The orienting response, and future directions of its development

The orienting response (OR) is a specific behavioral act directed towards extraction of information from the environment. Head and eye movements represent only the tip of the iceberg of internal responses, which includes vascular modifications, EEG changes, and event-related potentials. Two mechanisms of the OR have to be differentiated: voluntary and involuntary. In the event-related potential, such a differentiation is expressed in mismatch negativity (involuntary effect) and processing negativity (voluntary effect). Single unit studies have shown that hippocampal neurones are simulating specific features of the OR as a response to novelty. Repeated presentation of stimuli results in a selective habituation of novelty detectors in hippocampus and of the OR. The trace of a standard stimulus formed at the level of hippocampal neurones matches the features of the standard stimulus and can be called a "neuronal model of the stimulus." The OR is triggered by mismatch between the test stimulus and the elaborated neuronal model, and is activated by verbal instruction, by reinforcement during the initial stage of conditioned reflex elaboration, and by differentiation of signal and non-signal stimuli. A promising new area of practical application of the OR lies in the evaluation of a corridor of optimal functional state for efficient computer-based learning. Registration of the OR and defensive responses can be used for an objective evaluation of the functional state of the student, or, in a wider sense, of the industrial operator. New avenues of OR research are opened by recent techniques that isolate single-trial event related potentials, and their correlation with autonomic and behavioral manifestations of the OR.(ABSTRACT TRUNCATED AT 250 WORDS)     

Orienting reflex: "targeting reaction" and "searchlight of attention"

The concept of orienting reflex based on the principle of vector coding of cognitive and executive processes is proposed. The orienting reflex to non-signal and signal stimuli is a set of orienting reactions: motor, autonomic, neuronal, and subjective emphasizing new and significant stimuli. Two basic mechanisms can be identified within the orienting reflex: a "targeting reaction" and a "searchlight of attention". In the visual system the first one consists in a foveation of a target stimulus. The foveation is performed with participation of premotor neurons excited by saccadic command neurons of the superior colliculi. The "searchlight of attention" is based on the resonance of gamma-oscillations in the reticular thalamus selectively enhancing responses of cortical neurons (involuntary attention). The novelty signal is generated in novelty neurons of the hippocampus, which are selectively tuned to a repeatedly presented standard stimulus. The selective tuning is caused by the depression of plastic synapses representing a "neuronal model" of the standard stimulus. A mismatch of the novel stimulus with the established neuronal model gives rise to a "novelty signal" enhancing the novel input. The novelty signal inhibits current conditioned reflexes (external inhibition) contributing to redirecting the behavior. By triggering the expression of early genes the novelty signal initiates the formation of the long-term memory connected with neoneurogenesis.     

Absolute coding of stimulus novelty in the human substantia nigra/VTA

Novelty exploration can enhance hippocampal plasticity in animals through dopaminergic neuromodulation arising in the substantia nigra/ventral tegmental area (SN/VTA). This enhancement can outlast the exploration phase by several minutes. Currently, little is known about dopaminergic novelty processing and its relationship to hippocampal function in humans. In two functional magnetic resonance imaging (fMRI) studies, SN/VTA activations in humans were indeed driven by stimulus novelty rather than other forms of stimulus salience such as rareness, negative emotional valence, or targetness of familiar stimuli, whereas hippocampal responses were less selective. SN/VTA novelty responses were scaled according to absolute rather than relative novelty in a given context, unlike adaptive SN/VTA responses recently reported for reward outcome in animal studies. Finally, novelty enhanced learning and perirhinal/parahippocampal processing of familiar items presented in the same context. Thus, the human SN/VTA can code absolute stimulus novelty and might contribute to enhancing learning in the context of novelty.     

