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1.  Independent Population Coding of Speech with Sub-Millisecond Precision 
The Journal of Neuroscience  2013;33(49):19362-19372.
To understand the strategies used by the brain to analyze complex environments, we must first characterize how the features of sensory stimuli are encoded in the spiking of neuronal populations. Characterizing a population code requires identifying the temporal precision of spiking and the extent to which spiking is correlated, both between cells and over time. In this study, we characterize the population code for speech in the gerbil inferior colliculus (IC), the hub of the auditory system where inputs from parallel brainstem pathways are integrated for transmission to the cortex. We find that IC spike trains can carry information about speech with sub-millisecond precision, and, consequently, that the temporal correlations imposed by refractoriness can play a significant role in shaping spike patterns. We also find that, in contrast to most other brain areas, the noise correlations between IC cells are extremely weak, indicating that spiking in the population is conditionally independent. These results demonstrate that the problem of understanding the population coding of speech can be reduced to the problem of understanding the stimulus-driven spiking of individual cells, suggesting that a comprehensive model of the subcortical processing of speech may be attainable in the near future.
PMCID: PMC3850048  PMID: 24305831
2.  The effects of interaural time difference and intensity on the coding of low frequency sounds in the mammalian midbrain 
We examined how changes in intensity and interaural time difference (ITD) influenced the coding of low frequency sounds in the inferior colliculus (IC) of male gerbils at both the single neuron and population levels. We found that changes in intensity along the positive slope of the rate-level function (RLF) evoked changes in spectrotemporal filtering that influenced in the overall timing of spike events, but preserved their precision across trials such that the decoding of single neuron responses was not affected. In contrast, changes in ITD did not trigger changes in spectrotemporal filtering, but had strong effects on the precision of spike events and, consequently, on decoder performance. However, changes in ITD had opposing effects in the two brain hemispheres, and, thus, canceled out at the population level. These results were similar with and without the addition of background noise. We also found that the effects of changes in intensity along the negative slope of the rate-level function (RLF) were different from the effects of changes in intensity along the positive slope in that they evoked changes in both spectrotemporal filtering and in the precision of spike events across trials, as well as in decoder performance. These results demonstrate that, at least at moderate intensities, the auditory system employs different strategies at the single neuron and population levels simultaneously to ensure that the coding of sounds is robust to changes in other stimulus features.
PMCID: PMC3083843  PMID: 21389237
auditory; coding; decoding; spectrotemporal filtering; ITD; midbrain
3.  Incremental Mutual Information: A New Method for Characterizing the Strength and Dynamics of Connections in Neuronal Circuits 
PLoS Computational Biology  2010;6(12):e1001035.
Understanding the computations performed by neuronal circuits requires characterizing the strength and dynamics of the connections between individual neurons. This characterization is typically achieved by measuring the correlation in the activity of two neurons. We have developed a new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI). By conditioning out the temporal dependencies in the responses of individual neurons before measuring the dependency between them, IMI improves on standard correlation-based measures in several important ways: 1) it has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources (e.g. shared inputs or intrinsic cellular or network mechanisms) provided that the dependencies have appropriate timescales, 2) for the study of early sensory systems, it does not require responses to repeated trials of identical stimulation, and 3) it does not assume that the connection between neurons is linear. We describe the theory and implementation of IMI in detail and demonstrate its utility on experimental recordings from the primate visual system.
Author Summary
The root of our brain's computational power lies in its trillions of connections. With our increasing ability to study these connections experimentally comes the need for analytical tools that can be used to develop meaningful quantitative characterizations. In this manuscript, we present a new such tool, incremental mutual information (IMI), that enables the characterization of the strength and dynamics of the connection between a pair of neurons based on the statistical dependencies in their spiking activity. IMI is an important step forward from existing approaches, as it has the potential to disambiguate dependencies due to the connection between two neurons from those due to other sources, such as shared external inputs, provided that the dependencies have appropriate timescales. We demonstrate the utility of IMI through the analysis of simulated neuronal activity as well as activity recorded in the primate visual system.
PMCID: PMC3000350  PMID: 21151578
4.  Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations 
As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model is an extension of previous attempts to model population spike trains designed to control only the total correlation between cells. In our model, the response of each cell is represented as a binary vector given by the dichotomized sum of a deterministic “signal” that is repeated on each trial and a Gaussian random “noise” that is different on each trial. This model allows the simulation of population spike trains with PSTHs, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the noise correlations in the spike trains to be manipulated independently of the signal correlations and single-cell properties. To demonstrate the utility of the model, we use it to simulate and manipulate experimental responses from the mammalian auditory and visual systems. We also present a general form of the model in which both the signal and noise are Gaussian random processes, allowing the mean spike rate, trial-to-trial variability, and pairwise signal and noise correlations to be specified independently. Together, these methods for modeling spike trains comprise a potentially powerful set of tools for both theorists and experimentalists studying population responses in sensory systems.
PMCID: PMC2998046  PMID: 21152346
population; correlation; noise correlation; simulation; model
5.  Timing Precision in Population Coding of Natural Scenes in the Early Visual System 
PLoS Biology  2008;6(12):e324.
The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ∼10-ms resolution is a crucial property of the neural code entering cortex.
