Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex

The precise timing of spikes of cortical neurons relative to stimulus onset carries substantial sensory information. To access this information the sensory systems would need to maintain an internal temporal reference that reflects the precise stimulus timing. Whether and how sensory systems implement such reference frames to decode time-dependent responses, however, remains debated. Studying the encoding of naturalistic sounds in primate (Macaca mulatta) auditory cortex we here investigate potential intrinsic references for decoding temporally precise information. Within the population of recorded neurons, we found one subset responding with stereotyped fast latencies that varied little across trials or stimuli, while the remaining neurons had stimulus-modulated responses with longer and variable latencies. Computational analysis demonstrated that the neurons with stereotyped short latencies constitute an effective temporal reference for relative coding. Using the response onset of a simultaneously recorded stereotyped neuron allowed decoding most of the stimulus information carried by onset latencies and the full spike train of stimulus-modulated neurons. Computational modeling showed that few tens of such stereotyped reference neurons suffice to recover nearly all information that would be available when decoding the same responses relative to the actual stimulus onset. These findings reveal an explicit neural signature of an intrinsic reference for decoding temporal response patterns in the auditory cortex of alert animals. Furthermore, they highlight a role for apparently unselective neurons as an early saliency signal that provides a temporal reference for extracting stimulus information from other neurons.     

How many neurons can we see with current spike sorting algorithms?

Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8-10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms.     

How the brain uses time to represent and process visual information(1)

Information theory provides a theoretical framework for addressing fundamental questions concerning the nature of neural codes. Harnessing its power is not straightforward, because of the differences between mathematical abstractions and laboratory reality. We describe an approach to the analysis of neural codes that seeks to identify the informative features of neural responses, rather than to estimate the information content of neural responses per se. Our analysis, applied to neurons in primary visual cortex (V1), demonstrates that the informative precision of spike times varies with the stimulus modality being represented. Contrast is represented by spike times on the shortest time scale, and different kinds of pattern information are represented on longer time scales. The interspike interval distribution has a structure that is unanticipated from the firing rate. The significance of this structure is not that it contains additional information, but rather, that it may provide a means for simple synaptic mechanisms to decode the information that is multiplexed within a spike train. Extensions of this analysis to the simultaneous responses of pairs of neurons indicate that neighboring neurons convey largely independent information, if the decoding process is sensitive to the neuron of origin and not just the average firing rate. In summary, stimulus-related information is encoded into the precise times of spikes fired by V1 neurons. Much of this information would be obscured if individual spikes were merely taken to be estimators of the firing rate. Additional information would be lost by averaging across the responses of neurons in a local population. We propose that synaptic mechanisms sensitive to interspike intervals and dendritic processing beyond simple summation exist at least in part to enable the brain to take advantage of this extra information.     

Measuring the quality of neuronal identification in ensemble recordings

Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.     

How the brain uses time to represent and process visual information

Information theory provides a theoretical framework for addressing fundamental questions concerning the nature of neural codes. Harnessing its power is not straightforward, because of the differences between mathematical abstractions and laboratory reality. We describe an approach to the analysis of neural codes that seeks to identify the informative features of neural responses, rather than to estimate the information content of neural responses per se. Our analysis, applied to neurons in primary visual cortex (V1), demonstrates that the informative precision of spike times varies with the stimulus modality being represented. Contrast is represented by spike times on the shortest time scale, and different kinds of pattern information are represented on longer time scales. The interspike interval distribution has a structure that is unanticipated from the firing rate. The significance of this structure is not that it contains additional information, but rather, that it may provide a means for simple synaptic mechanisms to decode the information that is multiplexed within a spike train. Extensions of this analysis to the simultaneous responses of pairs of neurons indicate that neighboring neurons convey largely independent information, if the decoding process is sensitive to the neuron of origin and not just the average firing rate. In summary, stimulus-related information is encoded into the precise times of spikes fired by V1 neurons. Much of this information would be obscured if individual spikes were merely taken to be estimators of the firing rate. Additional information would be lost by averaging across the responses of neurons in a local population. We propose that synaptic mechanisms sensitive to interspike intervals and dendritic processing beyond simple summation exist at least in part to enable the brain to take advantage of this extra information.     