The P300 based brain-computer interface: effect of stimulus position in a stimulus train

The P300 brain-computer interface (BCI) is currently the most efficient BCI. This interface is based on detection of the P300 wave of the brain potentials evoked when a symbol related to the intended input is highlighted. To increase operation speed of the P300 BCI, reduction of the number of stimuli repetitions is needed. This reduction leads to increase of the relative contribution to the input symbol detection from the reaction to the first target stimulus. It is known that the event-related potentials (ERP) to the first stimulus presentations can be different from the ERP to stimuli presented latter. In particular, the amplitude of responses to the first stimulus presentations is often increased, which is beneficial for their recognition by the BCI. However, this effect was not studied within the BCI framework. The current study examined the ERP obtained from healthy participants (n = 14) in the standard P300 BCI paradigm using 10 trials, as well as in the modified P300 BCI with stimuli presented on moving objects in triple-trial (n = 6) and single-trial (n = 6) stimulation modes. Increased ERP amplitude was observed in response to the first target stimuli in both conditions, as well as in the single-trial mode comparing to triple-trial. We discuss the prospects of using the specific features of the ERP to first stimuli and the single-trial ERP for optimizing the high-speed modes in the P300 BCIs.     

Neural correlates of novelty detection in pulse-type weakly electric fish

Evoked potentials (EPs) and single unit recordings from various electrosensory-processing regions of several pulse-type gymnotiform species were made to investigate neural activity patterns that could be associated with novelty detection. Whereas the electrosensory afferents and cells in the ELL exhibited only minor changes in response size as stimuli were presented less frequently (novel stimuli), most units studied in the torus semicircularis (TS) showed very strong, increased responsiveness to stimuli presented less frequently relative to stimuli presented persistently (at every EOD event. The responses of the TS were graded with respect to stimulus frequency. The discrimination between novel and persistent stimuli by the TS occurred with stimuli presented transversely or longitudinally with respect to the fish's long axis, and regardless of the timing of the stimulus with respect to the fish's pacemaker-related signal (PS). When electrosensory novelties were presented persistently the responses of the TS rapidly habituated. This may indicate that activity in this region of the TS is novelty related. This novelty-related activity in the TS can be correlated with certain aspects of the fish's behavior, i.e., EOD interval length during a behavioral novelty response. However, TS activity may continue to indicate the occurrence of electrosensory novelties after the behavior has habituated. It is suggested that the novelty-related activity of the TS of these fish is necessary, but not sufficient, for the production of electrosensory novelty-induced behavioral responses. Lesions of the region of the TS containing the rapidly-habituating neurons abolished the electrosensory novelty response, but not that resulting from visual and auditory stimulation.     

Vector representation of associative learning

I. P. Pavlov [12] has shown that conditioned reflexes are selective both with respect to conditioned stimuli and to conditioned reflexes elicited by those conditioned stimuli. At the neuronal level selective aspects of conditioned stimuli are based on detectors selectively tuned to respective stimuli. The selective aspects of conditioned reflexes are due to command neurons representing specific unconditioned reflexes. It can be assumed that conditioned reflexes result from association between selective detectors and specific command neurons. The detectors activated by a conditioned stimulus constitute a combination of excitations--a detector excitation vector. The detector excitation vector acts on a command neuron via a set of plastic synapses--a synaptic weight vector. Plastic synapses are modified in the process of learning making command neuron selectively tuned to a specific conditioned stimulus. The selective tuning of a particular command neuron to a specific excitation vector referred to a conditioned stimulus is a basis of associative learning. The probabilities of conditioned reflexes elicited by conditioned and differential stimuli implicitly contain information concerning excitation vectors that encode respective stimuli. Contribution of the vector code to associative learning was explored combining differential color conditioning with intracellular recording from color-coding neurons. It was shown that colors in carps and monkeys are represented on a hypersphere in the four-dimensional space similar to human color space. The basis of the color space is constituted by red-green, blue-yellow, brightness and darkness neurons.     