Author Summary
Neurons convey information about the world in the form of trains of action potentials (spikes). These trains are highly repeatable when the same stimulus is presented multiple times, and this temporal precision across repetitions can be as fine as a few milliseconds. It is usually assumed that this time scale also corresponds to the timing precision of several neighboring neurons firing in concert. However, the relative timing of spikes emitted by different neurons in a local population is not necessarily as fine as the temporal precision across repetitions within a single neuron. In the visual system of the brain, the level of contrast in the image entering the retina can affect single-neuron temporal precision, but the effects of contrast on the neural population code are unknown. Here we show that the temporal scale of the population code entering visual cortex is on the order of 10 ms and is largely insensitive to changes in visual contrast. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary in representing the more slowly varying natural environment, preserving relative spike timing at a ∼10-ms resolution may be a crucial property of the neural code entering cortex.
Early neural representation of visual scenes occurs with a temporal precision on the order of 10 ms, which is precise enough to strongly drive downstream neurons in the visual pathway. Unlike individual neurons, the neural population code is largely insensitive to pronounced changes in visual contrast.
PMCID: PMC2602720  PMID: 19090624
6.  Estimating Receptive Fields from Responses to Natural Stimuli with Asymmetric Intensity Distributions 
PLoS ONE  2008;3(8):e3060.
The reasons for using natural stimuli to study sensory function are quickly mounting, as recent studies have revealed important differences in neural responses to natural and artificial stimuli. However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used. While a number of approaches have been developed to explicitly correct the bias due to stimulus correlations, there is no complementary technique to correct the bias due to stimulus asymmetries. Here, we develop a method for RF estimation that corrects reverse correlation RF estimates for the spherical asymmetries present in natural stimuli. Using simulated neural responses, we demonstrate how stimulus asymmetries can bias reverse-correlation RF estimates (even for uncorrelated stimuli) and illustrate how this bias can be removed by explicit correction. We demonstrate the utility of the asymmetry correction method under experimental conditions by estimating RFs from the responses of retinal ganglion cells to natural stimuli and using these RFs to predict responses to novel stimuli.
PMCID: PMC2518112  PMID: 18725977
7.  Adaptation to stimulus contrast and correlations during natural visual stimulation 
Neuron  2007;55(3):479-491.
In this study, we characterize the adaptation of neurons in the cat lateral geniculate nucleus to changes in stimulus contrast and correlations. By comparing responses to high and low contrast natural scene movie and white noise stimuli, we show that an increase in contrast or correlations results in receptive fields with faster temporal dynamics and stronger antagonistic surrounds, as well as decreases in gain and selectivity. We also observe contrast- and correlation-induced changes in the reliability and sparseness of neural responses. We find that reliability is determined primarily by processing in the receptive field (the effective contrast of the stimulus), while sparseness is determined by the interactions between several functional properties. These results reveal a number of novel adaptive phenomena and suggest that adaptation to stimulus contrast and correlations may play an important role in visual coding in a dynamic natural environment.
PMCID: PMC1994647  PMID: 17678859
8.  Efficient Temporal Processing of Naturalistic Sounds 
PLoS ONE  2008;3(2):e1655.
In this study, we investigate the ability of the mammalian auditory pathway to adapt its strategy for temporal processing under natural stimulus conditions. We derive temporal receptive fields from the responses of neurons in the inferior colliculus to vocalization stimuli with and without additional ambient noise. We find that the onset of ambient noise evokes a change in receptive field dynamics that corresponds to a change from bandpass to lowpass temporal filtering. We show that these changes occur within a few hundred milliseconds of the onset of the noise and are evident across a range of overall stimulus intensities. Using a simple model, we illustrate how these changes in temporal processing exploit differences in the statistical properties of vocalizations and ambient noises to increase the information in the neural response in a manner consistent with the principles of efficient coding.
PMCID: PMC2249929  PMID: 18301738
9.  Dynamic Encoding of Natural Luminance Sequences by LGN Bursts 
PLoS Biology  2006;4(7):e209.
In the lateral geniculate nucleus (LGN) of the thalamus, visual stimulation produces two distinct types of responses known as tonic and burst. Due to the dynamics of the T-type Ca 2+ channels involved in burst generation, the type of response evoked by a particular stimulus depends on the resting membrane potential, which is controlled by a network of modulatory connections from other brain areas. In this study, we use simulated responses to natural scene movies to describe how modulatory and stimulus-driven changes in LGN membrane potential interact to determine the luminance sequences that trigger burst responses. We find that at low resting potentials, when the T channels are de-inactivated and bursts are relatively frequent, an excitatory stimulus transient alone is sufficient to evoke a burst. However, to evoke a burst at high resting potentials, when the T channels are inactivated and bursts are relatively rare, prolonged inhibitory stimulation followed by an excitatory transient is required. We also observe evidence of these effects in vivo, where analysis of experimental recordings demonstrates that the luminance sequences that trigger bursts can vary dramatically with the overall burst percentage of the response. To characterize the functional consequences of the effects of resting potential on burst generation, we simulate LGN responses to different luminance sequences at a range of resting potentials with and without a mechanism for generating bursts. Using analysis based on signal detection theory, we show that bursts enhance detection of specific luminance sequences, ranging from the onset of excitatory sequences at low resting potentials to the offset of inhibitory sequences at high resting potentials. These results suggest a dynamic role for burst responses during visual processing that may change according to behavioral state.
This visual neuroscience paper simulates how resting potential and stimulus driven modulations in membrane potential interact to determine the response mode of LGN neurons to natural images.
PMCID: PMC1475766  PMID: 16756389

Results 1-9 (9)