resolution imaging at the cellular level has revealed subnetworks at very fine spatial scales: segregated subnetworks of neurons exist within the larger functional network (Yoshimura & Callaway, 2005), while the functional selectivity of neurons can shift over spatial scales of 20 μm (Ohki et al. 2005, 2006). The distinct properties in our records may therefore reflect a fine‐scale functional architecture within a larger columnar organization for visual cortex.

Additional information

Competing interests

None of the authors have any conflicts of interests for this paper.

Author contributions

B.S. and N.J.P. conceived and designed experiments. B.S. collected and assembled the data. B.S. and S.A. analysed the data. B.S. and N.J.P. interpreted the data. B.S. and N.J.P. wrote and revised the manuscript. All experiments were performed at UT Austin in Austin, TX, USA.

Funding

This work was supported by grants from the National Institutes of Health (EY‐019288) and the PEW Charitable Trusts.

Acknowledgements

We thank Jessica Hanover for helpful discussion.

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