Conditioned spikes: a simple and fast method to represent rates and temporal patterns in multielectrode recordings

Increasing evidence suggests that the brain utilizes distributed codes that can only be analyzed by simultaneously recording the activity of multiple neurons. This paper introduces a new methodology for studying neural ensemble recordings. The method uses a novel representation to provide complementary information about the stimuli which are contained in the temporal pattern of the spike sequence. By using this procedure, a high correlation of synchronized events with stimuli times is apparent. To quantify the results and to compare the performance of this method against the most traditional raster plot, we have used Fano factor and cross-correlation analysis. Our results suggest that several consecutive spikes from different neurons within an extended time window may encode behaviorally relevant information. We propose that this new representation, in addition to the other approaches currently used (standard raster plots, multivariate statistical methods, neuronal networks, information theory, etc.), can be a useful procedure to describe population spike dynamics.     

Building blocks for electronic spiking neural networks.

We present an electronic circuit modelling the spike generation process in the biological neuron. This simple circuit is capable of simulating the spiking behaviour of several different types of biological neurons. At the same time, the circuit is small so that many neurons can be implemented on a single silicon chip. This is important, as neural computation obtains its power not from a single neuron, but from the interaction between a large number of neurons. Circuits that model these interactions are also presented in this paper. They include the circuits for excitatory, inhibitory and shunting inhibitory synapses, a circuit which models the regeneration of spikes on the axon, and a circuit which models the reduction of input strength with the distance of the synapse to the cell body on the dendrite of the cell. Together these building blocks allow the implementation of electronic spiking neural networks.     

Generation of spike latency tuning by thalamocortical circuits in auditory cortex

In many sensory systems, the latency of spike responses of individual neurons is found to be tuned for stimulus features and proposed to be used as a coding strategy. Whether the spike latency tuning is simply relayed along sensory ascending pathways or generated by local circuits remains unclear. Here, in vivo whole-cell recordings from rat auditory cortical neurons in layer 4 revealed that the onset latency of their aggregate thalamic input exhibited nearly flat tuning for sound frequency, whereas their spike latency tuning was much sharper with a broadly expanded dynamic range. This suggests that the spike latency tuning is not simply inherited from the thalamus, but can be largely reconstructed by local circuits in the cortex. Dissecting of thalamocortical circuits and neural modeling further revealed that broadly tuned intracortical inhibition prolongs the integration time for spike generation preferentially at off-optimal frequencies, while sharply tuned intracortical excitation shortens it selectively at the optimal frequency. Such push and pull mechanisms mediated likely by feedforward excitatory and inhibitory inputs respectively greatly sharpen the spike latency tuning and expand its dynamic range. The modulation of integration time by thalamocortical-like circuits may represent an efficient strategy for converting information spatially coded in synaptic strength to temporal representation.     

Embedded ensemble encoding hypothesis: The role of the “Prepared” cell

We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10–20 mV, duration 200–500 ms). Our central hypothesis is that synaptically‐evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic‐to‐action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from “resting” to “depolarized” neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200–500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit “binding” of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer‐lasting Prepared ensemble of neurons. We hypothesize that “embedded ensemble encoding” may be an important organizing principle in networks of neurons.     

Accurate spike sorting for multi‐unit recordings

Simultaneous recordings with multi‐channel electrodes are widely used for studying how multiple neurons are recruited for information processing. The recorded signals contain the spike events of a number of adjacent or distant neurons and must be sorted correctly into spike trains of individual neurons. Several mathematical methods have been proposed for spike sorting but the process is difficult in practice, as extracellularly recorded signals are corrupted by biological noise. Moreover, spike sorting is often time‐consuming, as it usually requires corrections by human operators. Methods are needed to obtain reliable spike clusters without heavy manual operation. Here, we introduce several methods of spike sorting and compare the accuracy and robustness of their performance by using publicized data of simultaneous extracellular and intracellular recordings of neuronal activity. The best and excellent performance was obtained when a newly proposed filter for spike detection was combined with the wavelet transform and variational Bayes for a finite mixture of Student’s t‐distributions, namely, robust variational Bayes. Wavelet transform extracts features that are characteristic of the detected spike waveforms and the robust variational Bayes categorizes the extracted features into clusters corresponding to spikes of the individual neurons. The use of Student’s t‐distributions makes this categorization robust against noisy data points. Some other new methods also exhibited reasonably good performance. We implemented all of the proposed methods in a C++ code named ‘EToS’ (Efficient Technology of Spike sorting), which is freely available on the Internet.     

