Nicholas A. Lesica

Temporal precision in the neural code and the timescales of natural vision.

The timing of action potentials relative to sensory stimuli can be precise down to milliseconds in the visual system, even though the relevant timescales of natural vision are much slower. The existence of such precision contributes to a fundamental debate over the basis of the neural code and, specifically, what timescales are important for neural computation. Using recordings in the lateral geniculate nucleus, here we demonstrate that the relevant timescale of neuronal spike trains depends on the frequency content of the visual stimulus, and that ‘relative’, not absolute, precision is maintained both during spatially uniform white-noise visual stimuli and naturalistic movies. Using information-theoretic techniques, we demonstrate a clear role of relative precision, and show that the experimentally observed temporal structure in the neuronal response is necessary to represent accurately the more slowly changing visual world. By establishing a functional role of precision, we link visual neuron function on slow timescales to temporal structure in the response at faster timescales, and uncover a straightforward purpose of fine-timescale features of neuronal spike trains.

Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex.

Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input from nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways.Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input from nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways.

Encoding of Natural Scene Movies by Tonic and Burst Spikes in the Lateral Geniculate Nucleus

The role of the lateral geniculate nucleus (LGN) of the thalamus in visual encoding remains an open question. Here, we characterize the function of tonic and burst spikes in cat LGN X-cells in signaling features of natural stimuli. A significant increase in bursting was observed during natural stimulation (relative to white noise stimulation) and was linked to the strong correlation structure of the natural scene movies. Burst responses were triggered by specific stimulus events consisting of a prolonged inhibitory stimulus, followed by an excitatory stimulus, such as the movement of an object into the receptive field. LGN responses to natural scene movies were predicted using an integrate-and-fire (IF) framework and compared with experimentally observed responses. The standard IF model successfully predicted LGN responses to natural scene movies during tonic firing, indicating a linear relationship between stimulus and response. However, the IF model typically underpredicted the LGN response during periods of bursting, indicating a nonlinear amplification of the stimulus in the actual response. The addition of a burst mechanism to the IF model was necessary to accurately predict the entire LGN response. These results suggest that LGN bursts are an important part of the neural code, providing a nonlinear amplification of stimulus features that are typical of the natural environment.

Adaptation to stimulus contrast and correlations during natural visual stimulation

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 adaptive phenomena and suggest that adaptation to stimulus contrast and correlations may play an important role in visual coding in a dynamic natural environment.

Dynamic encoding of natural luminance sequences by LGN bursts

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.

State-dependent population coding in primary auditory cortex.

Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and desynchronized cortical states, i.e., in the presence or absence of intrinsically generated up and down states. Here we investigated the impact of cortical state on the population coding of tones and speech in the primary auditory cortex (A1) of gerbils, and found that responses were qualitatively different in synchronized and desynchronized cortical states. Activity in synchronized A1 was only weakly modulated by sensory input, and the spike patterns evoked by tones and speech were unreliable and constrained to a small range of patterns. In contrast, responses to tones and speech in desynchronized A1 were temporally precise and reliable across trials, and different speech tokens evoked diverse spike patterns with extremely weak noise correlations, allowing responses to be decoded with nearly perfect accuracy. Restricting the analysis of synchronized A1 to activity within up states yielded similar results, suggesting that up states are not equivalent to brief periods of desynchronization. These findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.

Timing Precision in Population Coding of Natural Scenes in the Early Visual System

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.

Dynamic Spectrotemporal Feature Selectivity in the Auditory Midbrain

The transformation of auditory information from the cochlea to the cortex is a highly nonlinear process. Studies using tone stimuli have revealed that changes in even the most basic parameters of the auditory stimulus can alter neural response properties; for example, a change in stimulus intensity can cause a shift in a neurons preferred frequency. However, it is not yet clear how such nonlinearities contribute to the processing of spectrotemporal features in complex sounds. Here, we use spectrotemporal receptive fields (STRFs) to characterize the effects of stimulus intensity on feature selectivity in the mammalian inferior colliculus (IC). At low intensities, we find that STRFs are relatively simple, typically consisting of a single excitatory region, indicating that the neural response is simply a reflection of the stimulus amplitude at the preferred frequency. In contrast, we find that STRFs at high intensities typically consist of a combination of an excitatory region and one or more inhibitory regions, often in a spectrotemporally inseparable arrangement, indicating selectivity for complex auditory features. We show that a linear–nonlinear model with the appropriate STRF can predict neural responses to stimuli with a fixed intensity, and we demonstrate that a simple extension of the model with an intensity-dependent STRF can predict responses to stimuli with varying intensity. These results illustrate the complexity of auditory feature selectivity in the IC, but also provide encouraging evidence that the prediction of nonlinear responses to complex stimuli is a tractable problem.

Efficient temporal processing of naturalistic sounds.

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.

Population Coding of Interaural Time Differences in Gerbils and Barn Owls

Interaural time differences (ITDs) are the primary cue for the localization of low-frequency sound sources in the azimuthal plane. For decades, it was assumed that the coding of ITDs in the mammalian brain was similar to that in the avian brain, where information is sparsely distributed across individual neurons, but recent studies have suggested otherwise. In this study, we characterized the representation of ITDs in adult male and female gerbils. First, we performed behavioral experiments to determine the acuity with which gerbils can use ITDs to localize sounds. Next, we used different decoders to infer ITDs from the activity of a population of neurons in central nucleus of the inferior colliculus. These results show that ITDs are not represented in a distributed manner, but rather in the summed activity of the entire population. To contrast these results with those from a population where the representation of ITDs is known to be sparsely distributed, we performed the same analysis on activity from the external nucleus of the inferior colliculus of adult male and female barn owls. Together, our results support the idea that, unlike the avian brain, the mammalian brain represents ITDs in the overall activity of a homogenous population of neurons within each hemisphere.