Coding of Sensory Signals by Neuronal Populations: The Role of Correlated Activity

A causal perspective on the analysis of signal and noise correlations and their role in population coding

The role of correlations between neuronal responses is crucial to understanding the neural code. A framework used to study this role comprises a breakdown of the mutual information between stimuli and responses into terms that aim to account for different coding modalities and the distinction between different notions of independence. Here we complete the list of types of independence and distinguish activity independence (related to total correlations), conditional independence (related to noise correlations), signal independence (related to signal correlations), coding independence (related to information transmission), and information independence (related to redundancy). For each type, we identify the probabilistic criterion that defines it, indicate the information-theoretic measure used as statistic to test for it, and provide a graphical criterion to recognize the causal configurations of stimuli and responses that lead to its existence. Using this causal analysis, we first provide sufficiency conditions relating these types. Second, we differentiate the use of the measures as statistics to test for the existence of independence from their use for quantification. We indicate that signal and noise correlation cannot be quantified separately. Third, we explicitly define alternative system configurations used to construct the measures, in which noise correlations or noise and signal correlations are eliminated. Accordingly, we examine which measures are meaningful only as a comparison across configurations and which ones provide a characterization of the actually observed responses without resorting to other configurations. Fourth, we compare the commonly used nonparametric approach to eliminate noise correlations with a functional (model-based) approach, showing that the former approach does not remove those effects of noise correlations captured by the tuning properties of the individual neurons, and implies nonlocal causal structure manipulations. These results improve the interpretation of the measures on the framework and help in understanding how to apply it to analyze the role of correlations.

Readout of the intrinsic and extrinsic properties of a stimulus from un-experienced neuronal activities: towards cognitive neuroprostheses

While sensory and motor systems have attracted most of the research effort in the field neuroprosthetics, little attention has been devoted to higher order cortical processes. Here, we propose a first step in the direction of applying neural decoding to the study and manipulation of visuospatial attention, an endogenous process at the interface between sensory and motor functions. To this aim, we investigate whether the offline activity of a population of non-human primate frontal eye field neurons (FEF) in response to an endogenous cue can be readout on a trial by trial basis to provide a precise description of the cue's attributes, namely, its location and identity, but also the allocation of attention following its interpretation. Using a linear decoder, we reach up to 86% correct predictions for the different decoded variables, including the spatial allocation of endogenous attention. We show that the decoding performance drops on incorrect trials, indicating that cue encoding participates to the animal's behavioral performance. Last, we show that the temporal resolution of the decoding influences readout performance. These results are a strong indication of the feasibility of the readout of endogenous variables by standard decoding algorithms, on a suboptimal dataset. However, its validity remains to be proved in a real-time situation.

The behavioral neuroscience of anuran social signal processing

Acoustic communication is the major component of social behavior in anuran amphibians (frogs and toads) and has served as a neuroethological model for the nervous system's processing of social signals related to mate choice decisions. The male's advertisement or mating call is its most conspicuous social signal, and the nervous system's analysis of the call is a progressive process. As processing proceeds through neural systems, response properties become more specific to the signal and, in addition, neural activity gradually shifts from representing sensory (auditory periphery and brainstem) to sensorimotor (diencephalon) to motor (forebrain) components of a behavioral response. A comparative analysis of many anuran species shows that the first stage in biasing responses toward conspecific signals over heterospecific signals, and toward particular features of conspecific signals, lies in the tuning of the peripheral auditory system. Biases in processing signals are apparent through the brainstem auditory system, where additional feature detection neurons are added by the time processing reaches the level of the midbrain. Recent work using immediate early gene expression as a marker of neural activity suggests that by the level of the midbrain and forebrain, the differential neural representation of conspecific and heterospecific signals involves both changes in mean activity levels across multiple subnuclei, and in the functional correlations among acoustically active areas. Our data show that in frogs the auditory midbrain appears to play an important role in controlling behavioral responses to acoustic social signals by acting as a regulatory gateway between the stimulus analysis of the brainstem and the behavioral and physiological control centers of the forebrain. We predict that this will hold true for other vertebrate groups such as birds and fish that produce acoustic social signals, and perhaps also in fish where electroreception or vibratory sensing through the lateral line systems plays a role in social signaling, as in all these cases ascending sensory information converges onto midbrain nuclei which relay information to higher brain centers.

Information Carried by Population Spike Times in the Whisker Sensory Cortex can be Decoded Without Knowledge of Stimulus Time

Computational analyses have revealed that precisely timed spikes emitted by somatosensory cortical neuronal populations encode basic stimulus features in the rat's whisker sensory system. Efficient spike time based decoding schemes both for the spatial location of a stimulus and for the kinetic features of complex whisker movements have been defined. To date, these decoding schemes have been based upon spike times referenced to an external temporal frame – the time of the stimulus itself. Such schemes are limited by the requirement of precise knowledge of the stimulus time signal, and it is not clear whether stimulus times are known to rats making sensory judgments. Here, we first review studies of the information obtained from spike timing referenced to the stimulus time. Then we explore new methods for extracting spike train information independently of any external temporal reference frame. These proposed methods are based on the detection of stimulus-dependent differences in the firing time within a neuronal population. We apply them to a data set using single-whisker stimulation in anesthetized rats and find that stimulus site can be decoded based on the millisecond-range relative differences in spike times even without knowledge of stimulus time. If spike counts alone are measured over tens or hundreds of milliseconds rather than milliseconds, such decoders are much less effective. These results suggest that decoding schemes based on millisecond-precise spike times are likely to subserve robust and information-rich transmission of information in the somatosensory system.

Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands

Studies analyzing sensory cortical processing or trying to decode brain activity often rely on a combination of different electrophysiological signals, such as local field potentials (LFPs) and spiking activity. Understanding the relation between these signals and sensory stimuli and between different components of these signals is hence of great interest. We here provide an analysis of LFPs and spiking activity recorded from visual and auditory cortex during stimulation with natural stimuli. In particular, we focus on the time scales on which different components of these signals are informative about the stimulus, and on the dependencies between different components of these signals. Addressing the first question, we find that stimulus information in low frequency bands (<12 Hz) is high, regardless of whether their energy is computed at the scale of milliseconds or seconds. Stimulus information in higher bands (>50 Hz), in contrast, is scale dependent, and is larger when the energy is averaged over several hundreds of milliseconds. Indeed, combined analysis of signal reliability and information revealed that the energy of slow LFP fluctuations is well related to the stimulus even when considering individual or few cycles, while the energy of fast LFP oscillations carries information only when averaged over many cycles. Addressing the second question, we find that stimulus information in different LFP bands, and in different LFP bands and spiking activity, is largely independent regardless of time scale or sensory system. Taken together, these findings suggest that different LFP bands represent dynamic natural stimuli on distinct time scales and together provide a potentially rich source of information for sensory processing or decoding brain activity.

Neural coding and contextual influences in the whisker system

A fundamental problem in neuroscience, to which Prof. Segundo has made seminal contributions, is to understand how action potentials represent events in the external world. The aim of this paper is to review the issue of neural coding in the context of the rodent whiskers, an increasingly popular model system. Key issues we consider are: the role of spike timing; mechanisms of spike timing; decoding and context-dependence. Significant insight has come from the development of rigorous, information theoretic frameworks for tackling these questions, in conjunction with suitably designed experiments. We review both the theory and experimental studies. In contrast to the classical view that neurons are noisy and unreliable, it is becoming clear that many neurons in the subcortical whisker pathway are remarkably reliable and, by virtue of spike timing with millisecond-precision, have high bandwidth for conveying sensory information. In this way, even small (approximately 200 neuron) subcortical modules are able to support the sensory processing underlying sophisticated whisker-dependent behaviours. Future work on neural coding in cortex will need to consider new findings that responses are highly dependent on context, including behavioural and internal states.

Mutual information expansion for studying the role of correlations in population codes: how important are autocorrelations?

The role of correlations in the activity of neural populations responding to a set of stimuli can be studied within an information theory framework. Regardless of whether one approaches the problem from an encoding or decoding perspective, the main measures used to study the role of correlations can be derived from a common source: the expansion of the mutual information. Two main formalisms of mutual information expansion have been proposed: the series expansion and the exact breakdown. Here we clarify that these two formalisms have a different representation of autocorrelations, so that even when the total information estimated differs by less than 1%, individual terms can diverge. More precisely, the series expansion explicitly evaluates the informational contribution of autocorrelations in the count of spikes, that is, count autocorrelations, whereas the exact breakdown does not. We propose a new formalism of mutual information expansion, the Poisson exact breakdown, which introduces Poisson equivalents in order to explicitly evaluate the informational contribution of count autocorrelations with no approximation involved. Because several widely employed manipulations of spike trains, most notably binning and pooling, alter the structure of count autocorrelations, the new formalism can provide a useful general framework for studying the role of correlations in population codes.

Dynamic population coding of category information in inferior temporal and prefrontal cortex

Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain "abstract" category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.

Latency represents sound frequency in mouse IC

Frequency is one of the fundamental parameters of sound. The frequency of an acoustic stimulus can be represented by a neural response such as spike rate, and/or first spike latency (FSL) of a given neuron. The spike rates/frequency function of most neurons changes with different acoustic amplitudes, whereas FSL/frequency function is highly stable. This implies that FSL might represent the frequency of a sound stimulus more efficiently than spike rate. This study involved representations of acoustic frequency by spike rate and FSL of central inferior colliculus (IC) neurons responding to free-field pure-tone stimuli. We found that the FSLs of neurons responding to characteristic frequency (CF) of sound stimulus were usually the shortest, regardless of sound intensity, and that spike rates of most neurons showed a variety of function according to sound frequency, especially at high intensities. These results strongly suggest that FSL of auditory IC neurons can represent sound frequency more precisely than spike rate.

Spatio-temporal information analysis of event-related BOLD responses

A new approach for analysis of event-related fMRI (BOLD) signals is proposed. The technique is based on measures from information theory and is used both for spatial localization of task-related activity, as well as for extracting temporal information regarding the task-dependent propagation of activation across different brain regions. This approach enables whole brain visualization of voxels (areas) most involved in coding of a specific task condition, the time at which they are most informative about the condition, as well as their average amplitude at that preferred time. The approach does not require prior assumptions about the shape of the hemodynamic response function (HRF) nor about linear relations between BOLD response and presented stimuli (or task conditions). We show that relative delays between different brain regions can also be computed without prior knowledge of the experimental design, suggesting a general method that could be applied for analysis of differential time delays that occur during natural, uncontrolled conditions. Here we analyze BOLD signals recorded during performance of a motor learning task. We show that, during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex. Brain areas found to be associated with reduced activity during motor learning, predominantly in prefrontal brain regions, are informative about the task typically at significantly later times.