Millisecond-scale motor encoding in a cortical vocal area

Neuronal activation sequences in lateral prefrontal cortex encode visuospatial working memory during virtual navigation

Working memory (WM) is the ability to maintain and manipulate information 'in mind'. The neural codes underlying WM have been a matter of debate. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of male macaque monkeys during a visuospatial WM task that required navigation in a virtual 3D environment. Here, we demonstrate distinct neuronal activation sequences (NASs) that encode remembered target locations in the virtual environment. This NAS code outperformed the persistent firing code for remembered locations during the virtual reality task, but not during a classical WM task using stationary stimuli and constraining eye movements. Finally, blocking NMDA receptors using low doses of ketamine deteriorated the NAS code and behavioral performance selectively during the WM task. These results reveal the versatility and adaptability of neural codes supporting working memory function in the primate lateral prefrontal cortex.

An information theoretic method to resolve millisecond-scale spike timing precision in a comprehensive motor program

Sensory inputs in nervous systems are often encoded at the millisecond scale in a precise spike timing code. There is now growing evidence in behaviors ranging from slow breathing to rapid flight for the prevalence of precise timing encoding in motor systems. Despite this, we largely do not know at what scale timing matters in these circuits due to the difficulty of recording a complete set of spike-resolved motor signals and assessing spike timing precision for encoding continuous motor signals. We also do not know if the precision scale varies depending on the functional role of different motor units. We introduce a method to estimate spike timing precision in motor circuits using continuous MI estimation at increasing levels of added uniform noise. This method can assess spike timing precision at fine scales for encoding rich motor output variation. We demonstrate the advantages of this approach compared to a previously established discrete information theoretic method of assessing spike timing precision. We use this method to analyze the precision in a nearly complete, spike resolved recording of the 10 primary wing muscles control flight in an agile hawk moth, Manduca sexta. Tethered moths visually tracked a robotic flower producing a range of turning (yaw) torques. We know that all 10 muscles in this motor program encode the majority of information about yaw torque in spike timings, but we do not know whether individual muscles encode motor information at different levels of precision. We demonstrate that the scale of temporal precision in all motor units in this insect flight circuit is at the sub-millisecond or millisecond-scale, with variation in precision scale present between muscle types. This method can be applied broadly to estimate spike timing precision in sensory and motor circuits in both invertebrates and vertebrates.

Rhythmically bursting songbird vocomotor neurons are organized into multiple sequences, suggesting a network/intrinsic properties model encoding song and error, not time

In zebra finch, basal ganglia projecting "HVC _X " neurons emit one or more spike bursts during each song motif (canonical sequence of syllables), which are thought to be driven in part by a process of spike rebound excitation. Zebra finch songs are highly stereotyped and recent results indicate that the intrinsic properties of HVC _X neurons are similar within each bird, vary among birds depending on similarity of the songs, and vary with song errors. We tested the hypothesis that the timing of spike bursts during singing also evince individual-specific distributions. Examining previously published data, we demonstrated that the intervals between bursts of multibursting HVC _X are similar for neurons within each bird, in many cases highly clustered at distinct peaks, with the patterns varying among birds. The fixed delay between bursts and different times when neurons are first recruited in the song yields precisely timed multiple sequences of bursts throughout the song, not the previously envisioned single sequence of bursts treated as events having statistically independent timing. A given moment in time engages multiple sequences and both single bursting and multibursting HVC _X simultaneously. This suggests a model where a population of HVC _X sharing common intrinsic properties driving spike rebound excitation influence the timing of a given HVC _X burst through lateral inhibitory interactions. Perturbations in burst timing, representing error, could propagate in time. Our results extend the concept of central pattern generators to complex vertebrate vocal learning and suggest that network activity (timing of inhibition) and HVC _X intrinsic properties become coordinated during developmental birdsong learning.

Volitional Generation of Reproducible, Efficient Temporal Patterns

One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.

The quest for multiscale brain modeling

Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies.

Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries

The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein's function based on its sequence, we still do not understand how to accurately predict an organism's behavior based on neural activity. Here we introduce the unsupervised Bayesian Ising Approximation (uBIA) for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. In data recorded during singing from neurons in a vocal control region, our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets.

