In the mammalian cerebral cortex, neural responses are highly variable during spontaneous activity and sensory stimulation. To explain this variability, the cortex of alert animals has been proposed to be in an asynchronous high-conductance state in which irregular spiking arises from the convergence of large numbers of uncorrelated excitatory and inhibitory inputs onto individual neurons1,2,3,4. Signatures of this state are that a neuron’s membrane potential (Vm) hovers just below spike threshold, and its aggregate synaptic input is nearly Gaussian, arising from many uncorrelated inputs1,2,3,4. Alternatively, irregular spiking could arise from infrequent correlated input events that elicit large fluctuations in Vm (refs 5, 6). To distinguish between these hypotheses, we developed a technique to perform whole-cell Vm measurements from the cortex of behaving monkeys, focusing on primary visual cortex (V1) of monkeys performing a visual fixation task. Here we show that, contrary to the predictions of an asynchronous state, mean Vm during fixation was far from threshold (14 mV) and spiking was triggered by occasional large spontaneous fluctuations. Distributions of Vm values were skewed beyond that expected for a range of Gaussian input6,7, but were consistent with synaptic input arising from infrequent correlated events5,6. Furthermore, spontaneous fluctuations in Vm were correlated with the surrounding network activity, as reflected in simultaneously recorded nearby local field potential. Visual stimulation, however, led to responses more consistent with an asynchronous state: mean Vm approached threshold, fluctuations became more Gaussian, and correlations between single neurons and the surrounding network were disrupted. These observations show that sensory drive can shift a common cortical circuitry from a synchronous to an asynchronous state.
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van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726 (1996)
van Vreeswijk, C. & Sompolinsky, H. Chaotic balanced state in a model of cortical circuits. Neural Comput. 10, 1321–1371 (1998)
Kumar, A., Schrader, S., Aertsen, A. & Rotter, S. The high-conductance state of cortical networks. Neural Comput. 20, 1–43 (2008)
Renart, A. et al. The asynchronous state in cortical circuits. Science 327, 587–590 (2010)
DeWeese, M. R. & Zador, A. M. Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex. J. Neurosci. 26, 12206–12218 (2006)
Richardson, M. J. & Gerstner, W. Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance. Neural Comput. 17, 923–947 (2005)
Rudolph, M. & Destexhe, A. Characterization of subthreshold voltage fluctuations in neuronal membranes. Neural Comput. 15, 2577–2618 (2003)
Tolhurst, D. J., Movshon, J. A. & Thompson, I. D. The dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast. Exp. Brain Res. 41, 414–419 (1981)
Legenstein, R., Pecevski, D. & Maass, W. A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLOS Comput. Biol. 4, e1000180 (2008)
Thomson, A. M. & Lamy, C. Functional maps of neocortical local circuitry. Front. Neurosci. 1, 19–42 (2007)
Steriade, M., Timofeev, I. & Grenier, F. Natural waking and sleep states: a view from inside neocortical neurons. J. Neurophysiol. 85, 1969–1985 (2001)
Destexhe, A. & Pare, D. Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo . J. Neurophysiol. 81, 1531–1547 (1999)
Crochet, S. & Petersen, C. C. Correlating whisker behavior with membrane potential in barrel cortex of awake mice. Nature Neurosci. 9, 608–610 (2006)
Poulet, J. F. & Petersen, C. C. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature 454, 881–885 (2008)
Okun, M., Naim, A. & Lampl, I. The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats. J. Neurosci. 30, 4440–4448 (2010)
Hromadka, T., Zador, A. M. & Deweese, M. R. Up states are rare in awake auditory cortex. J. Neurophysiol. 109, 1989–1995 (2013)
Peyrache, A. et al. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proc. Natl Acad. Sci. USA 109, 1731–1736 (2012)