A Fast and Simple Population Code for Orientation in Primate V1

Orientation tuning has been a classic model for understanding single-neuron computation in the neocortex. However, little is known about how orientation can be read out from the activity of neural populations, in particular in alert animals. Our study is a first step toward that goal. We recorded from up to 20 well isolated single neurons in the primary visual cortex of alert macaques simultaneously and applied a simple, neurally plausible decoder to read out the population code. We focus on two questions: First, what are the time course and the timescale at which orientation can be read out from the population response? Second, how complex does the decoding mechanism in a downstream neuron have to be to reliably discriminate between visual stimuli with different orientations? We show that the neural ensembles in primary visual cortex of awake macaques represent orientation in a way that facilitates a fast and simple readout mechanism: With an average latency of 30-80 ms, the population code can be read out instantaneously with a short integration time of only tens of milliseconds, and neither stimulus contrast nor correlations need to be taken into account to compute the optimal synaptic weight pattern. Our study shows that-similar to the case of single-neuron computation-the representation of orientation in the spike patterns of neural populations can serve as an exemplary case for understanding the computations performed by neural ensembles underlying visual processing during behavior.     

Optimizing for generalization in the decoding of internally generated activity in the hippocampus

The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a result, it is unclear how decoders of such internally generated activity should be configured in practice. We suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization performance using cross‐validation. Using ensemble recording data from hippocampal place cells, we show that this cross‐validation approach results in different decoding error, different optimal decoding parameters, and different distributions of error across the decoded variable space. In addition, we show that a minor modification to the commonly used Bayesian decoding procedure, which enables the use of spike density functions, results in substantially lower decoding errors. These results have implications for the interpretation of covert neural activity, and suggest easy‐to‐implement changes to commonly used procedures across domains, with applications to hippocampal place cells in particular. © 2017 Wiley Periodicals, Inc.     

A comparative analysis of integrating visual information in local neuronal ensembles

Spike directivity, a new measure that quantifies the transient charge density dynamics within action potentials provides better results in discriminating different categories of visual object recognition. Specifically, intracranial recordings from medial temporal lobe (MTL) of epileptic patients have been analyzed using firing rate, interspike intervals and spike directivity. A comparative statistical analysis of the same spikes from a local ensemble of four selected neurons shows that electrical patterns in these neurons display higher separability to input images compared to spike timing features. If the observation vector includes data from all four neurons then the comparative analysis shows a highly significant separation between categories for spike directivity (p=0.0023) and does not display separability for interspike interval (p=0.3768) and firing rate (p=0.5492). Since electrical patterns in neuronal spikes provide information regarding different presented objects this result shows that related information is intracellularly processed in neurons and carried out within a millisecond-level time domain of action potential occurrence. This significant statistical outcome obtained from a local ensemble of four neurons suggests that meaningful information can be electrically inferred at the network level to generate a better discrimination of presented images.     

Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: a simulation study

One of the most important building blocks of the brain–machine interface (BMI) based on neuronal spike trains is the decoding algorithm, a computational method for the reconstruction of desired information from spike trains. Previous studies have reported that a simple linear filter is effective for this purpose and that no noteworthy gain is achieved from the use of nonlinear algorithms. In order to test this premise, we designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR). Their performances were assessed using multiple neuronal spike trains generated by a biophysical neuron model and by a directional tuning model of the primary motor cortex. The performances of the nonlinear algorithms, in general, were superior. The advantages of using nonlinear algorithms were more profound for cases where false-positive/negative errors occurred in spike trains. When the MLPs were trained using trial-and-error, they often showed disappointing performance comparable to that of the linear filter. The nonlinear SVR showed the highest performance, and this may be due to the superiority of SVR in training and generalization.     

An FPGA-based approach to high-speed simulation of conductance-based neuron models

The constant requirement for greater performance in neural model simulation has created the need for high-speed simulation platforms. We present a generalized, scalable field programmable gate array (FPGA)-based architecture for fast computation of neural models and focus on the steps involved in implementing a single-compartment and a two-compartment neuron model. Based on timing tests, it is shown that FPGAs can outperform traditional desktop computers in simulating these fairly simple models and would most likely provide even larger performance gains over computers in simulating more complex models. The potential of this method for improving neural modeling and dynamic clamping is discussed. In particular, it is believed that this approach could greatly speed up simulations of both highly complex single neuron models and networks of neurons. Additionally, our design is particularly well suited to automated parameter searches for tuning model behavior and to real-time simulation.     

