Neural representation of the acoustic biotope: On the existence of stimulus-event relations for sensory neurons

In the context of a behaviourally oriented investigation of neural information processing we have studied single unit responses from cochlear nucleus (12 units) and auditory cortex (23 units) of the cat. Attention was focused on two aspects: the composition of a wide stimulus ensemble and the influence of this ensemble on the single unit neural activity: action potentials or neural events. The stimulus ensemble was based on sounds occurring in the natural environment of the animal: the Acoustic Biotope. A realization of such an ensemble was constructed and used in the experiments. A systematic analysis of the effect of this stimulus ensemble on the neural activity comprises different questions with respect to a possible stimulus-event relation: existence and nature. In the present paper emphasis is given to the question of existence of a s-e relation. The existence of such a relation has been investigated by presenting the stimulus ensemble twice. The degree of reproducibility of neural activity indicates to what extent a s-e relation indeed exists. A formal measure of testing the reproducibility of two sequences of neural events is defined: the Cross Coincidence Function. Results are presented, interpretation and formal problems discussed. Results obtained so far indicate that on the whole the existence of a s-e relation can be established more definitely for cochlear nucleus units than for auditory cortex units. Applications of the presented procedure arise in exploratory investigations in central regions of the sensory nervous system and in regions with presumed multimodal inputs.     

The neurochrome

Recording of simultaneous but separated activity of neural populations overwhelms the experimenter with a large amount of information. A clearly structured display technique the Neurochrome is introduced, usable on-line and real-time. It shows neural activity patterns while preserving neural identity by employing a color code. The Neurochrome assists the experimenter in generating and verifying hypotheses about neural correlations and stimulus-event relations already during the experiment. In auditory research single neurons are characterized by their spectro-temporal sensitivity to auditory stimuli. A straightforward generalization of this concept, applicable to neural populations, is proposed leading to a global indication of a populations' activity to stimuli: the Multi-Unit Spectro-Temporal Sensitivity. This approach is inversely related to the Neurochrome, the latter however containing more information. The combination of both approaches seems quite powerful in the investigation of neural assemblies. The procedures are illustrated with examples of extracellular multiunit recordings from the auditory midbrain of the grassfrog (Rana temporaria L.).     

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al. in Spikes: exploring the neural code. MIT Press, Cambridge, 1999; Brenner et al. in Neural Comput 12(7):1531-1552, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.     

Detection Theory Applied to the Absolute Sensitivity of Sensory Systems

Mainly the skin senses touch and warmth have been investigated. It is shown that the decision model describes the experimental data better than the threshold model. The experiments lead to the assumption that an internal noise exists, which is a neural activity being undistinguishable from the neural activity caused by small stimuli and which adds to the neural activity caused by the stimulus. The probability distribution of this internal noise can be considered to be gaussian. The relation between stimulus and neural activity is alinear for the touch sense. The question of whether noise of a multiplicative nature must be assumed is discussed.     

Neural network mechanisms underlying stimulus driven variability reduction

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits.     

Oscillatory synchrony and human visual cognition.

Oscillatory synchrony could be used to establish dynamic links between the various cortical areas participating in the same cognitive process. Is it possible to detect oscillatory synchrony in humans, and is it relevant to behavior? There is now converging evidence for the existence of a transient oscillatory activity in the gamma range (30-60 Hz), obtained in response to static visual objects, and having only a loose temporal relationship to stimulus onset. This so-called "induced" gamma response is much larger in response to coherent static or moving objects. However, functional variations of gamma and/or beta (15-20 Hz) oscillations are not restricted to perceptive, bottom-up mechanisms, but are also observed during visual imagery or short-term memory maintenance. Oscillations at the scalp level thus seem to reflect large-scale neural cooperativity in a variety of task-dependent networks. Human intra-cranial recordings in a short-term memory paradigm further reveal the existence and the task-dependency of oscillatory synchrony in the beta range, between focal sites separated by several centimeters and with a few milliseconds time-lag. These findings thus confirm experimentally the hypothesis of a functional role of synchronized oscillatory activity in the coordination of distributed neural activity in humans, and support Hebb's concept of short-term memory maintenance by reentrant activity within the activated network. In addition, the intra-cranial data obtained in humans and monkeys also help to better understand the neural mechanisms generating scalp-recorded oscillations.     

Spectro-temporal receptive fields of auditory neurons in the grassfrog

The stimulus-event relation of single units in the auditory midbrain area, the torus semicircularis, of the anaesthetized grassfrog (Rana temporaria L.) during stimulation with a wide ensemble of natural stimuli, was analysed using first and second order statistical analysis techniques. The average stimulus preceding the occurrence of action potentials, in general, did not prove to give very informative results. The second order procedure consisted in the determination of the average dynamic power spectrum of the pre-event stimuli, following procedures as described elsewhere (Aertsen and Johannesma, 1980; Aertsen et al., 1980). The outcome of this analysis was filtered with the overall power spectrum of the complete stimulus ensemble in order to correct for its non-uniform spectral composition. The stimulus-filtered average pre-event dynamic spectrum gives a first indication of the spectro-temporal receptive field of a neuron under natural stimulus conditions. Results for a limited number of recordings are presented and, globally, compared to the outcome of an analogous analysis of experiments with tonal stimuli.     

Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process

Background

Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed.