Matsumura, M., Chen, D., Sawaguchi, T., Kubota, K. & Fetz, E. E. Synaptic interactions between primate precentral cortex neurons revealed by spike-triggered averaging of intracellular membrane potentials in vivo . J. Neurosci. 16, 7757–7767 (1996)
Pei, X., Volgushev, M., Vidyasagar, T. R. & Creutzfeldt, O. D. Whole cell recording and conductance measurements in cat visual cortex in vivo . Neuroreport 2, 485–488 (1991)
Ferster, D. & Jagadeesh, B. EPSP–IPSP interactions in cat visual cortex studied with in vivo whole-cell patch recording. J. Neurosci. 12, 1262–1274 (1992)
Borg-Graham, L. J., Monier, C. & Fregnac, Y. Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature 393, 369–373 (1998)
Hirsch, J. A., Alonso, J. M., Reid, R. C. & Martinez, L. M. Synaptic integration in striate cortical simple cells. J. Neurosci. 18, 9517–9528 (1998)
Kohn, A. & Smith, M. A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25, 3661–3673 (2005)
Nauhaus, I., Busse, L., Carandini, M. & Ringach, D. L. Stimulus contrast modulates functional connectivity in visual cortex. Nature Neurosci. 12, 70–76 (2009)
Poulet, J. F., Fernandez, L. M., Crochet, S. & Petersen, C. C. Thalamic control of cortical states. Nature Neurosci. 15, 370–372 (2012)
de Kock, C. P. & Sakmann, B. Spiking in primary somatosensory cortex during natural whisking in awake head-restrained rats is cell-type specific. Proc. Natl Acad. Sci. USA 106, 16446–16450 (2009)
Brunel, N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comput. Neurosci. 8, 183–208 (2000)
Mehring, C., Hehl, U., Kubo, M., Diesmann, M. & Aertsen, A. Activity dynamics and propagation of synchronous spiking in locally connected random networks. Biol. Cybern. 88, 395–408 (2003)
Cohen, M. R. & Maunsell, J. H. Attention improves performance primarily by reducing interneuronal correlations. Nature Neurosci. 12, 1594–1600 (2009)
Mitchell, J. F., Sundberg, K. A. & Reynolds, J. H. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63, 879–888 (2009)
Chen, Y., Geisler, W. S. & Seidemann, E. Optimal decoding of correlated neural population responses in the primate visual cortex. Nature Neurosci. 9, 1412–1420 (2006)
Chen, Y., Geisler, W. S. & Seidemann, E. Optimal temporal decoding of neural population responses in a reaction-time visual detection task. J. Neurophysiol. 99, 1366–1379 (2008)
Yang, Z., Heeger, D. J. & Seidemann, E. Rapid and precise retinotopic mapping of the visual cortex obtained by voltage-sensitive dye imaging in the behaving monkey. J. Neurophysiol. 98, 1002–1014 (2007)
Matsumura, M. Intracellular synaptic potentials of primate motor cortex neurons during voluntary movement. Brain Res. 163, 33–48 (1979)
Chen, D. & Fetz, E. E. Characteristic membrane potential trajectories in primate sensorimotor cortex neurons recorded in vivo . J. Neurophysiol. 94, 2713–2725 (2005)
Margrie, T. W., Brecht, M. & Sakmann, B. In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain. Pflugers Arch. 444, 491–498 (2002)
Richardson, M. J. & Gerstner, W. Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise. Chaos 16, 026106 (2006)
Richardson, M. J. & Swarbrick, R. Firing-rate response of a neuron receiving excitatory and inhibitory synaptic shot noise. Phys. Rev. Lett. 105, 178102 (2010)
Wolff, L. & Lindner, B. Method to calculate the moments of the membrane voltage in a model neuron driven by multiplicative filtered shot noise. Phys. Rev. E 77, 041913 (2008)
Mitra, P. & Bokil, H. Observed Brain Dynamics (Oxford Univ. Press, 2008)
Miller, K. D. & Troyer, T. W. Neural noise can explain expansive, power-law nonlinearities in neural response functions. J. Neurophysiol. 87, 653–659 (2002)
Hansel, D. & van Vreeswijk, C. How noise contributes to contrast invariance of orientation tuning in cat visual cortex. J. Neurosci. 22, 5118–5128 (2002)
Priebe, N. J., Mechler, F., Carandini, M. & Ferster, D. The contribution of spike threshold to the dichotomy of cortical simple and complex cells. Nature Neurosci. 7, 1113–1122 (2004)
