Analyzing variability in neural responses to complex natural sounds in the awake songbird

Variations on a theme: Songbirds, variability, and sensorimotor error correction

Songbirds provide a powerful animal model for investigating how the brain uses sensory feedback to correct behavioral errors. Here, we review a recent study in which we used online manipulations of auditory feedback to quantify the relationship between sensory error size, motor variability, and vocal plasticity. We found that although inducing small auditory errors evoked relatively large compensatory changes in behavior, as error size increased the magnitude of error correction declined. Furthermore, when we induced large errors such that auditory signals no longer overlapped with the baseline distribution of feedback, the magnitude of error correction approached zero. This pattern suggests a simple and robust strategy for the brain to maintain the accuracy of learned behaviors by evaluating sensory signals relative to the previously experienced distribution of feedback. Drawing from recent studies of auditory neurophysiology and song discrimination, we then speculate as to the mechanistic underpinnings of the results obtained in our behavioral experiments. Finally, we review how our own and other studies exploit the strengths of the songbird system, both in the specific context of vocal systems and more generally as a model of the neural control of complex behavior.

Variability and information content in auditory cortex spike trains during an interval-discrimination task

Processing of temporal information is key in auditory processing. In this study, we recorded single-unit activity from rat auditory cortex while they performed an interval-discrimination task. The animals had to decide whether two auditory stimuli were separated by either 150 or 300 ms and nose-poke to the left or to the right accordingly. The spike firing of single neurons in the auditory cortex was then compared in engaged vs. idle brain states. We found that spike firing variability measured with the Fano factor was markedly reduced, not only during stimulation, but also in between stimuli in engaged trials. We next explored if this decrease in variability was associated with an increased information encoding. Our information theory analysis revealed increased information content in auditory responses during engagement compared with idle states, in particular in the responses to task-relevant stimuli. Altogether, we demonstrate that task-engagement significantly modulates coding properties of auditory cortical neurons during an interval-discrimination task.

Linear and nonlinear auditory response properties of interneurons in a high-order avian vocal motor nucleus during wakefulness

Motor-related forebrain areas in higher vertebrates also show responses to passively presented sensory stimuli. However, sensory tuning properties in these areas, especially during wakefulness, and their relation to perception, are poorly understood. In the avian song system, HVC (proper name) is a vocal-motor structure with auditory responses well defined under anesthesia but poorly characterized during wakefulness. We used a large set of stimuli including the bird's own song (BOS) and many conspecific songs (CON) to characterize auditory tuning properties in putative interneurons (HVC(IN)) during wakefulness. Our findings suggest that HVC contains a diversity of responses that vary in overall excitability to auditory stimuli, as well as bias in spike rate increases to BOS over CON. We used statistical tests to classify cells in order to further probe auditory responses, yielding one-third of neurons that were either unresponsive or suppressed and two-thirds with excitatory responses to one or more stimuli. A subset of excitatory neurons were tuned exclusively to BOS and showed very low linearity as measured by spectrotemporal receptive field analysis (STRF). The remaining excitatory neurons responded well to CON stimuli, although many cells still expressed a bias toward BOS. These findings suggest the concurrent presence of a nonlinear and a linear component to responses in HVC, even within the same neuron. These characteristics are consistent with perceptual deficits in distinguishing BOS from CON stimuli following lesions of HVC and other song nuclei and suggest mirror neuronlike qualities in which "self" (here BOS) is used as a referent to judge "other" (here CON).

Neuron-specific stimulus masking reveals interference in spike timing at the cortical level

The auditory system is capable of robust recognition of sounds in the presence of competing maskers (e.g., other voices or background music). This capability arises despite the fact that masking stimuli can disrupt neural responses at the cortical level. Since the origins of such interference effects remain unknown, in this study, we work to identify and quantify neural interference effects that originate due to masking occurring within and outside receptive fields of neurons. We record from single and multi-unit auditory sites from field L, the auditory cortex homologue in zebra finches. We use a novel method called spike timing-based stimulus filtering that uses the measured response of each neuron to create an individualized stimulus set. In contrast to previous adaptive experimental approaches, which have typically focused on the average firing rate, this method uses the complete pattern of neural responses, including spike timing information, in the calculation of the receptive field. When we generate and present novel stimuli for each neuron that mask the regions within the receptive field, we find that the time-varying information in the neural responses is disrupted, degrading neural discrimination performance and decreasing spike timing reliability and sparseness. We also find that, while removing stimulus energy from frequency regions outside the receptive field does not significantly affect neural responses for many sites, adding a masker in these frequency regions can nonetheless have a significant impact on neural responses and discriminability without a significant change in the average firing rate. These findings suggest that maskers can interfere with neural responses by disrupting stimulus timing information with power either within or outside the receptive fields of neurons.

Voltage fluctuations in neurons: signal or noise?

Noise and variability are fundamental companions to ion channels and synapses and thus inescapable elements of brain function. The overriding unresolved issue is to what extent noise distorts and limits signaling on one hand and at the same time constitutes a crucial and fundamental enrichment that allows and facilitates complex adaptive behavior in an unpredictable world. Here we review the growing experimental evidence that functional network activity is associated with intense fluctuations in membrane potential and spike timing. We trace origins and consequences of noise and variability. Finally, we discuss noise-free neuronal signaling and detrimental and beneficial forms of noise in large-scale functional neural networks. Evidence that noise and variability in some cases go hand in hand with behavioral variability and increase behavioral choice, richness, and adaptability opens new avenues for future studies.

A robust and biologically plausible spike pattern recognition network

The neural mechanisms that enable recognition of spiking patterns in the brain are currently unknown. This is especially relevant in sensory systems, in which the brain has to detect such patterns and recognize relevant stimuli by processing peripheral inputs; in particular, it is unclear how sensory systems can recognize time-varying stimuli by processing spiking activity. Because auditory stimuli are represented by time-varying fluctuations in frequency content, it is useful to consider how such stimuli can be recognized by neural processing. Previous models for sound recognition have used preprocessed or low-level auditory signals as input, but complex natural sounds such as speech are thought to be processed in auditory cortex, and brain regions involved in object recognition in general must deal with the natural variability present in spike trains. Thus, we used neural recordings to investigate how a spike pattern recognition system could deal with the intrinsic variability and diverse response properties of cortical spike trains. We propose a biologically plausible computational spike pattern recognition model that uses an excitatory chain of neurons to spatially preserve the temporal representation of the spike pattern. Using a single neural recording as input, the model can be trained using a spike-timing-dependent plasticity-based learning rule to recognize neural responses to 20 different bird songs with >98% accuracy and can be stimulated to evoke reverse spike pattern playback. Although we test spike train recognition performance in an auditory task, this model can be applied to recognize sufficiently reliable spike patterns from any neuronal system.