<Emphasis Type="Italic">P</Emphasis>300-based brain-computer interface: The effect of the stimulus position in a stimulus train

The most popular type of brain-computer interfaces (BCIs) are based on the detection of the P300 wave of the evoked potentials appearing in response to a stimulus chosen by the subject. In order to increase the speed of operation of these BCIs, it is possible to decrease the number of repeated stimulus presentations. It is associated with an increase in the relative importance of the response to the first stimulus in a train for correct recognition of the stimulus chosen. Event-related potentials (ERPs) in response to the first stimulus presentations are known to have their own specificity. Particularly, in many cases, the amplitude of the response to the first presentations is enhanced, which makes it very suitable for recognition in a BCI. However, this effect has not been studied to date. In this study, the ERPs recorded in healthy subjects in a standard BCI paradigm (n = 14) with ten presentations of stimuli or during triple-trial (n = 6) and single-trial (n = 6) presentations of stimuli in a modified BCI paradigm with moving objects have been analyzed. In both cases, first presentations of the target stimuli or single-trial presentation of the target stimulus were associated with higher amplitudes of ERPs. The opportunity to use specific differences between the responses to the first or single-trial presentations and the responses to later stimuli during their repeated presentations for improving high-speed operations in the P300-based BCI is discussed.     

Invariant recognition of feature combinations in the visual system

 The operation of a hierarchical competitive network model (VisNet) of invariance learning in the visual system is investigated to determine how this class of architecture can solve problems that require the spatial binding of features. First, we show that VisNet neurons can be trained to provide transform-invariant discriminative responses to stimuli which are composed of the same basic alphabet of features, where no single stimulus contains a unique feature not shared by any other stimulus. The investigation shows that the network can discriminate stimuli consisting of sets of features which are subsets or supersets of each other. Second, a key feature-binding issue we address is how invariant representations of low-order combinations of features in the early layers of the visual system are able to uniquely specify the correct spatial arrangement of features in the overall stimulus and ensure correct stimulus identification in the output layer. We show that output layer neurons can learn new stimuli if the lower layers are trained solely through exposure to simpler feature combinations from which the new stimuli are composed. Moreover, we show that after training on the low-order feature combinations which are common to many objects, this architecture can – after training with a whole stimulus in some locations – generalise correctly to the same stimulus when it is shown in a new location. We conclude that this type of hierarchical model can solve feature-binding problems to produce correct invariant identification of whole stimuli. Received: 4 August 1999 / Accepted in revised form: 11 October 2000     

Brain state and plasticity: an integration of the reciprocal interaction model of sleep cycle oscillation with attentional models of hippocampal function

The present paper relates the reciprocal interaction model for sleep cycle oscillation (McCarley and Hobson, ref. 29) to an attentional model of hippocampal function (Schmajuk and Moore, ref. 44). We consider mechanisms by which the interaction between gigantocellular tegmental field (FTG) cells and locus coeruleus (LC) activity proposed by the sleep cycle model may differentially modulate the information processing carried out in the hippocampus as described by the attentional model. Our fundamental assumption is that learning about the relevancy of different stimuli is proportional to the level of LC activation. If the environment becomes unpredictable during waking, the FTG and LC are activated and the LC facilitates hippocampal learning about stimulus relevancy. In a predictable situation during waking, FTG cells discharge rarely because no novelty is detected, and LC neurons are moderately active. If the predictable situation lasts, LC cells also decrease their activity, and a sleep period might start. At sleep onset, LC inhibition decreases and FTG activity is low leading to slow sleep. As FTG activity increases and LC activity reaches its low point, REM sleep starts. Because LC activity is low during REM sleep, values of stimulus relevancy remain unchanged. Since during sleep the threshold for external stimuli is high, only internally generated novel stimuli (subjectively perceived as dream mentation) may activate the LC. LC renewed inhibitory influence on the FTG ends REM sleep.     