Swindale, N. V. Orientation tuning curves: empirical description and estimation of parameters. Biol. Cybern. 78, 45–56 (1998)
Ringach, D. L., Shapley, R. M. & Hawken, M. J. Orientation selectivity in macaque V1: diversity and laminar dependence. J. Neurosci. 22, 5639–5651 (2002)
Skottun, B. C. et al. Classifying simple and complex cells on the basis of response modulation. Vision Res. 31, 1079–1086 (1991)
Destexhe, A., Rudolph, M., Fellous, J. M. & Sejnowski, T. J. Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107, 13–24 (2001)
Pospischil, M. et al. Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons. Biol. Cybern. 99, 427–441 (2008)
Goodman, D. & Brette, R. Brian: a simulator for spiking neural networks in python. Front. Neuroinform. 2, 5 (2008)
Goodman, D. F. & Brette, R. The Brian simulator. Front. Neurosci. 3, 192–197 (2009)
Anderson, J. S., Carandini, M. & Ferster, D. Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. J. Neurophysiol. 84, 909–926 (2000)
We thank T. Cakic for assistance with this project, and J. Hanover, D. Ferster, K. D. Miller and A. C. Huk for discussions and comments. A.Y.Y.T., B.S. and N.J.P. were supported by grants from the National Institutes of Health (NIH) (EY-019288) and the Pew Charitable Trusts; Y.C. and E.S. were supported by grants from the NIH (EY-016454 and EY-16752).
The authors declare no competing financial interests.
Extended data figures and tables
a, Vm responses (top traces), eye position traces (bottom pairs of traces) from three blank trials (left), three trials at the preferred orientation (centre), and three trials at the orthogonal orientation (right). b, Trial averaged Vm (top) and spike rate (bottom) for all orientations, from the neuron in a. c, Spike rate versus membrane potential, and best-fit thresholded power law, from the neuron in a. d, Orientation tuning curves for Vm and spike rate, and predicted spike rate orientation tuning curve using the Vm orientation tuning curve and the best-fit thresholded power law in c, from the neuron in a. e, Orientation selectivity index (OSI) for spike rate versus OSI for Vm. Lines represent expected relationships between spike rate OSI and Vm OSI for thresholded power laws with exponents 2, 3 and 5 (blue, red and black, respectively). f, Fourier component of the response with the same temporal frequency as the moving sinusoidal grating visual stimulus divided by the time-averaged response for spike rate (R1/R0) versus that for Vm (V1/V0). Lines represent expected relationships between R1/R0 and V1/V0 for thresholded power laws with exponents 2, 3 and 5 (blue, red and black, respectively).
a, Gaussian excitatory (black) and inhibitory (red) conductances, Vm with spiking disabled (green), Vm with spiking enabled (light blue), and Vm with spikes removed (dark blue), and corresponding Vm amplitude histograms and skewness values ζ, for a simulated neuron with Hodgkin–Huxley conductances. b, Vm with spiking (light blue) and with spikes removed (dark blue) and corresponding Vm amplitude histograms and skewness values ζ, for a recorded neuron. c, Apparent skewness from Vm with spikes removed versus skewness from Vm with spiking disabled from a simulated neuron with Hodgkin–Huxley conductances, for a range of Gaussian inputs.
a, Raw traces from several trials. b, Traces after bandpass filtering and spike removal. c, Vm responses from each cycle (top grey traces), cycle-averaged response (top black trace) and histogram of Vm responses (top histogram); residual traces from each cycle after subtraction of cycle-averaged response (bottom grey traces), cycle-averaged residuals (bottom black trace) and histogram of Vm residuals (bottom histogram). Note the change in vertical scale from top to bottom panels.
a, Joint distribution of Vm–threshold distance and skewness ζ during blank trials. b, Joint distribution of Vm–threshold distance and skewness ζ during preferred orientation trials.