One-to-one innervation of vocal muscles allows precise control of birdsong

The motor control resolution of any animal behavior is limited to the minimal force step available when activating muscles, which is set by the number and size distribution of motor units (MUs) and muscle-specific force. Birdsong is an excellent model system for understanding acquisition and maintenance of complex fine motor skills, but we know surprisingly little about how the motor pool controlling the syrinx is organized and how MU recruitment drives changes in vocal output. Here we developed an experimental paradigm to measure MU size distribution using spatiotemporal imaging of intracellular calcium concentration in cross-sections of living intact syrinx muscles. We combined these measurements with muscle stress and an in vitro syrinx preparation to determine the control resolution of fundamental frequency (f_o), a key vocal parameter, in zebra finches. We show that syringeal muscles have extremely small MUs, with 40%-50% innervating ≤3 and 13%-17% innervating a single muscle fiber. Combined with the lowest specific stress (5 mN/mm²) known to skeletal vertebrate muscle, small force steps by the major f_o controlling muscle provide control of 50-mHz to 7.3-Hz steps per MU. We show that the song system has the highest motor control resolution possible in the vertebrate nervous system and suggest this evolved due to strong selection on fine gradation of vocal output. Furthermore, we propose that high-resolution motor control was a key feature contributing to the radiation of songbirds that allowed diversification of song and speciation by vocal space expansion.

Statistical structure of the trial-to-trial timing variability in synfire chains

Timing and its variability are crucial for behavior. Consequently, neural circuits that take part in the control of timing and in the measurement of temporal intervals have been the subject of much research. Here we provide an analytical and computational account of the temporal variability in what is perhaps the most basic model of a timing circuit-the synfire chain. First we study the statistical structure of trial-to-trial timing variability in a reduced but analytically tractable model: a chain of single integrate-and-fire neurons. We show that this circuit's variability is well described by a generative model consisting of local, global, and jitter components. We relate each of these components to distinct neural mechanisms in the model. Next we establish in simulations that these results carry over to a noisy homogeneous synfire chain. Finally, motivated by the fact that a synfire chain is thought to underlie the circuit that takes part in the control and timing of the zebra finch song, we present simulations of a biologically realistic synfire chain model of the zebra finch timekeeping circuit. We find the structure of trial-to-trial timing variability to be consistent with our previous findings and to agree with experimental observations of the song's temporal variability. Our study therefore provides a possible neuronal account of behavioral variability in zebra finches.

Variable but not random: temporal pattern coding in a songbird brain area necessary for song modification

Practice of a complex motor gesture involves motor exploration to attain a better match to target, but little is known about the neural code for such exploration. We examine spiking in a premotor area of the songbird brain critical for song modification and quantify correlations between spiking and time in the motor sequence. While isolated spikes code for time in song during performance of song to a female bird, extended strings of spiking and silence, particularly bursts, code for time in song during undirected (solo) singing, or "practice." Bursts code for particular times in song with more information than individual spikes, and this spike-spike synergy is significantly higher during undirected singing. The observed pattern information cannot be accounted for by a Poisson model with a matched time-varying rate, indicating that the precise timing of spikes in both bursts in undirected singing and isolated spikes in directed singing code for song with a temporal code. Temporal coding during practice supports the hypothesis that lateral magnocellular nucleus of the anterior nidopallium neurons actively guide song modification at local instances in time.NEW & NOTEWORTHY This paper shows that bursts of spikes in the songbird brain during practice carry information about the output motor pattern. The brain's code for song changes with social context, in performance versus practice. Synergistic combinations of spiking and silence code for time in the bird's song. This is one of the first uses of information theory to quantify neural information about a motor output. This activity may guide changes to the song.

Increasing Muscle Speed Drives Changes in the Neuromuscular Transform of Motor Commands during Postnatal Development in Songbirds

Progressive changes in vocal behavior over the course of vocal imitation leaning are often attributed exclusively to developing neural circuits, but the effects of postnatal body changes remain unknown. In songbirds, the syrinx transforms song system motor commands into sound and exhibits changes during song learning. Here we test the hypothesis that the transformation from motor commands to force trajectories by syringeal muscles functionally changes over vocal development in zebra finches. Our data collected in both sexes show that, only in males, muscle speed significantly increases and that supralinear summation occurs and increases with muscle contraction speed. Furthermore, we show that previously reported submillisecond spike timing in the avian cortex can be resolved by superfast syringeal muscles and that the sensitivity to spike timing increases with speed. Because motor neuron and muscle properties are tightly linked, we make predictions on the boundaries of the yet unknown motor code that correspond well with cortical activity. Together, we show that syringeal muscles undergo essential transformations during song learning that drastically change how neural commands are translated into force profiles and thereby acoustic features. We propose that the song system motor code must compensate for these changes to achieve its acoustic targets. Our data thus support the hypothesis that the neuromuscular transformation changes over vocal development and emphasizes the need for an embodied view of song motor learning.SIGNIFICANCE STATEMENT Fine motor skill learning typically occurs in a postnatal period when the brain is learning to control a body that is changing dramatically due to growth and development. How the developing body influences motor code formation and vice versa remains largely unknown. Here we show that vocal muscles in songbirds undergo critical transformations during song learning that drastically change how neural commands are translated into force profiles and thereby acoustic features. We propose that the motor code must compensate for these changes to achieve its acoustic targets. Our data thus support the hypothesis that the neuromuscular transformation changes over vocal development and emphasizes the need for an embodied view of song motor learning.

