Anthony M. Zador

Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex

Neurons in the primary auditory cortex are tuned to the intensity and specific frequencies of sounds, but the synaptic mechanisms underlying this tuning remain uncertain. Inhibition seems to have a functional role in the formation of cortical receptive fields, because stimuli often suppress similar or neighbouring responses, and pharmacological blockade of inhibition broadens tuning curves. Here we use whole-cell recordings in vivo to disentangle the roles of excitatory and inhibitory activity in the tone-evoked responses of single neurons in the auditory cortex. The excitatory and inhibitory receptive fields cover almost exactly the same areas, in contrast to the predictions of classical lateral inhibition models. Thus, although inhibition is typically as strong as excitation, it is not necessary to establish tuning, even in the receptive field surround. However, inhibition and excitation occurred in a precise and stereotyped temporal sequence: an initial barrage of excitatory input was rapidly quenched by inhibition, truncating the spiking response within a few (1–4) milliseconds. Balanced inhibition might thus serve to increase the temporal precision and thereby reduce the randomness of cortical operation, rather than to increase noise as has been proposed previously.

Natural scene statistics at the centre of gaze

Early stages of visual processing may exploit the characteristic structure of natural visual stimuli. This structure may differ from the intrinsic structure of natural scenes, because sampling of the environment is an active process. For example, humans move their eyes several times a second when looking at a scene. The portions of a scene that fall on the fovea are sampled at high spatial resolution, and receive a disproportionate fraction of cortical processing. We recorded the eye positions of human subjects while they viewed images of natural scenes. We report that active selection affected the statistics of the stimuli encountered by the fovea, and also by the parafovea up to eccentricities of 4 degrees. We found two related effects. First, subjects looked at image regions that had high spatial contrast. Second, in these regions, the intensities of nearby image points (pixels) were less correlated with each other than in images selected at random. These effects could serve to increase the information available to the visual system for further processing. We show that both of these effects can be simply obtained by constructing an artificial ensemble comprised of the highest-contrast regions of images.

Input synchrony and the irregular firing of cortical neurons

Cortical neurons in the waking brain fire highly irregular, seemingly random, spike trains in response to constant sensory stimulation, whereas in vitro they fire regularly in response to constant current injection. To test whether, as has been suggested, this high in vivo variability could be due to the postsynaptic currents generated by independent synaptic inputs, we injected synthetic synaptic current into neocortical neurons in brain slices. We report that independent inputs cannot account for this high variability, but this variability can be explained by a simple alternative model of the synaptic drive in which inputs arrive synchronously. Our results suggest that synchrony may be important in the neural code by providing a means for encoding signals with high temporal fidelity over a population of neurons.

Sparse Representation of Sounds in the Unanesthetized Auditory Cortex

How do neuronal populations in the auditory cortex represent acoustic stimuli? Although sound-evoked neural responses in the anesthetized auditory cortex are mainly transient, recent experiments in the unanesthetized preparation have emphasized subpopulations with other response properties. To quantify the relative contributions of these different subpopulations in the awake preparation, we have estimated the representation of sounds across the neuronal population using a representative ensemble of stimuli. We used cell-attached recording with a glass electrode, a method for which single-unit isolation does not depend on neuronal activity, to quantify the fraction of neurons engaged by acoustic stimuli (tones, frequency modulated sweeps, white-noise bursts, and natural stimuli) in the primary auditory cortex of awake head-fixed rats. We find that the population response is sparse, with stimuli typically eliciting high firing rates (>20 spikes/second) in less than 5% of neurons at any instant. Some neurons had very low spontaneous firing rates (<0.01 spikes/second). At the other extreme, some neurons had driven rates in excess of 50 spikes/second. Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported. Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex. Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.

Synaptic Mechanisms of Forward Suppression in Rat Auditory Cortex

In the auditory cortex, brief sounds elicit a powerful suppression of responsiveness that can persist for hundreds of milliseconds. This forward suppression (sometimes also called forward masking) has usually been attributed to synaptic (GABAergic) inhibition. Here we have used whole-cell recordings in vivo to assess the role of synaptic inhibition in forward suppression in auditory cortex. We measured the excitatory and inhibitory synaptic conductances elicited by pairs of brief sounds presented at intervals from tens to hundreds of milliseconds. We find that inhibitory conductances rarely last longer than 50-100 ms, whereas spike responses and synaptic inputs remain suppressed for hundreds of milliseconds. We conclude that postsynaptic inhibition contributes to forward suppression for only the first 50-100 ms after a stimulus and that intracortical contributions to long-lasting suppression must involve other mechanisms, such as synaptic depression.

