Kamal Sen

Cortical Discrimination of Complex Natural Stimuli: Can Single Neurons Match Behavior?

A central finding in many cortical areas is that single neurons can match behavioral performance in the discrimination of sensory stimuli. However, whether this is true for natural behaviors involving complex natural stimuli remains unknown. Here we use the model system of songbirds to address this problem. Specifically, we investigate whether neurons in field L, the homolog of primary auditory cortex, can match behavioral performance in the discrimination of conspecific songs. We use a classification framework based on the (dis)similarity between single spike trains to quantify neural discrimination. We use this framework to investigate the discriminability of single spike trains in field L in response to conspecific songs, testing different candidate neural codes underlying discrimination. We find that performance based on spike timing is significantly higher than performance based on spike rate and interspike intervals. We then assess the impact of temporal correlations in spike trains on discrimination. In contrast to widely discussed effects of correlations in limiting the accuracy of a population code, temporal correlations appear to improve the performance of single neurons in the majority of cases. Finally, we compare neural performance with behavioral performance. We find a diverse range of performance levels in field L, with neural performance matching behavioral accuracy only for the best neurons using a spike-timing-based code.

A new multineuron spike train metric

The Victor-Purpura spike train metric has recently been extended to a family of multineuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The metric is one of the two metrics commonly used for quantifying the distance between two spike trains; the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multineuron metric. We believe this gives a metric that is both natural and easy to calculate. Both types of multineuron metric are applied to simulated data and are compared.

Cortical interference effects in the cocktail party problem

Humans and animals must often discriminate between complex natural sounds in the presence of competing sounds (maskers). Although the auditory cortex is thought to be important in this task, the impact of maskers on cortical discrimination remains poorly understood. We examined neural responses in zebra finch (Taeniopygia guttata) field L (homologous to primary auditory cortex) to target birdsongs that were embedded in three different maskers (broadband noise, modulated noise and birdsong chorus). We found two distinct forms of interference in the neural responses: the addition of spurious spikes occurring primarily during the silent gaps between song syllables and the suppression of informative spikes occurring primarily during the syllables. Both effects systematically degraded neural discrimination as the target intensity decreased relative to that of the masker. The behavioral performance of songbirds degraded in a parallel manner. Our results identify neural interference that could explain the perceptual interference at the heart of the cocktail party problem.

Invariance and Sensitivity to Intensity in Neural Discrimination of Natural Sounds

Intensity variation poses a fundamental problem for sensory discrimination because changes in the response of sensory neurons as a result of stimulus identity, e.g., a change in the identity of the speaker uttering a word, can potentially be confused with changes resulting from stimulus intensity, for example, the loudness of the utterance. Here we report on the responses of neurons in field L, the primary auditory cortex homolog in songbirds, which allow for accurate discrimination of birdsongs that is invariant to intensity changes over a large range. Such neurons comprise a subset of a population that is highly diverse, in terms of both discrimination accuracy and intensity sensitivity. We find that the neurons with a high degree of invariance also display a high discrimination performance, and that the degree of invariance is significantly correlated with the reproducibility of spike timing on a short time scale and the temporal sparseness of spiking activity. Our results indicate that a temporally sparse spike timing-based code at a primary cortical stage can provide a substrate for intensity-invariant discrimination of natural sounds.

Spatial unmasking of birdsong in human listeners: Energetic and informational factors

Spatial unmasking describes the improvement in the detection or identification of a target sound afforded by separating it spatially from simultaneous masking sounds. This effect has been studied extensively for speech intelligibility in the presence of interfering sounds. In the current study, listeners identified zebra finch song, which shares many acoustic properties with speech but lacks semantic and linguistic content. Three maskers with the same long-term spectral content but different short-term statistics were used: (1) chorus (combinations of unfamiliar zebra finch songs), (2) song-shaped noise (broadband noise with the average spectrum of chorus), and (3) chorus-modulated noise (song-shaped noise multiplied by the broadband envelope from a chorus masker). The amount of masking and spatial unmasking depended on the masker and there was evidence of release from both energetic and informational masking. Spatial unmasking was greatest for the statistically similar chorus masker. For the two noise maskers, there was less spatial unmasking and it was wholly accounted for by the relative target and masker levels at the acoustically better ear. The results share many features with analogous results using speech targets, suggesting that spatial separation aids in the segregation of complex natural sounds through mechanisms that are not specific to speech.

