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

Modulation of Visual Responses in the Superior Temporal Sulcus by Audio-Visual Congruency

Our ability to identify or recognize visual objects is often enhanced by evidence provided by other sensory modalities. Yet, where and how visual object processing benefits from the information received by the other senses remains unclear. One candidate region is the temporal lobe, which features neural representations of visual objects, and in which previous studies have provided evidence for multisensory influences on neural responses. In the present study we directly tested whether visual representations in the lower bank of the superior temporal sulcus (STS) benefit from acoustic information. To this end, we recorded neural responses in alert monkeys passively watching audio-visual scenes, and quantified the impact of simultaneously presented sounds on responses elicited by the presentation of naturalistic visual scenes. Using methods of stimulus decoding and information theory, we then asked whether the responses of STS neurons become more reliable and informative in multisensory contexts. Our results demonstrate that STS neurons are indeed sensitive to the modality composition of the sensory stimulus. Importantly, information provided by STS neurons’ responses about the particular visual stimulus being presented was highest during congruent audio-visual and unimodal visual stimulation, but was reduced during incongruent bimodal stimulation. Together, these findings demonstrate that higher visual representations in the STS not only convey information about the visual input but also depend on the acoustic context of a visual scene

Category-Specific Responses to Faces and Objects in Primate Auditory Cortex

Auditory and visual signals often occur together, and the two sensory channels are known to influence each other to facilitate perception. The neural basis of this integration is not well understood, although other forms of multisensory influences have been shown to occur at surprisingly early stages of processing in cortex. Primary visual cortex neurons can show frequency-tuning to auditory stimuli, and auditory cortex responds selectively to certain somatosensory stimuli, supporting the possibility that complex visual signals may modulate early stages of auditory processing. To elucidate which auditory regions, if any, are responsive to complex visual stimuli, we recorded from auditory cortex and the superior temporal sulcus while presenting visual stimuli consisting of various objects, neutral faces, and facial expressions generated during vocalization. Both objects and conspecific faces elicited robust field potential responses in auditory cortex sites, but the responses varied by category: both neutral and vocalizing faces had a highly consistent negative component (N100) followed by a broader positive component (P180) whereas object responses were more variable in time and shape, but could be discriminated consistently from the responses to faces. The face response did not vary within the face category, i.e., for expressive vs. neutral face stimuli. The presence of responses for both objects and neutral faces suggests that auditory cortex receives highly informative visual input that is not restricted to those stimuli associated with auditory components. These results reveal selectivity for complex visual stimuli in a brain region conventionally described as non-visual “unisensory” cortex

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

<p>Abstract</p> <p>Background</p> <p>Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.</p> <p>Results</p> <p>Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.</p> <p>Conclusion</p> <p>The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.</p

Cortical mechanisms of sensory learning and object recognition

Learning about the world through our senses constrains our ability to recognise our surroundings. Experience shapes perception. What is the neural basis for object recognition and how are learning-induced changes in recognition manifested in neural populations? We consider first the location of neurons that appear to be critical for object recognition, before describing what is known about their function. Two complementary processes of object recognition are considered: discrimination among diagnostic object features and generalization across non-diagnostic features. Neural plasticity appears to underlie the development of discrimination and generalization for a given set of features, though tracking these changes directly over the course of learning has remained an elusive task

Dopamine-induced dissociation of BOLD and neural activity in macaque visual cortex

Neuromodulators determine how neural circuits process information during cognitive states such as wakefulness, attention, learning, and memory [1]. fMRI can provide insight into their function and dynamics, but their exact effect on BOLD responses remains unclear [2, 3 and 4], limiting our ability to interpret the effects of changes in behavioral state using fMRI. Here, we investigated the effects of dopamine (DA) injections on neural responses and haemodynamic signals in macaque primary visual cortex (V1) using fMRI (7T) and intracortical electrophysiology. Aside from DA’s involvement in diseases such as Parkinson’s and schizophrenia, it also plays a role in visual perception [5, 6, 7 and 8]. We mimicked DAergic neuromodulation by systemic injection of L-DOPA and Carbidopa (LDC) or by local application of DA in V1 and found that systemic application of LDC increased the signal-to-noise ratio (SNR) and amplitude of the visually evoked neural responses in V1. However, visually induced BOLD responses decreased, whereas cerebral blood flow (CBF) responses increased. This dissociation of BOLD and CBF suggests that dopamine increases energy metabolism by a disproportionate amount relative to the CBF response, causing the reduced BOLD response. Local application of DA in V1 had no effect on neural activity, suggesting that the dopaminergic effects are mediated by long-range interactions. The combination of BOLD-based and CBF-based fMRI can provide a signature of dopaminergic neuromodulation, indicating that the application of multimodal methods can improve our ability to distinguish sensory processing from neuromodulatory effects