Linearity of Cortical Receptive Fields Measured with Natural Sounds

How do cortical neurons represent the acoustic environment? This question is often addressed by probing with simple stimuli such as clicks or tone pips. Such stimuli have the advantage of yielding easily interpreted answers, but have the disadvantage that they may fail to uncover complex or higher-order neuronal response properties. Here, we adopt an alternative approach, probing neuronal responses with complex acoustic stimuli, including animal vocalizations. We used in vivo whole-cell methods in the rat auditory cortex to record subthreshold membrane potential fluctuations elicited by these stimuli. Most neurons responded robustly and reliably to the complex stimuli in our ensemble. Using regularization techniques, we estimated the linear component, the spectrotemporal receptive field (STRF), of the transformation from the sound (as represented by its time-varying spectrogram) to the membrane potential of the neuron. We find that the STRF has a rich dynamical structure, including excitatory regions positioned in general accord with the prediction of the classical tuning curve. However, whereas the STRF successfully predicts the responses to some of the natural stimuli, it surprisingly fails completely to predict the responses to others; on average, only 11% of the response power could be predicted by the STRF. Therefore, most of the response of the neuron cannot be predicted by the linear component, although the response is deterministically related to the stimulus. Analysis of the systematic errors of the STRF model shows that this failure cannot be attributed to simple nonlinearities such as adaptation to mean intensity, rectification, or saturation. Rather, the highly nonlinear response properties of auditory cortical neurons must be attributable to nonlinear interactions between sound frequencies and time-varying properties of the neural encoder.

PINP: A New Method of Tagging Neuronal Populations for Identification during In Vivo Electrophysiological Recording

Neural circuits are exquisitely organized, consisting of many different neuronal subpopulations. However, it is difficult to assess the functional roles of these subpopulations using conventional extracellular recording techniques because these techniques do not easily distinguish spikes from different neuronal populations. To overcome this limitation, we have developed PINP (Photostimulation-assisted Identification of Neuronal Populations), a method of tagging neuronal populations for identification during in vivo electrophysiological recording. The method is based on expressing the light-activated channel channelrhodopsin-2 (ChR2) to restricted neuronal subpopulations. ChR2-tagged neurons can be detected electrophysiologically in vivo since illumination of these neurons with a brief flash of blue light triggers a short latency reliable action potential. We demonstrate the feasibility of this technique by expressing ChR2 in distinct populations of cortical neurons using two different strategies. First, we labeled a subpopulation of cortical neurons—mainly fast-spiking interneurons—by using adeno-associated virus (AAV) to deliver ChR2 in a transgenic mouse line in which the expression of Cre recombinase was driven by the parvalbumin promoter. Second, we labeled subpopulations of excitatory neurons in the rat auditory cortex with ChR2 based on projection target by using herpes simplex virus 1 (HSV1), which is efficiently taken up by axons and transported retrogradely; we find that this latter population responds to acoustic stimulation differently from unlabeled neurons. Tagging neurons is a novel application of ChR2, used in this case to monitor activity instead of manipulating it. PINP can be readily extended to other populations of genetically identifiable neurons, and will provide a useful method for probing the functional role of different neuronal populations in vivo.

Corticostriatal neurons in auditory cortex drive decisions during auditory discrimination.

The neural pathways by which information about the acoustic world reaches the auditory cortex are well characterized, but how auditory representations are transformed into motor commands is not known. Here we use a perceptual decision-making task in rats to study this transformation. We demonstrate the role of corticostriatal projection neurons in auditory decisions by manipulating the activity of these neurons in rats performing an auditory frequency-discrimination task. Targeted channelrhodopsin-2 (ChR2)-mediated stimulation of corticostriatal neurons during the task biased decisions in the direction predicted by the frequency tuning of the stimulated neurons, whereas archaerhodopsin-3 (Arch)-mediated inactivation biased decisions in the opposite direction. Striatal projections are widespread in cortex and may provide a general mechanism for the control of motor decisions by sensory cortex.

Non-Gaussian Membrane Potential Dynamics Imply Sparse, Synchronous Activity in Auditory Cortex

Many models of cortical dynamics have focused on the high-firing regime, in which neurons are driven near their maximal rate. Here we consider the responses of neurons in auditory cortex under typical low-firing rate conditions, when stimuli have not been optimized to drive neurons maximally. We used whole-cell patch-clamp recording in vivo to measure subthreshold membrane potential fluctuations in rat primary auditory cortex in both the anesthetized and awake preparations. By analyzing the subthreshold membrane potential dynamics on single trials, we made inferences about the underlying population activity. We found that, during both spontaneous and evoked responses, membrane potential was highly non-Gaussian, with dynamics consisting of occasional large excursions (sometimes tens of millivolts), much larger than the small fluctuations predicted by most random walk models that predict a Gaussian distribution of membrane potential. Thus, presynaptic inputs under these conditions are organized into quiescent periods punctuated by brief highly synchronous volleys, or “bumps.” These bumps were typically so brief that they could not be well characterized as “up states” or “down states.” We estimate that hundreds, perhaps thousands, of presynaptic neurons participate in the largest volleys. These dynamics suggest a computational scheme in which spike timing is controlled by concerted firing among input neurons rather than by small fluctuations in a sea of background activity.

Dynamic Stochastic Synapses as Computational Units

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-andfire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increased through the use of dynamic synapses.