Methodology/Principal Findings

We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d'') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30–60 Hz) and alpha (8–14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.

Conclusions/Significance

We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.     

Stimulus dependent neural correlations in the auditory midbrain of the grassfrog (Rana temporaria L.)

Few-unit recordings were obtained using metal microelectrodes. Separation into single-unit spike trains was based on differences in spike amplitude and spike waveform. For that purpose a hardware microprocessor based spike waveform analyser was designed and built. Spikes are filtered by four matched filters and filter outputs at the moments of spike occurrence are read by a computer and used for off-line separation and spike waveform reconstruction. Thirty-one double unit recordings were obtained and correlation between the separated spike trains was determined. After stimulus correction correlation remained in only 8 of the double unit records. It appeared that in most cases this neural correlation was stimulus dependent. Continuous noise stimulation resulted in the strongest neural correlation remaining after correction for stimulus coupling, stimulation with 48 ms duration tonepips presented once per second generally did not result in a significant neural correlation after the correction procedure for stimulus lock. The usefulness of the additive model for neural correlation and the correction procedure based thereupon is discussed.     

Coupling of total hemoglobin concentration,oxygenation, and neural activity in rat somatosensory cortex

Recent advances in brain imaging techniques, including functional magnetic resonance imaging (fMRI), offer great promise for noninvasive mapping of brain function. However, the indirect nature of the imaging signals to the underlying neural activity limits the interpretation of the resulting maps. The present report represents the first systematic study with sufficient statistical power to quantitatively characterize the relationship between changes in blood oxygen content and the neural spiking and synaptic activity. Using two-dimensional optical measurements of hemodynamic signals, simultaneous recordings of neural activity, and an event-related stimulus paradigm, we demonstrate that (1) there is a strongly nonlinear relationship between electrophysiological measures of neuronal activity and the hemodynamic response, (2) the hemodynamic response continues to grow beyond the saturation of electrical activity, and (3) the initial increase in deoxyhemoglobin that precedes an increase in blood volume is counterbalanced by an equal initial decrease in oxyhemoglobin.     

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.     

Normal variability of the amplitude and phase of steady-state VEPs

The present study quantifies the amplitude and phase variability of steady-state VEPs (S-VEPs) and compares this variability between subjects and between individual runs. The S-VEPs were recorded repeatedly in 14 normal subjects with varying spatial and temporal frequencies of sinusoidal gratings; 6 spatial frequencies (range 0.5–8.0 c/deg) with 3 temporal frequencies (4, 6 and 8 Hz) were used. A total of 75 responses were averaged and analyzed by the Fourier method. Four recordings were obtained in each spatio-temporal combination.In general, the phase data showed small inter- and intrasubject variability. As anticipated, the amplitude data showed a large degree of intersubject variability, although the intrasubject variability was very small. In addition, in some stimulus conditions the inter- and intrasubject variability increased, which thus suggested the existence of an optimal spatio-temporal combination. Therefore, these stimulus parameters should be taken into consideration when S-VEPs are applied in clinical practice.     

Spiking networks for Bayesian inference and choice

Systems neuroscience traditionally conceptualizes a population of spiking neurons as merely encoding the value of a stimulus. Yet, psychophysics has revealed that people take into account stimulus uncertainty when performing sensory or motor computations and do so in a nearly Bayes-optimal way. This suggests that neural populations do not encode just a single value but an entire probability distribution over the stimulus. Several such probabilistic codes have been proposed, including one that utilizes the structure of neural variability to enable simple neural implementations of probabilistic computations such as optimal cue integration. This approach provides a quantitative link between Bayes-optimal behaviors and specific neural operations. It allows for novel ways to evaluate probabilistic codes and for predictions for physiological population recordings.     

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.     

Fear conditioning in humans: the influence of awareness and autonomic arousal on functional neuroanatomy

The degree to which perceptual awareness of threat stimuli and bodily states of arousal modulates neural activity associated with fear conditioning is unknown. We used functional magnetic neuroimaging (fMRI) to study healthy subjects and patients with peripheral autonomic denervation to examine how the expression of conditioning-related activity is modulated by stimulus awareness and autonomic arousal. In controls, enhanced amygdala activity was evident during conditioning to both "seen" (unmasked) and "unseen" (backward masked) stimuli, whereas insula activity was modulated by perceptual awareness of a threat stimulus. Absent peripheral autonomic arousal, in patients with autonomic denervation, was associated with decreased conditioning-related activity in insula and amygdala. The findings indicate that the expression of conditioning-related neural activity is modulated by both awareness and representations of bodily states of autonomic arousal.     

The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal

Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygen-level-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single- and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.     

Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states

In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as “engaging in dialogue” and “using electronics”. Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity’s covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.     

Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study

Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity--as measured by the Fisher information--is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint.     

Osmotic thirst suppression elicited by electrical stimulation of the basal forebrain in the dog.

Reactivity of the thirst mechanism to osmotic stimuli was measured in conscious dogs chronically implanted with electrodes in the basal forebrain. It was found that the thirst threshold to the osmotic stimulus increases markedly during electrical stimulation of the preoptic area, septum, nucleus accumbens septi, and to smaller degree during stimulation of the nucleus caudatus. The possibility of existence of some inhibitory components in the neural system controlling thirst in the dog is suggested.