Model of Familiarity Discrimination in the Perirhinal Cortex

Much evidence indicates that recognition memory involves two separable processes, recollection and familiarity discrimination, with familiarity discrimination being dependent on the perirhinal cortex of the temporal lobe. Here, we describe a new neural network model designed to mimic the response patterns of perirhinal neurons that signal information concerning the novelty or familiarity of stimuli. The model achieves very fast and accurate familiarity discrimination while employing biologically plausible parameters and Hebbian learning rules. The fact that the activity patterns of the model's simulated neurons are closely similar to those of neurons recorded from the primate perirhinal cortex indicates that this brain region could discriminate familiarity using principles akin to those of the model. If so, the capacity of the model establishes that the perirhinal cortex alone may discriminate the familiarity of many more stimuli than current neural network models indicate could be recalled (recollected) by all the remaining areas of the cerebral cortex. This efficiency and speed of detecting novelty provides an evolutionary advantage, thereby providing a reason for the existence of a familiarity discrimination network in addition to networks used for recollection.     

Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans

Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes.     

The linearity of emergent spectro-temporal receptive fields in a model of auditory cortex

The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (<1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system.     

Attentional modulation strength in cortical area MT depends on stimulus contrast

The attentional modulation of sensory information processing in the visual system is the result of top-down influences, which can cause a multiplicative modulation of the firing rate of sensory neurons in extrastriate visual cortex, an effect reminiscent of the bottom-up effect of changes in stimulus contrast. This similarity could simply reflect the multiplicity of both effects. But, here we show that in direction-selective neurons in monkey visual cortical area MT, stimulus and attentional effects share a nonlinearity. These neurons show higher response gain for both contrast and attentional changes for intermediate contrast stimuli and smaller gain for low- and high-contrast stimuli. This finding suggests a close relationship between the neural encoding of stimulus contrast and the modulating effect of the behavioral relevance of stimuli.     

Novelty-induced increased expression of immediate-early genes c-fos and arg 3.1 in the mouse brain

The detection of novel stimuli is a memory-dependent process. The presented stimulus has to be compared with memory contents to judge its novelty. In addition, the novelty of stimuli activates attention-related processes that facilitate memory formation. To determine the involvement of limbic and neocortical brain structures in novelty detection, we exposed mice to a novel gustatory stimulus (0.5% saccharin) added to their drinking fluid. We then compared the novelty-induced expression of the two immediate-early genes (IEGs) c-fos and arg 3.1, with their expression in mice familiarized with the same stimulus or mice not exposed to that stimulus. Exposure to taste novelty increased expression of c-fos and arg 3.1 mRNA in the cingulate cortex and deep layers of the parietal cortex. In addition, c-fos mRNA expression was increased in the amygdala and arg 3.1 mRNA was increased in the dentate gyrus. Expression of c-fos and arg 3.1 was elevated 30 min after the exposure to novelty. For arg 3.1, a second peak of expression was found 4.5 h after presentation of the novel stimulus. Our results indicate that the amygdala, the dentate gyrus, and the cingulate and parietal cortices may be involved in novelty detection and associated cognitive events, and suggest that c-fos and arg 3.1 play distinct roles in these processes.     

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

It is currently not known how distributed neuronal responses in early visual areas carry stimulus-related information. We made multielectrode recordings from cat primary visual cortex and applied methods from machine learning in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters of the alphabet) presented for 100 ms and with intervals of 100 ms or larger. Most of the information about visual stimuli extractable by sophisticated methods of machine learning, i.e., support vector machines with nonlinear kernel functions, was also extractable by simple linear classification such as can be achieved by individual neurons. New stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and, when using short time constants for integration (e.g., 20 ms), in the precise timing of individual spikes (≤∼20 ms), and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and is capable of performing online computations utilizing information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs.     