Cortical Gamma Rhythms Modulate NMDAR-Mediated Spike Timing Dependent Plasticity in a Biophysical Model

Spike timing dependent plasticity (STDP) has been observed experimentally in vitro and is a widely studied neural algorithm for synaptic modification. While the functional role of STDP has been investigated extensively, the effect of rhythms on the precise timing of STDP has not been characterized as well. We use a simplified biophysical model of a cortical network that generates pyramidal interneuronal gamma rhythms (PING). Plasticity via STDP is investigated at the excitatory pyramidal cell synapse from a gamma frequency (30–90 Hz) input independent of the network gamma rhythm. The input may represent a corticocortical or an information-specific thalamocortical connection. This synapse is mediated by N-methyl-D-aspartate receptor mediated (NMDAR) currents. For distinct network and input frequencies, the model shows robust frequency regimes of potentiation and depression, providing a mechanism by which responses to certain inputs can potentiate while responses to other inputs depress. For potentiating regimes, the model suggests an optimal amount and duration of plasticity that can occur, which depends on the time course for the decay of the postsynaptic NMDAR current. Prolonging the duration of the input beyond this optimal time results in depression. Inserting pauses in the input can increase the total potentiation. The optimal pause length corresponds to the decay time of the NMDAR current. Thus, STDP in this model provides a mechanism for potentiation and depression depending on input frequency and suggests that the slow NMDAR current decay helps to regulate the optimal amplitude and duration of the plasticity. The optimal pause length is comparable to the time scale of the negative phase of a modulatory theta rhythm, which may pause gamma rhythm spiking. Our pause results may suggest a novel role for this theta rhythm in plasticity. Finally, we discuss our results in the context of auditory thalamocortical plasticity.

Spatial Unmasking of Birdsong in Zebra Finches (Taeniopygia guttata) and Budgerigars (Melopsittacus undulatus)

Budgerigars and zebra finches were tested, using operant conditioning techniques, on their ability to identify a zebra finch song in the presence of a background masker emitted from either the same or a different location as the signal. Identification thresholds were obtained for three masker types differing in their spectrotemporal characteristics (noise, modulated noise, and a song chorus). Both bird species exhibited similar amounts of spatial unmasking across the three masker types. The amount of unmasking was greater when the masker was played continuously compared to when the target and masker were presented simultaneously. These results suggest that spatial factors are important for birds in the identification of natural signals in noisy environments.

A biologically plausible computational model for auditory object recognition.

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

Competing sound sources reveal spatial effects in cortical processing.

Neurons in the avian auditory forebrain show strong sensitivity to the spatial configuration of two competing sources, even though there is only weak spatial dependence for any single source.

Online Stimulus Optimization Rapidly Reveals Multidimensional Selectivity in Auditory Cortical Neurons

Neurons in sensory brain regions shape our perception of the surrounding environment through two parallel operations: decomposition and integration. For example, auditory neurons decompose sounds by separately encoding their frequency, temporal modulation, intensity, and spatial location. Neurons also integrate across these various features to support a unified perceptual gestalt of an auditory object. At higher levels of a sensory pathway, neurons may select for a restricted region of feature space defined by the intersection of multiple, independent stimulus dimensions. To further characterize how auditory cortical neurons decompose and integrate multiple facets of an isolated sound, we developed an automated procedure that manipulated five fundamental acoustic properties in real time based on single-unit feedback in awake mice. Within several minutes, the online approach converged on regions of the multidimensional stimulus manifold that reliably drove neurons at significantly higher rates than predefined stimuli. Optimized stimuli were cross-validated against pure tone receptive fields and spectrotemporal receptive field estimates in the inferior colliculus and primary auditory cortex. We observed, from midbrain to cortex, increases in both level invariance and frequency selectivity, which may underlie equivalent sparseness of responses in the two areas. We found that onset and steady-state spike rates increased proportionately as the stimulus was tailored to the multidimensional receptive field. By separately evaluating the amount of leverage each sound feature exerted on the overall firing rate, these findings reveal interdependencies between stimulus features as well as hierarchical shifts in selectivity and invariance that may go unnoticed with traditional approaches.