Signal detection in extracellular neural ensemble recordings using higher criticism

Information processing in the brain is conducted by a concerted action of multiple neural populations. Gaining insights in the organization and dynamics of such populations can best be studied with broadband intracranial recordings of so-called extracellular field potential, reflecting neuronal spiking as well as mesoscopic activities, such as waves, oscillations, intrinsic large deflections, and multiunit spiking activity. Such signals are critical for our understanding of how neuronal ensembles encode sensory information and how such information is integrated in the large networks underlying cognition. The aforementioned principles are now well accepted, yet the efficacy of extracting information out of the complex neural data, and their employment for improving our understanding of neural networks, critically depends on the mathematical processing steps ranging from simple detection of action potentials in noisy traces - to fitting advanced mathematical models to distinct patterns of the neural signal potentially underlying intra-processing of information, e.g. interneuronal interactions. Here, we present a robust strategy for detecting signals in broadband and noisy time series such as spikes, sharp waves and multi-unit activity data that is solely based on the intrinsic statistical distribution of the recorded data. By using so-called higher criticism - a second-level significance testing procedure comparing the fraction of observed significances to an expected fraction under the global null - we are able to detect small signals in correlated noisy time-series without prior filtering, denoising or data regression. Results demonstrate the efficiency and reliability of the method and versatility over a wide range of experimental conditions and suggest the appropriateness of higher criticism to characterize neuronal dynamics without prior manipulation of the data

Towards extracellular Ca2+ sensing by MRI: synthesis and calcium-dependent 1H and 17O relaxation studies of two novel bismacrocyclic Gd3+ complexes

Two new bismacrocyclic Gd3+ chelates containing a specific Ca2+ binding site were synthesized as potential MRI contrast agents for the detection of Ca2+ concentration changes at the millimolar level in the extracellular space. In the ligands, the Ca2+-sensitive BAPTA-bisamide central part is separated from the DO3A macrocycles either by an ethylene (L1) or by a propylene (L2) unit [H4BAPTA is 1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic acid; H3DO3A is 1,4,7,10-tetraazacyclododecane-1,4,7-triacetic acid]. The sensitivity of the Gd3+ complexes towards Ca2+ and Mg2+ was studied by 1H relaxometric titrations. A maximum relaxivity increase of 15 and 10% was observed upon Ca2+ binding to Gd2L1 and Gd2L2, respectively, with a distinct selectivity of Gd2L1 towards Ca2+ compared with Mg2+. For Ca2+ binding, association constants of log K = 1.9 (Gd2L1) and log K = 2.7 (Gd2L2) were determined by relaxometry. Luminescence lifetime measurements and UV–vis spectrophotometry on the corresponding Eu3+ analogues proved that the complexes exist in the form of monohydrated and nonhydrated species; Ca2+ binding in the central part of the ligand induces the formation of the monohydrated state. The increasing hydration number accounts for the relaxivity increase observed on Ca2+ addition. A 1H nuclear magnetic relaxation dispersion and 17O NMR study on Gd2L1 in the absence and in the presence of Ca2+ was performed to assess the microscopic parameters influencing relaxivity. On Ca2+ binding, the water exchange is slightly accelerated, which is likely related to the increased steric demand of the central part leading to a destabilization of the Ln–water binding interaction

Comparing the Feature Selectivity of the Gamma-Band of the Local Field Potential and the Underlying Spiking Activity in Primate Visual Cortex

The local field potential (LFP), comprised of low-frequency extra-cellular voltage fluctuations, has been used extensively to study the mechanisms of brain function. In particular, oscillations in the gamma-band (30–90 Hz) are ubiquitous in the cortex of many species during various cognitive processes. Surprisingly little is known about the underlying biophysical processes generating this signal. Here, we examine the relationship of the local field potential to the activity of localized populations of neurons by simultaneously recording spiking activity and LFP from the primary visual cortex (V1) of awake, behaving macaques. The spatial organization of orientation tuning and ocular dominance in this area provides an excellent opportunity to study this question, because orientation tuning is organized at a scale around one order of magnitude finer than the size of ocular dominance columns. While we find a surprisingly weak correlation between the preferred orientation of multi-unit activity and gamma-band LFP recorded on the same tetrode, there is a strong correlation between the ocular preferences of both signals. Given the spatial arrangement of orientation tuning and ocular dominance, this leads us to conclude that the gamma-band of the LFP seems to sample an area considerably larger than orientation columns. Rather, its spatial resolution lies at the scale of ocular dominance columns