Techniques for temporal detection of neural sensitivity to external stimulation

We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a positive response, we propose two methods: Fisherian statistical testing and a data-driven Bayesian approach to determine the response probability of a neuron. The latter is non-parametric, data-driven, and captures a lower bound for the probability of neural responses to sensory stimulation. Both methods are compared with a standard test that assumes normal probability distributions. We applied the sensitivity estimation based on the proposed method to experimental data recorded from the mushroom body (MB) of locusts. We show that there is a broad range of sensitivity that the MB response sweeps during odor stimulation. The neurons are initially tuned to specific odors, but tend to demonstrate a generalist behavior towards the end of the stimulus period, meaning that the emphasis shifts from discrimination to feature learning.     

Learning strengthens the response of primary visual cortex to simple patterns

Training can significantly improve performance on even the most basic visual tasks, such as detecting a faint patch of light or determining the orientation of a bar (for reviews, see ). The neural mechanisms of visual learning, however, remain controversial. One simple way to improve behavior is to increase the overall neural response to the trained stimulus by increasing the number or gain of responsive neurons. Learning of this type has been observed in other sensory modalities, where training increases the number of receptive fields that cover the trained stimulus. Here, we show that visual learning can selectively increase the overall response to trained stimuli in primary visual cortex (V1). We used functional magnetic resonance imaging (fMRI) to measure neural signals before and after one month of practice at detecting very low-contrast oriented patterns. Training increased V1 response for practiced orientations relative to control orientations by an average of 39%, and the magnitude of the change in V1 correlated moderately well with the magnitude of changes in detection performance. The elevation of V1 activity by training likely results from an increase in the number of neurons responding to the trained stimulus or an increase in response gain.     

Ensembles of human MTL neurons "jump back in time" in response to a repeated stimulus

Episodic memory, which depends critically on the integrity of the medial temporal lobe (MTL), has been described as "mental time travel" in which the rememberer "jumps back in time." The neural mechanism underlying this ability remains elusive. Mathematical and computational models of performance in episodic memory tasks provide a specific hypothesis regarding the computation that supports such a jump back in time. The models suggest that a representation of temporal context, a representation that changes gradually over macroscopic periods of time, is the cue for episodic recall. According to these models, a jump back in time corresponds to a stimulus recovering a prior state of temporal context. In vivo single-neuron recordings were taken from the human MTL while epilepsy patients distinguished novel from repeated images in a continuous recognition memory task. The firing pattern of the ensemble of MTL neurons showed robust temporal autocorrelation over macroscopic periods of time during performance of the memory task. The gradually-changing part of the ensemble state was causally affected by the visual stimulus being presented. Critically, repetition of a stimulus caused the ensemble to elicit a pattern of activity that resembled the pattern of activity present before the initial presentation of the stimulus. These findings confirm a direct prediction of this class of temporal context models and may be a signature of the mechanism that underlies the experience of episodic memory as mental time travel. ? 2012 Wiley Periodicals, Inc.     

Decoding Speech Perception by Native and Non-Native Speakers Using Single-Trial Electrophysiological Data

Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.     

Searching for Optimal Sensory Signals: Iterative Stimulus Reconstruction in Closed-Loop Experiments

Shaped by evolutionary processes, sensory systems often represent behaviorally relevant stimuli with higher fidelity than other stimuli. The stimulus dependence of neural reliability could therefore provide an important clue in a search for relevant sensory signals. We explore this relation and introduce a novel iterative algorithm that allows one to find stimuli that are reliably represented by the sensory system under study. To assess the quality of a neural representation, we use stimulus reconstruction methods. The algorithm starts with the presentation of an initial stimulus (e.g. white noise). The evoked spike train is recorded and used to reconstruct the stimulus online. Within a closed-loop setup, this reconstruction is then played back to the sensory system. Iterating this procedure, the newly generated stimuli can be better and better reconstructed. We demonstrate the feasibility of this method by applying it to auditory receptor neurons in locusts. Our data show that the optimal stimuli often exhibit pronounced sub-threshold periods that are interrupted by short, yet intense pulses. Similar results are obtained for simple model neurons and suggest that these stimuli are encoded with high reliability by a large class of neurons.