Flexible Multielectrode Arrays With 2-D and 3-D Contacts for In Vivo Electromyography Recording

We present a system for recording in vivo electromyographic (EMG) signals from songbirds using hybrid polyimide-polydimethylsiloxane (PDMS) flexible multielectrode arrays (MEAs). 2-D electrodes with a diameter of 200, 125, and 50 μm and a center-to-center pitch of 300, 200, and 100 μm, respectively, were fabricated. 3-D MEAs were fabricated using a photoresist reflow process to obtain hemispherical domes utilized to form the 3-D electrodes. Biocompatibility and flexibility of the arrays were ensured by using polyimide and PDMS as the materials of choice for the arrays. EMG activity was recorded from the expiratory muscle group of anesthetized songbirds using the fabricated 2-D and 3-D arrays. Air pressure data were also recorded simultaneously from the air sac of the songbird. Together, EMG recordings and air pressure measurements can be used to characterize how the nervous system controls breathing and other motor behaviors. Such technologies can in turn provide unique insights into motor control in a range of species, including humans. An improvement of over 7× in the signal-to-noise ratio (SNR) is observed with the utilization of 3-D MEAs in comparison to 2-D MEAs.

Precise timing is ubiquitous, consistent, and coordinated across a comprehensive, spike-resolved flight motor program

Sequences of action potentials, or spikes, carry information in the number of spikes and their timing. Spike timing codes are critical in many sensory systems, but there is now growing evidence that millisecond-scale changes in timing also carry information in motor brain regions, descending decision-making circuits, and individual motor units. Across all of the many signals that control a behavior, how ubiquitous, consistent, and coordinated are spike timing codes? Assessing these open questions ideally involves recording across the whole motor program with spike-level resolution. To do this, we took advantage of the relatively few motor units controlling the wings of a hawk moth, Manduca sexta. We simultaneously recorded nearly every action potential from all major wing muscles and the resulting forces in tethered flight. We found that timing encodes more information about turning behavior than spike count in every motor unit, even though there is sufficient variation in count alone. Flight muscles vary broadly in function as well as in the number and timing of spikes. Nonetheless, each muscle with multiple spikes consistently blends spike timing and count information in a 3:1 ratio. Coding strategies are consistent. Finally, we assess the coordination of muscles using pairwise redundancy measured through interaction information. Surprisingly, not only are all muscle pairs coordinated, but all coordination is accomplished almost exclusively through spike timing, not spike count. Spike timing codes are ubiquitous, consistent, and essential for coordination.

Neural coding: The bureaucratic model of the brain

The neural coding metaphor is so ubiquitous that we tend to forget its metaphorical nature. What do we mean when we assert that neurons encode and decode? What kind of causal and representational model of the brain does the metaphor entail? What lies beneath the neural coding metaphor, I argue, is a bureaucratic model of the brain.

Motor Cortex Inputs at the Optimum Phase of Beta Cortical Oscillations Undergo More Rapid and Less Variable Corticospinal Propagation