a, Distribution of membrane resistance (left) and corresponding membrane conductance (right) during blank trials. b, Change in membrane conductance during visual stimulation in two example neurons during blank (left), preferred (centre) and 45° from preferred (right) trials. Each row shows data from a different neuron.
a, Power spectrum of Vm (top panels) and LFP (bottom panels) fluctuations from the trial average (residuals) during blank trials (left panels), residuals during preferred orientation stimulation (middle panels), and raw Vm traces during preferred orientation stimulation (right panel). Each trace corresponds to an individual neuron. b, Population-averaged ratio of power spectrum at the preferred orientation to power spectrum for blank trials for Vm fluctuations from the trial average (‘Vm residuals’, left panel), LFP fluctuations from the trial average (‘LFP residuals’, middle panel), and raw Vm (right panel). Error bars are jack-knifed standard errors.
Population-averaged Vm–LFP coherence magnitudes for blank trials (green) and at the preferred orientation (lavender). Error bars are jack-knifed standard errors.
Extended Data Figure 8 Decreased magnitude of Vm–LFP correlation during a flashed stimulus in a visual saccade task.
a, Each trial began when a fixation spot was displayed at the centre of a monitor in front of the monkey. The monkey had to shift gaze to the fixation point and maintain tight fixation for at least 1,500 ms. A flashed Gabor target stimulus appeared at a random time between 1,000 and 1,500 ms after the monkey had established tight fixation. The monkey had to saccade to the target stimulus within 600 ms to receive a reward. We analysed Vm and LFP only from trials in which the monkey performed the task successfully. b, Simultaneously recorded Vm and LFP, as well as eye movement traces, in two trials from an example neuron. Asterisks indicate near-simultaneous deflections in Vm and LFP during the pre-stimulus fixation period. Grey shading indicates the analysis period for correlations during the flashed Gabor stimulus; we included 30 ms after saccade onset in this period, because the visual latency for spike responses in the lateral geniculate nucleus is greater than 30 ms. c, Zero-lag cross-correlation between Vm and LFP fluctuations from the trial average during the flashed Gabor stimulus versus during the pre-stimulus period.
Extended Data Figure 9 Summary of first saccade latency and peak velocity in monkeys T and W, which together contributed the majority of the recorded data.
a, Top: histogram of latency of first saccade after fixation point termination in three neurons (158 trials) in monkey W. Arrow indicates median latency (217 ms). In 1.9% of the trials no saccade was detected in the 600 ms after fixation point termination. Bottom: histogram of peak eye velocity for first saccades during the 600 ms after fixation point offset. Arrow indicates median peak velocity (292° s−1). b, Results from eight neurons (464 trials) in monkey T. The format is the same as in a. Median latency is 314 ms and median peak velocity is 229° s−1. Monkey W tended to make larger saccades away from fixation, whereas monkey T tended to make smaller saccades and in a small subset of the trials remained close to the fixation point location until the next trial was initiated. This may reflect the fact that the minimal inter-trial interval was shorter in monkey T than in monkey W. The short latency of the saccades after fixation point termination in the vast majority of the trials indicates that both monkeys were alert and attentive and were actively engaged in maintaining tight fixation.
a, Vm response to injections of current steps of different magnitudes in an example neuron. b, Interspike interval during the current step versus interval ordinal. The interspike interval increased with interval ordinal, indicating that this neuron was regular-spiking.
This file contains Supplementary Text and Data and Supplementary References. (PDF 316 kb)
Continuous whole cell current clamp record of Vm from a V1 neuron and eye movements in the behaving macaque viewing sinusoidal drifting grating visual stimuli over multiple trials and inter-trial periods.
Continuous whole cell current clamp record of Vm from a V1 neuron and eye movements in the behaving macaque viewing sinusoidal drifting grating visual stimuli over multiple trials and inter-trial periods. (MP4 23508 kb)
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Tan, A., Chen, Y., Scholl, B. et al. Sensory stimulation shifts visual cortex from synchronous to asynchronous states. Nature 509, 226–229 (2014). https://doi.org/10.1038/nature13159
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