Human Decision Making Based on Variations in Internal Noise: An EEG Study

Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or ‘internal’ noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG) activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments.     

Neural Representation of Ambiguous Visual Objects in the Inferior Temporal Cortex

Inferior temporal (IT) cortex as the final stage of the ventral visual pathway is involved in visual object recognition. In our everyday life we need to recognize visual objects that are degraded by noise. Psychophysical studies have shown that the accuracy and speed of the object recognition decreases as the amount of visual noise increases. However, the neural representation of ambiguous visual objects and the underlying neural mechanisms of such changes in the behavior are not known. Here, by recording the neuronal spiking activity of macaque monkeys’ IT we explored the relationship between stimulus ambiguity and the IT neural activity. We found smaller amplitude, later onset, earlier offset and shorter duration of the response as visual ambiguity increased. All of these modulations were gradual and correlated with the level of stimulus ambiguity. We found that while category selectivity of IT neurons decreased with noise, it was preserved for a large extent of visual ambiguity. This noise tolerance for category selectivity in IT was lost at 60% noise level. Interestingly, while the response of the IT neurons to visual stimuli at 60% noise level was significantly larger than their baseline activity and full (100%) noise, it was not category selective anymore. The latter finding shows a neural representation that signals the presence of visual stimulus without signaling what it is. In general these findings, in the context of a drift diffusion model, explain the neural mechanisms of perceptual accuracy and speed changes in the process of recognizing ambiguous objects.     

Spectro-temporal interpretation of activity patterns of auditory neurons

Sensory receptors transform an external sensory stimulus into an internal neural activity pattern. This mapping is studied through its inverse. An earlier paper showed that within the context of a neuron model composed of a linear filter followed by an exponential pulse generator and a Gaussian stimulus ensemble a unique "most plausible" first-order stimulus estimate can be constructed. This method, applicable only to neurons showing phase-lock, is extended to neurons without phase-lock. In this situation second-order spectro-temporal stimulus estimates are produced; examples are given from simulation. The method is applied to activity of neurons in the auditory system of the frog.     

Nonmonotonic noise tuning of BOLD fMRI signal to natural images in the visual cortex of the anesthetized monkey

BACKGROUND: The perceptual ability of humans and monkeys to identify objects in the presence of noise varies systematically and monotonically as a function of how much noise is introduced to the visual display. That is, it becomes more and more difficult to identify an object with increasing noise. Here we examine whether the blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) signal in anesthetized monkeys also shows such monotonic tuning. We employed parametric stimulus sets containing natural images and noise patterns matched for spatial frequency and intensity as well as intermediate images generated by interpolation between natural images and noise patterns. Anesthetized monkeys provide us with the unique opportunity to examine visual processing largely in the absence of top-down cognitive modulations and can thus provide an important baseline against which work with awake monkeys and humans can be compared. RESULTS: We measured BOLD activity in occipital visual cortical areas as natural images and noise patterns, as well as intermediate interpolated patterns at three interpolation levels (25%, 50%, and 75%) were presented to anesthetized monkeys in a block paradigm. We observed reliable visual activity in occipital visual areas including V1, V2, V3, V3A, and V4 as well as the fundus and anterior bank of the superior temporal sulcus (STS). Natural images consistently elicited higher BOLD levels than noise patterns. For intermediate images, however, we did not observe monotonic tuning. Instead, we observed a characteristic V-shaped noise-tuning function in primary and extrastriate visual areas. BOLD signals initially decreased as noise was added to the stimulus but then increased again as the pure noise pattern was approached. We present a simple model based on the number of activated neurons and the strength of activation per neuron that can account for these results. CONCLUSIONS: We show that, for our parametric stimulus set, BOLD activity varied nonmonotonically as a function of how much noise was added to the visual stimuli, unlike the perceptual ability of humans and monkeys to identify such stimuli. This raises important caveats for interpreting fMRI data and demonstrates the importance of assessing not only which neural populations are activated by contrasting conditions during an fMRI study, but also the strength of this activation. This becomes particularly important when using the BOLD signal to make inferences about the relationship between neural activity and behavior.     

