Cyrus P. Billimoria

Mass spectrometric investigation of the neuropeptide complement and release in the pericardial organs of the crab, Cancer borealis

The crustacean stomatogastric ganglion (STG) is modulated by both locally released neuroactive compounds and circulating hormones. This study presents mass spectrometric characterization of the complement of peptide hormones present in one of the major neurosecretory structures, the pericardial organs (POs), and the detection of neurohormones released from the POs. Direct peptide profiling of Cancer borealis PO tissues using matrix‐assisted laser desorption/ionization (MALDI) time‐of‐flight (TOF) mass spectrometry (MS) revealed many previously identified peptides, including proctolin, red pigment concentrating hormone (RPCH), crustacean cardioactive peptide (CCAP), several orcokinins, and SDRNFLRFamide. This technique also detected corazonin, a well‐known insect hormone, in the POs for the first time. However, most mass spectral peaks did not correspond to previously known peptides. To characterize and identify these novel peptides, we performed MALDI postsource decay (PSD) and electrospray ionization (ESI) MS/MS de novo sequencing of peptides fractionated from PO extracts. We characterized a truncated form of previously identified TNRNFLRFamide, NRNFLRFamide. In addition, we sequenced five other novel peptides sharing a common C‐terminus of RYamide from the PO tissue extracts. High K+ depolarization of isolated POs released many peptides present in this tissue, including several of the novel peptides sequenced in the current study.

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.

Neuromodulation of spike-timing precision in sensory neurons.

The neuropeptide allatostatin decreases the spike rate in response to time-varying stretches of two different crustacean mechanoreceptors, the gastropyloric receptor 2 in the crab Cancer borealis and the coxobasal chordotonal organ (CBCTO) in the crab Carcinus maenas. In each system, the decrease in firing rate is accompanied by an increase in the timing precision of spikes triggered by discrete temporal features in the stimulus. This was quantified by calculating the standard deviation or “jitter” in the times of individual identified spikes elicited in response to repeated presentations of the stimulus. Conversely, serotonin increases the firing rate but decreases the timing precision of the CBCTO response. Intracellular recordings from the afferents of this receptor demonstrate that allatostatin increases the conductance of the neurons, consistent with its inhibitory action on spike rate, whereas serotonin decreases the overall membrane conductance. We conclude that spike-timing precision of mechanoreceptor afferents in response to dynamic stimulation can be altered by neuromodulators acting directly on the afferent neurons.

Profiling of neuropeptides released at the stomatogastric ganglion of the crab, Cancer borealis with mass spectrometry

Studies of release under physiological conditions provide more direct data about the identity of neuromodulatory signaling molecules than studies of tissue localization that cannot distinguish between processing precursors and biologically active neuropeptides. We have identified neuropeptides released by electrical stimulation of nerves that contain the axons of the modulatory projection neurons to the stomatogastric ganglion of the crab, Cancer borealis. Preparations were bathed in saline containing a cocktail of peptidase inhibitors to minimize peptide degradation. Both electrical stimulation of projection nerves and depolarization with high K+ saline were used to evoke release. Releasates were desalted and then identified by mass using MALDI–TOF (matrix‐assisted laser desorption/ionization–time‐of‐flight) mass spectrometry. Both previously known and novel peptides were detected. Subsequent to electrical stimulation proctolin, Cancer borealis tachykinin‐related peptide (CabTRP), FVNSRYa, carcinustatin‐8, allatostatin‐3 (AST‐3), red pigment concentrating hormone, NRNFLRFa, AST‐5, SGFYANRYa, TNRNFLRFa, AST‐9, orcomyotropin‐related peptide, corazonin, Ala13‐orcokinin, and Ser9‐Val13‐orcokinin were detected. Some of these were also detected after high K+ depolarization. Release was calcium dependent. In summary, we have shown release of the neuropeptides thought to play an important neuromodulatory role in the stomatogastric ganglion, as well as numerous other candidate neuromodulators that remain to be identified.

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.

Analyzing Variability in Neural Responses to Complex Natural Sounds in the Awake Songbird

Studies of auditory processing in awake, behaving songbirds allow for the possibility of new classes of experiments, including those involving attention and plasticity. Detecting and determining the significance of plasticity, however, requires assessing the intrinsic variability in neural responses. Effects such as rapid plasticity have been investigated in the auditory system through the use of the spectrotemporal receptive field (STRF), a characterization of the properties of sounds to which a neuron best responds. Here we investigated neural response variability in awake recordings obtained from zebra finch field L, the analog of the primary auditory cortex. To quantify the level of variability in the neural recordings, we used three similarity measures: an STRF-based metric, a spike-train correlation-based metric, and a spike-train discrimination-based metric. We then extracted a number of parameters from these measures, quantifying how they fluctuated over time. Our results indicate that 1) awake responses are quite stable over time; 2) the different measures of response are complementary-specifically, the spike-train-based measures yield new information complementary to the STRF; and 3) different STRF parameters show distinct levels of variability. These results provide critical constraints for the design of robust decoding strategies and novel experiments on attention and plasticity in the awake songbird.

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.

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.

Auditory forebrain neurons track temporal features of time-warped natural stimuli.

ABSTRACTA fundamental challenge for sensory systems is to recognize natural stimuli despite stimulus variations. A compelling example occurs in speech, where the auditory system can recognize words spoken at a wide range of speeds. To date, there have been more computational models for time-warp invariance than experimental studies that investigate responses to time-warped stimuli at the neural level. Here, we address this problem in the model system of zebra finches anesthetized with urethane. In behavioral experiments, we found high discrimination accuracy well beyond the observed natural range of song variations. We artificially sped up or slowed down songs (preserving pitch) and recorded auditory responses from neurons in field L, the avian primary auditory cortex homolog. We found that field L neurons responded robustly to time-warped songs, tracking the temporal features of the stimuli over a broad range of warp factors. Time-warp invariance was not observed per se, but there was sufficient information in the neural responses to reliably classify which of two songs was presented. Furthermore, the average spike rate was close to constant over the range of time warps, contrary to recent modeling predictions. We discuss how this response pattern is surprising given current computational models of time-warp invariance and how such a response could be decoded downstream to achieve time-warp-invariant recognition of sounds.