Brain oscillations involve rhythmic fluctuations of neuronal excitability and may play a crucial role in neural communication. The human corticomuscular system is characterized by beta activity and is readily probed by transcranial magnetic stimulation (TMS). TMS inputs arriving at the excitable phase of beta oscillations in the motor cortex are known to lead to muscle responses of greater amplitude. Here we explore two other possible manifestations of rhythmic excitability in the beta band; windows of reduced response variability and shortened latency. We delivered single-pulse TMS to the motor cortex of healthy human volunteers (10 females and 7 males) during electroencephalography recordings made at rest. TMS delivered at a particular phase of the beta oscillation benefited from not only stronger, but also less variable and more rapid transmission, as evidenced by the greater amplitude, lower coefficient of variation, and shorter latency of motor evoked potentials. Thus, inputs aligned to the optimal phase of the beta EEG in the motor cortex enjoy transmission amplitude gain, but may also benefit from less variability and shortened latencies at subsequent synapses. Neuronal phase may therefore impact corticospinal communication.SIGNIFICANCE STATEMENT Brain oscillations involve rhythmic fluctuations of neuronal excitability. Therefore, motor responses to transcranial magnetic stimulation are larger when a cortical input arrives at a particular phase of the beta activity in the motor cortex. Here, we demonstrate that inputs to corticospinal neurons which coincide with windows of higher excitability also benefit from more rapid and less variable corticospinal transmission. This shortening of latency and increased reproducibility may confer additional advantage to inputs at specific phases. Moreover, these benefits are conserved despite appreciable corticospinal conduction delays.

Estimation of mutual information for real-valued data with error bars and controlled bias

Estimation of mutual information between (multidimensional) real-valued variables is used in analysis of complex systems, biological systems, and recently also quantum systems. This estimation is a hard problem, and universally good estimators provably do not exist. We focus on the estimator introduced by Kraskov et al. [Phys. Rev. E 69, 066138 (2004)PLEEE81539-375510.1103/PhysRevE.69.066138] based on the statistics of distances between neighboring data points, which empirically works for a wide class of underlying probability distributions. First, we illustrate pitfalls of naively applying bootstrapping to estimate the variance of the mutual information estimate. Then we improve this estimator by (1) expanding its range of applicability and by providing (2) a self-consistent way of verifying the absence of bias, (3) a method for estimation of its variance, and (4) guidelines for choosing the values of the free parameter of the estimator. We demonstrate the performance of our estimator on synthetic data sets, as well as on neurophysiological and systems biology data sets.

A Temporal Signal-Processing Circuit Based on Spiking Neuron and Synaptic Learning

Time is a continuous, homogeneous, one-way, and independent signal that cannot be modified by human will. The mechanism of how the brain processes temporal information remains elusive. According to previous work, time-keeping in medial premotor cortex (MPC) is governed by four kinds of ramp cell populations (Merchant et al., 2011). We believe that these cell populations participate in temporal information processing in MPC. Hence, in this the present study, we present a model that uses spiking neuron, including these cell populations, to construct a complete circuit for temporal processing. By combining the time-adaptive drift-diffusion model (TDDM) with the transmission of impulse information between neurons, this new model is able to successfully reproduce the result of synchronization-continuation tapping task (SCT). We also discovered that the neurons that we used exhibited some of the firing properties of time-related neurons detected by electrophysiological experiments in other studies. Therefore, we believe that our model reflects many of the physiological of neural circuits in the biological brain and can explain some of the phenomena in the temporal-perception process.

Estimating the Mutual Information between Two Discrete, Asymmetric Variables with Limited Samples

Determining the strength of nonlinear, statistical dependencies between two variables is a crucial matter in many research fields. The established measure for quantifying such relations is the mutual information. However, estimating mutual information from limited samples is a challenging task. Since the mutual information is the difference of two entropies, the existing Bayesian estimators of entropy may be used to estimate information. This procedure, however, is still biased in the severely under-sampled regime. Here, we propose an alternative estimator that is applicable to those cases in which the marginal distribution of one of the two variables—the one with minimal entropy—is well sampled. The other variable, as well as the joint and conditional distributions, can be severely undersampled. We obtain a consistent estimator that presents very low bias, outperforming previous methods even when the sampled data contain few coincidences. As with other Bayesian estimators, our proposal focuses on the strength of the interaction between the two variables, without seeking to model the specific way in which they are related. A distinctive property of our method is that the main data statistics determining the amount of mutual information is the inhomogeneity of the conditional distribution of the low-entropy variable in those states in which the large-entropy variable registers coincidences.

Transitioning between preparatory and precisely sequenced neuronal activity in production of a skilled behavior

Precise neural sequences are associated with the production of well-learned skilled behaviors. Yet, how neural sequences arise in the brain remains unclear. In songbirds, premotor projection neurons in the cortical song nucleus HVC are necessary for producing learned song and exhibit precise sequential activity during singing. Using cell-type specific calcium imaging we identify populations of HVC premotor neurons associated with the beginning and ending of singing-related neural sequences. We characterize neurons that bookend singing-related sequences and neuronal populations that transition from sparse preparatory activity prior to song to precise neural sequences during singing. Recordings from downstream premotor neurons or the respiratory system suggest that pre-song activity may be involved in motor preparation to sing. These findings reveal population mechanisms associated with moving from non-vocal to vocal behavioral states and suggest that precise neural sequences begin and end as part of orchestrated activity across functionally diverse populations of cortical premotor neurons.