Numerical exploration of the influence of neural noise on the psychometric function at low stimulation intensity levels

The relationship between stimulus intensity and the probability of detecting the presence of the stimulus is described by the psychometrical function. The probabilistic nature of this relationship is based on the stochastic behaviour of sensory neural channels and sensory networks involved in perceptual processing (Kiang 1968). This study tries to establish a continuum of variability across different levels of integration in the central nervous system. Once the opening and closing times of ionic channels was simulated, a threshold to the collective behaviour of voltage-gated ionic channels was imposed in order to generate the spike train of a single neuron. Afterwards, the trains of spikes of different neurons were added up, simulating the activity of a sensory nerve. By adding the activity due to the stimulus to the spontaneous neural behaviour, the psychometric function was simulated using a thresholding approach. The results can replicate the stochastic resonance phenomenon, but also open up the possibility that attentional phenomena can be mediated not only by increasing neural activity (bursting or oscillatory), but also by increasing noise at the neural level.     

A model for feature linking via collective oscillations in the primary visual cortex

A neural network model for explaining experimentally observed neuronal responses in cat primary visual cortex is proposed. In our model, the basic functional unit is an orientation column which is represented by a large homogeneous population of neurons modeled as integrate-and-fire type excitable elements. The orientation column exhibits spontaneous collective oscillations in activity in response to suitable visual stimuli. Such oscillations are caused by mutual synchronization among the neurons within the column. Numerical simulation for various stimulus patterns shows that as a result of activity correlations between different columns, the amplitude and the phase of the oscillation in each column depend strongly on the global feature of the stimulus pattern. These results satisfactorily account for experimental observations.     

A neural network model for selective attention in visual pattern recognition

A neural network model of the mechanism of selective attention in visual pattern recognition is proposed and simulated on a digital computer.When a complex figure consisting of two patterns or more is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the model is paying selective attention is affected by noise or defects, the model can recall the complete pattern from which the noise has been eliminated and the defects corrected. It is not necessary for perfect recall that the stimulus pattern should be identical in shape to the training pattern. Even though the pattern is distorted in shape or changed in size, it can be correctly recognized and the missing portions restored.The model consists of a hierarchical neural network which has efferent as well as afferent connections between cells. The afferent and the efferent signals interact with each other in the network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent ones, and, at the same time, the afferent signals gate efferent signal flow. When some feature in the stimulus is not extracted in the afferent paths, the threshold for detection of that feature is automatically lowered by decreasing the efficiency of inhibition, and the model tries to extract even vague traces of the undetected feature.     

Human perception of shape from touch

In this paper, I focus on the role of active touch in three aspects of shape perception and discrimination studies. First an overview is given of curvature discrimination experiments. The most prominent result is that first-order stimulus information (that is, the difference in attitude or slope over the stimulus) is the dominant factor determining the curvature threshold. Secondly, I compare touch under bimanual and two-finger performance with unimanual and one-finger performance. Consistently, bimanual or two-finger performance turned out to be worse. The most likely explanation for the former finding is that a loss of accuracy during intermanual comparisons is owing to interhemispheric relay. Thirdly, I address the presence of strong after-effects after just briefly touching a shape. These after-effects have been measured and studied in various conditions (such as, static, dynamic, transfer to other hand or finger). Combination of the results of these studies leads to the insight that there are possibly different classes of after-effect: a strong after-effect, caused by immediate contact with the stimulus, that does only partially transfer to the other hand, and one much less strong after-effect, caused by moving over the stimulus for a certain period, which shows a full transfer to other fingers.     

Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise

We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the “remnant” body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.     

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.     

Contrast, probability, and saccadic latency; evidence for independence of detection and decision

Many factors influence how long it takes to respond to a visual stimulus. The lowest-level factors, such as luminance and contrast, determine how easily different elements of a target can be detected. Higher-level factors are to do with whether these elements constitute a stimulus requiring a response; they include prior probability and urgency. It is natural to think of these two processes, detection and decision, as occurring in series, so that overall reaction time is essentially the sum of the contributions of each stage. Here, measurements of saccadic latency to visual targets whose contrast and prior probability are systematically manipulated demonstrate that there are indeed separable stages of detection and decision. Both can be quantitatively described by rise-to-threshold mechanisms; the average rate of rise of the first is a simple logarithmic function of target contrast, whereas the second shows the linear rise characteristic of the LATER model of neural decision making. The implication is that under normal, high-contrast conditions, in which detection is very fast, the random variability that is characteristic of all reaction times is not caused by sensory noise but is gratuitously introduced by the brain itself; paradoxically, by conferring unpredictability it may aid an organism's survival.     

