Biomedical subjects

Anthony M Zador

Publications and source records attributed to Anthony M Zador.

9 recordsLinked to original sources

PubMed2006

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.

Acoustic Stimulation↗

PubMed2006

Sparse representations for the cocktail party problem.

A striking feature of many sensory processing problems is that there appear to be many more neurons engaged in the internal representations of the signal than in its transduction. For example, humans have approximately 30,000 cochlear neurons, but at least 1000 times as many neurons in the auditory cortex. Such apparently redundant internal representations have sometimes been proposed as necessary to overcome neuronal noise. We instead posit that they directly subserve computations of interest. Here we provide an example of how sparse overcomplete linear representations can directly solve difficult acoustic signal processing problems, using as an example monaural source separation using solely the cues provided by the differential filtering imposed on a source by its path from its origin to the cochlea [the head-related transfer function (HRTF)]. In contrast to much previous work, the HRTF is used here to separate auditory streams rather than to localize them in space. The experimentally testable predictions that arise from this model, including a novel method for estimating the optimal stimulus of a neuron using data from a multineuron recording experiment, are generic and apply to a wide range of sensory computations.

Action Potentials↗

PubMed2005

Reliability and representational bandwidth in the auditory cortex.

It is unclear why there are so many more neurons in sensory cortex than in the sensory periphery. One possibility is that these "extra" neurons are used to overcome cortical noise and faithfully represent the acoustic stimulus. Another possibility is that even after overcoming cortical noise, there is "excess representational bandwidth" available and that this bandwidth is used to represent conjunctions of auditory and nonauditory information for computation. Here, we discuss recent data about neuronal reliability in auditory cortex showing that cortical noise may not be as high as was previously believed. Although at present, the data suggest that auditory cortex neurons can be more reliable than those in the visual cortex, we speculate that the principles governing cortical computation are universal and that visual and other cortical areas can also exploit strategies based on similarly high-fidelity activity.

Acoustic Stimulation↗

PubMed2005

Neural gallops across auditory streams.

We continually rely on our ability to segregate the myriad sounds in our environment--phones ringing, people talking--into separate "auditory streams," each originating from a different source. In this issue of Neuron, Micheyl et al. provide the most direct evidence to date linking single-unit spiking responses from auditory cortex to the perception of distinct auditory streams.

Acoustic Stimulation↗

PubMed2005

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.

Acoustic Stimulation↗

PubMed2004

Shared and private variability in the auditory cortex.

The high variability of cortical sensory responses is often assumed to impose a major constraint on efficient computation. In the auditory cortex, however, response variability can be very low. We have used in vivo whole cell patch-clamp methods to study the trial-to-trial variability of the subthreshold fluctuations in membrane potential underlying tone-evoked responses in the auditory cortex of anesthetized rats. Using methods adapted from classical quantal analysis, we partitioned this subthreshold variability into a private component (which includes synaptic, thermal, and other sources local to the recorded cell) and a shared component arising from network interactions. Here we report that this private component is remarkably small, usually about 1-3 mV, as quantified by the variance divided by the mean of the ensemble of tone-evoked response heights. The shared component can be much larger, and shows more heterogeneity across the population, ranging from about 0 to 10 mV. The remarkable fact that, at least 5 synapses from the auditory periphery, this variability remains so small raises the possibility that the intervening neural circuitry is organized so as to prevent private noise from accumulating as neural signals propagate to the cortex.

Acoustic Stimulation↗

PubMed2004

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.

Acoustic Stimulation↗

PubMed2003

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.

Acoustic Stimulation↗

PubMed2003

Binary spiking in auditory cortex.

Neurons are often assumed to operate in a highly unreliable manner: a neuron can signal the same stimulus with a variable number of action potentials. However, much of the experimental evidence supporting this view was obtained in the visual cortex. We have, therefore, assessed trial-to-trial variability in the auditory cortex of the rat. To ensure single-unit isolation, we used cell-attached recording. Tone-evoked responses were usually transient, often consisting of, on average, only a single spike per stimulus. Surprisingly, the majority of responses were not just transient, but were also binary, consisting of 0 or 1 action potentials, but not more, in response to each stimulus; several dramatic examples consisted of exactly one spike on 100% of trials, with no trial-to-trial variability in spike count. The variability of such binary responses differs from comparably transient responses recorded in visual cortical areas such as area MT, and represent the lowest trial-to-trial variability mathematically possible for responses of a given firing rate. Our study thus establishes for the first time that transient responses in auditory cortex can be described as a binary process, rather than as a highly variable Poisson process. These results demonstrate that cortical architecture can support a more precise control of spike number than was previously recognized, and they suggest a re-evaluation of models of cortical processing that assume noisiness to be an inevitable feature of cortical codes.

Action Potentials↗