Spatial-Temporal Dynamics of the Sensorimotor Cortex: Sustained and Transient Activity

How the sensorimotor cortex is organized with respect to controlling different features of movement is unclear. One unresolved question concerns the relation between the duration of an action and the duration of the associated neuronal activity change in the sensorimotor cortex. Using subdural electrocorticography electrodes, we investigated in five subjects, whether high frequency band (HFB; 75-135 Hz) power changes have a transient or sustained relation to speech duration, during pronunciation of the Dutch /i/ vowel with different durations. We showed that the neuronal activity patterns recorded from the sensorimotor cortex can be directly related to action duration in some locations, whereas in other locations, during the same action, neuronal activity is transient, with a peak in HFB activity at movement onset and/or offset. This data sheds light on the neural underpinnings of motor actions and we discuss the possible mechanisms underlying these different response types.

Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

The rate and temporal pattern of neural spiking each have the potential to influence computation. In the cerebellum, it has been hypothesized that the irregularity of interspike intervals in Purkinje cells affects their ability to transmit information to downstream neurons. Accordingly, during oculomotor behavior in mice and rhesus monkeys, mean irregularity of Purkinje cell spiking varied with mean eye velocity. However, moment-to-moment variations revealed a tight correlation between eye velocity and spike rate, with no additional information conveyed by spike irregularity. Moreover, when spike rate and irregularity were independently controlled using optogenetic stimulation, the eye movements elicited were well-described by a linear population rate code with 3-5 ms temporal precision. Biophysical and random-walk models identified biologically realistic parameter ranges that determine whether spike irregularity influences responses downstream. The results demonstrate cerebellar control of movements through a remarkably rapid rate code, with no evidence for an additional contribution of spike irregularity.

Deep learning in spiking neural networks

In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained, most often in a supervised manner using backpropagation. Vast amounts of labeled training examples are required, but the resulting classification accuracy is truly impressive, sometimes outperforming humans. Neurons in an ANN are characterized by a single, static, continuous-valued activation. Yet biological neurons use discrete spikes to compute and transmit information, and the spike times, in addition to the spike rates, matter. Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and are arguably the only viable option if one wants to understand how the brain computes at the neuronal description level. The spikes of biological neurons are sparse in time and space, and event-driven. Combined with bio-plausible local learning rules, this makes it easier to build low-power, neuromorphic hardware for SNNs. However, training deep SNNs remains a challenge. Spiking neurons' transfer function is usually non-differentiable, which prevents using backpropagation. Here we review recent supervised and unsupervised methods to train deep SNNs, and compare them in terms of accuracy and computational cost. The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNNs typically require many fewer operations and are the better candidates to process spatio-temporal data.

Fabrication and Characterization of 3D Multi-Electrode Array on Flexible Substrate for In Vivo EMG Recording from Expiratory Muscle of Songbird

This work presents fabrication and characterization of flexible three-dimensional (3D) multi-electrode arrays (MEAs) capable of high signal-to-noise (SNR) electromyogram (EMG) recordings from the expiratory muscle of a songbird. The fabrication utilizes a photoresist reflow process to obtain 3D structures to serve as the electrodes. A polyimide base with a PDMS top insulation was utilized to ensure flexibility and biocompatibility of the fabricated 3D MEA devices. SNR measurements from the fabricated 3D electrode show up to a 7x improvement as compared to the 2D MEAs.

Using spike train distances to identify the most discriminative neuronal subpopulation

BACKGROUND

Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses.

NEW METHOD

For the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best.

RESULTS

The best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease.

COMPARISON WITH EXISTING METHODS

Spike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary.

CONCLUSIONS

The spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results.

Millisecond Spike Timing Codes for Motor Control

Millisecond variations in spiking patterns can radically alter motor behavior, suggesting that traditional rate-based theories of motor control require revision. The importance of spike timing in sensorimotor control arises from dynamic interactions between the nervous system, muscles, and the body. New mechanisms, model systems, and theories are revealing how these interactions shape behavior.

Which spike train distance is most suitable for distinguishing rate and temporal coding?

BACKGROUND

It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance.

NEW METHOD

We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates?