Temporal gap detection in tactile channels

The ability of observers to detect temporal gaps in bursts of sinusoids or bursts of band-limited noise was measured to assess the temporal acuity of Pacinian (P) and non-Pacinian (NP) tactile information processing channels. The P channel was isolated by delivering high frequency sinusoids or high frequency noise through a large 1.5-cm2 contactor to the thenar eminence. The NP channels were isolated from the P channel by delivering these stimuli as well as stimuli with lower frequencies through a small 0.01-cm2 contactor to the same site. Gap detection thresholds were higher for gaps in noise than for gaps in sinusoids but did not differ among conditions designed to isolate P and NP channels. The finding that temporal acuity does not differ among channels supports the hypothesis that, after termination of a stimulus, the P and NP channels exhibit the same amount of neural persistence. Also consistent with this hypothesis are the earlier findings that the enhancement of the sensation magnitude of a stimulus by a prior stimulus (Verrillo and Gescheider, Percept Psychophys 18: 128-136, 1975) and the duration of sensation after the termination of a stimulus (Gescheider et al., J Acoust Soc Am 91: 1690-1696, 1992) are independent of stimulus frequency. One important implication of this hypothesis, if true, is that the presence of temporal summation in the P channel and its absence in the NP channels, results, not from the lack of neural persistence in the NP channels, but instead, in marked contrast to the P channel, from the lack of a mechanism for integrating persistent neural activity over time.     

Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli

Encoding features of spatiotemporally varying stimuli is quite important for understanding the neural mechanisms of various sensory coding. Temporal coding can encode features of time-varying stimulus, and population coding with temporal coding is adequate for encoding spatiotemporal correlation of stimulus features into spatiotemporal activity of neurons. However, little is known about how spatiotemporal features of stimulus are encoded by spatiotemporal property of neural activity. To address this issue, we propose here a population coding with burst spikes, called here spatiotemporal burst (STB) coding. In STB coding, the temporal variation of stimuli is encoded by the precise onset timing of burst spike, and the spatiotemporal correlation of stimuli is emphasized by one specific aspect of burst firing, or spike packet followed by silent interval. To show concretely the role of STB coding, we study the electrosensory system of a weakly electric fish. Weakly electric fish must perceive the information about an object nearby by analyzing spatiotemporal modulations of electric field around it. On the basis of well-characterized circuitry, we constructed a neural network model of the electrosensory system. Here we show that STB coding encodes well the information of object distance and size by extracting the spatiotemporal correlation of the distorted electric field. The burst activity of electrosensory neurons is also affected by feedback signals through synaptic plasticity. We show that the control of burst activity caused by the synaptic plasticity leads to extracting the stimulus features depending on the stimulus context. Our results suggest that sensory systems use burst spikes as a unit of sensory coding in order to extract spatiotemporal features of stimuli from spatially distributed stimuli.     

Spike correlation measures that eliminate stimulus effects in response to white noise

When measured in response to non-repeating white noise, standard covariance measures of two neuronal spike trains contain components due simply to a shared stimulus. We argue that, without stimulus repeats, model-free measures cannot in general remove these stimulus-induced components. We present spike correlation measures that eliminate them when the neural response can be approximated by a linear-nonlinear system. One of these measures fully characterizes the correlations in the special case that all remaining correlations are due to small reciprocal connections between the neurons. In addition, we demonstrate that the proposed measures can give accurate results with a more realistic, integrate-and-fire model of neural response, provided that it is driven like a linear-nonlinear system.     