Coding for auditory space in the superior colliculus of the rat

Although the rat is often used to determine behavioural sound-localization capabilities or neuronal computation of binaural information, the representation of auditory space in the rat brain has not been investigated so far. We obtained extracellular recordings from auditory neurons in the superior colliculus of anaesthetized rats and examined them for spatial tuning characteristics and topographical order. Many neurons (73%) showed significant tuning, with a single peak in the azimuth response profiles based on spike rates and response latencies. Best azimuth values from neurons in one SC were generally tuned to contralateral and rarely to frontal or ipsilateral directions. Tuning width was mostly broad; at supra-threshold sound pressure levels (35 dB SPL), 55% of the units had a tuning width of > 120 degrees in contralateral space. Additionally, tuning width increased with stimulation intensity. A significant but considerably scattered topographical order of best azimuth directions was observed in the deep layers of the superior colliculus with frontal directions being represented closer to the rostral pole. Tuned auditory units in the intermediate layers of the superior colliculus, however, showed no systematic spatial arrangement. This pattern was confirmed by analysing best azimuth directions from simultaneously recorded units. Our results indicate that the rat superior colliculus contains a representation of auditory space which is similar to that described for other small mammals.     

What single‐unit recording studies tell us about the basic mechanisms of sleep and wakefulness

This paper reviews the presence, localization and characteristics of state‐specific neurons in the mouse forebrain, midbrain and hindbrain that are involved in the control of ultradian sleep–wake cycles and shows that all these regions contain basic neural elements capable of generating the sleep–wake cycle. The chronic single‐unit recording method in unanaesthetized animals is useful for unravelling the dynamics of sleep–wake switching, in particular because it can analyse events at the level of single neurons, thereby decoding information used by the brain in determining its functional state. A prerequisite is to record a large number of all types of neurons, identify critical wake‐ and sleep‐promoting neurons and determine their activity profiles during the sleep–wake cycle and their trends in spike activity during the state transitions from wakefulness to sleep and from sleep to wakefulness in the same species. Here, I argue that single‐unit recordings in unanaesthetized mice help us to (a) determine key neural elements controlling sleep–wake dynamics, (b) elucidate the roles of forebrain and brainstem neurons in ultradian sleep–wake cyclicity and (c) gain a new insight into the functional significance of wakefulness, slow‐wave sleep and paradoxical (or rapid eye movement) sleep. I also discuss the merits and limitations of single‐unit recording compared with more recent genetic approaches, and I suggest that findings from studies using the classic electrophysiological technique will provide the foundation for future studies using new genetic techniques to dissect the neural networks responsible for the initiation, maintenance and cessation of each wake and sleep state.     

Temporal coupling between subicular and hippocampal neurons underlies retention of trial-specific events

The subiculum receives the majority of efferent outflow of neural information from the CA1 region of the hippocampus. As such it occupies a strategic position in which to integrate, transfer and resolve activity from the hippocampus relating to memory and performance. We have previously demonstrated that each structure has complementary ensemble firing patterns that together allow information to be represented continuously over the time course of a trial in a delayed-non-match-to-sample (DNMS) task. Here, we extend this analysis to show the precise manner in which specific neurons in both structures are coupled temporally across the delay interval on a single trial. Neurons in both structures encode position-specific information related to the sample lever press, but only subicular neurons continue to fire during the early portion of the subsequent variable delay interval. However, cross-correlation analysis of multiple spike trains showed that as the delay increased in duration other subicular neurons were temporally coupled to the subicular neurons that fired in the initial part of the delay. This latter population of subicular neurons showed strong cross-correlated firing with other initially activated subicular neurons until midway through the delay (<15s), but on longer delay intervals were coupled to a specific type of hippocampal neuron whose firing was critical for correct performance. Subicular neurons, therefore, play a critical role in bringing the hippocampus "back online" when trial delays exceed the minimum duration thus allowing both structures to cooperatively bridge relevant information across longer time intervals than would not otherwise be possible.     

Optimal firing rate estimation.

We define a measure for evaluating the quality of a predictive model of the behavior of a spiking neuron. This measure, information gain per spike (Is), indicates how much more information is provided by the model than if the prediction were made by specifying the neuron's average firing rate over the same time period. We apply a maximum Is criterion to optimize the performance of Gaussian smoothing filters for estimating neural firing rates. With data from bullfrog vestibular semicircular canal neurons and data from simulated integrate-and-fire neurons, the optimal bandwidth for firing rate estimation is typically similar to the average firing rate. Precise timing and average rate models are limiting cases that perform poorly. We estimate that bullfrog semicircular canal sensory neurons transmit in the order of 1 bit of stimulus-related information per spike.