RESULTS

Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only.

COMPARISON WITH EXISTING METHODS

While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates.

CONCLUSIONS

The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data.

Simple biochemical networks allow accurate sensing of multiple ligands with a single receptor

Cells use surface receptors to estimate concentrations of external ligands. Limits on the accuracy of such estimations have been well studied for pairs of ligand and receptor species. However, the environment typically contains many ligands, which can bind to the same receptors with different affinities, resulting in cross-talk. In traditional rate models, such cross-talk prevents accurate inference of concentrations of individual ligands. In contrast, here we show that knowing the precise timing sequence of stochastic binding and unbinding events allows one receptor to provide information about multiple ligands simultaneously and with a high accuracy. We show that such high-accuracy estimation of multiple concentrations can be realized with simple structural modifications of the familiar kinetic proofreading biochemical network diagram. We give two specific examples of such modifications. We argue that structural and functional features of real cellular biochemical sensory networks in immune cells, such as feedforward and feedback loops or ligand antagonism, sometimes can be understood as solutions to the accurate multi-ligand estimation problem.

Cells live in chemically complex environments with many different chemical ligands around them. Can cells estimate concentrations of more ligands than they have receptor types? In this paper, we show that, surprisingly, the answer is “yes”, and the estimation can be implemented with simple biochemical components already present in many cells. Therefore, cells may “know” a lot more about their environment and thus may be able to implement more complex and accurate response strategies than was previously thought.

Motor control by precisely timed spike patterns

A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior-namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision.

A Neural Code That Is Isometric to Vocal Output and Correlates with Its Sensory Consequences

What cortical inputs are provided to motor control areas while they drive complex learned behaviors? We study this question in the nucleus interface of the nidopallium (NIf), which is required for normal birdsong production and provides the main source of auditory input to HVC, the driver of adult song. In juvenile and adult zebra finches, we find that spikes in NIf projection neurons precede vocalizations by several tens of milliseconds and are insensitive to distortions of auditory feedback. We identify a local isometry between NIf output and vocalizations: quasi-identical notes produced in different syllables are preceded by highly similar NIf spike patterns. NIf multiunit firing during song precedes responses in auditory cortical neurons by about 50 ms, revealing delayed congruence between NIf spiking and a neural representation of auditory feedback. Our findings suggest that NIf codes for imminent acoustic events within vocal performance.

Transmission of birdsong across generations requires tight interactions between auditory and vocal systems. However, how these interactions take place is poorly understood. We studied neuronal activity in the brain area located at the intersection between auditory and song motor areas, which is known as the nucleus interface of the nidopallium. By recording during singing from neurons in the nucleus interface of the nidopallium that project to motor areas, we found that their spiking precedes peaks in vocal amplitudes by about 50 ms. Notably, quasi-identical notes produced at different times in the song motif were preceded by highly similar spike patterns in these projection neurons. Such local isometry between output from the nucleus interface of the nidopallium and vocalizations suggests that projection neurons in this brain area code for imminent acoustic events within vocal performance. In support of this conclusion, during singing, projection neurons do not respond to playback of white noise sound stimuli, and activity in the nucleus interface of the nidopallium precedes by about 50 ms neural activity in the avian analogue of auditory cortex. Therefore, we conclude that the role of neuronal activity in the nucleus interface of the nidopallium could be to link desired auditory targets to suitable motor commands required for hitting these targets.

Temporal and rate code analysis of responses to low-frequency components in the bird's own song by song system neurons

Auditory feedback (AF) plays a critical role in vocal learning. Previous studies in songbirds suggest that low-frequency (<~1 kHz) components may be salient cues in AF. We explored this with auditory stimuli including the bird's own song (BOS) and BOS variants with increased relative power at low frequencies (LBOS). We recorded single units from BOS-selective neurons in two forebrain nuclei (HVC and Area X) in anesthetized zebra finches. Song-evoked responses were analyzed based on both rate (spike counts) and temporal coding of spike trains. The BOS and LBOS tended to evoke similar spike-count responses in substantially overlapping populations of neurons in both HVC and Area X. Analysis of spike patterns demonstrated temporal coding information that discriminated among the BOS and LBOS stimuli significantly better than spike counts in the majority of HVC (94 %) and Area X (85 %) neurons. HVC neurons contained more and a broader range of temporal coding information to discriminate among the stimuli than Area X neurons. These results are consistent with a role of spike timing in coding differences in the spectral components of BOS in HVC and Area X neurons.

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.