周期激励下FHN模型的两种多峰分布放电节律

文章揭示了外界周期脉冲激励下神经元系统产生的随机整数倍和混沌多峰放电节律的关系.随机节律统计直方图呈多峰分布、峰值指数衰减、不可预报且复杂度接近1;混沌节律统计直方图呈不同的多峰分布,峰值非指数衰减、有一定的可预报性且复杂度小于1.混沌节律在激励脉冲周期小于系统内在周期且刺激强度较大时产生,参数范围较小;而随机节律在激励脉冲周期大于系统内在周期且脉冲刺激强度小时,可与随机因素共同作用而产生,产生的参数范围较大.上述结果揭示了两类节律的动力学特性,为区分两类节律提供了实用指标.     

Neural field theory of synaptic plasticity

Plasticity is crucial to neural development, learning, and memory. In the common in vivo situation where postsynaptic neural activity results from multiple presynaptic inputs, it is shown that a widely used class of correlation-dependent and spike-timing dependent plasticity rules can be written in a form that can be incorporated into neural field theory, which enables their system-level dynamics to be investigated. It is shown that the resulting plasticity dynamics depends strongly on the stimulus spectrum via overall system frequency responses. In the case of perturbations that are approximately linear, explicit formulas are found for the dynamics in terms of stimulus spectra via system transfer functions. The resulting theory is applied to a simple model system to reveal how collective effects, especially resonances, can drastically modify system-level plasticity dynamics from that implied by single-neuron analyses. The simplified model illustrates the potential relevance of these effects in applications to brain stimulation, synaptic homeostasis, and epilepsy.     

Recovering the contents of consciousness in the noise of neuroimaging

A classical approach in the neurosciences is to study neural activity modulations induced by a stimulus, a task, etc. This approach is anchored in a behaviourist culture and has proven informative within certain limits. The present paper shows that this approach nonetheless neglects aspects of neural activity that can also contribute important information about brain function. Over the last years, the contributions with the strongest impact on progress in cognitive neuroscience have used other approaches that exploit a spatial or temporal variability of neural activity that standard analyses consider as noise and hence do not take into account. By applying multi-variate analyses, spatial variability of evoked responses has permitted decoding sensory and cognitive representations in the brain. Temporal variability of ongoing neural activity influences how stimuli are perceived trial by trial as well as the associated evoked responses which points out the importance of spontaneous brain activity for cognition. We describe these two kind of approaches based on experiments using functional neuroimaging but the conclusions generalize to other techniques applied in the neurosciences.     

<Emphasis Type="BoldItalic">In vivo</Emphasis> conditions influence the coding of stimulus features by bursts of action potentials

The functional role of burst firing (i.e. the firing of packets of action potentials followed by quiescence) in sensory processing is still under debate. Should bursts be considered as unitary events that signal the presence of a particular feature in the sensory environment or is information about stimulus attributes contained within their temporal structure? We compared the coding of stimulus attributes by bursts in vivo and in vitro of electrosensory pyramidal neurons in weakly electric fish by computing correlations between burst and stimulus attributes. Our results show that, while these correlations were strong in magnitude and significant in vitro, they were actually much weaker in magnitude if at all significant in vivo. We used a mathematical model of pyramidal neuron activity in vivo and showed that such a model could reproduce the correlations seen in vitro, thereby suggesting that differences in burst coding were not due to differences in bursting seen in vivo and in vitro. We next tested whether variability in the baseline (i.e. without stimulation) activity of ELL pyramidal neurons could account for these differences. To do so, we injected noise into our model whose intensity was calibrated to mimic baseline activity variability as quantified by the coefficient of variation. We found that this noise caused significant decreases in the magnitude of correlations between burst and stimulus attributes and could account for differences between in vitro and in vivo conditions. We then tested this prediction experimentally by directly injecting noise in vitro through the recording electrode. Our results show that this caused a lowering in magnitude of the correlations between burst and stimulus attributes in vitro and gave rise to values that were quantitatively similar to those seen under in vivo conditions. While it is expected that noise in the form of baseline activity variability will lower correlations between burst and stimulus attributes, our results show that such variability can account for differences seen in vivo. Thus, the high variability seen under in vivo conditions has profound consequences on the coding of information by bursts in ELL pyramidal neurons. In particular, our results support the viewpoint that bursts serve as a detector of particular stimulus features but do not carry detailed information about such features in their structure.