813 research outputs found

    Temporal characteristics of the influence of punishment on perceptual decision making in the human brain

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    Perceptual decision making is the process by which information from sensory systems is combined and used to influence our behavior. In addition to the sensory input, this process can be affected by other factors, such as reward and punishment for correct and incorrect responses. To investigate the temporal dynamics of how monetary punishment influences perceptual decision making in humans, we collected electroencephalography (EEG) data during a perceptual categorization task whereby the punishment level for incorrect responses was parametrically manipulated across blocks of trials. Behaviorally, we observed improved accuracy for high relative to low punishment levels. Using multivariate linear discriminant analysis of the EEG, we identified multiple punishment-induced discriminating components with spatially distinct scalp topographies. Compared with components related to sensory evidence, components discriminating punishment levels appeared later in the trial, suggesting that punishment affects primarily late postsensory, decision-related processing. Crucially, the amplitude of these punishment components across participants was predictive of the size of the behavioral improvements induced by punishment. Finally, trial-by-trial changes in prestimulus oscillatory activity in the alpha and gamma bands were good predictors of the amplitude of these components. We discuss these findings in the context of increased motivation/attention, resulting from increases in punishment, which in turn yields improved decision-related processing

    Human scalp potentials reflect a mixture of decision-related signals during perceptual choices

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    Single-unit animal studies have consistently reported decision-related activity mirroring a process of temporal accumulation of sensory evidence to a fixed internal decision boundary. To date, our understanding of how response patterns seen in single-unit data manifest themselves at the macroscopic level of brain activity obtained from human neuroimaging data remains limited. Here, we use single-trial analysis of human electroencephalography data to show that population responses on the scalp can capture choice-predictive activity that builds up gradually over time with a rate proportional to the amount of sensory evidence, consistent with the properties of a drift-diffusion-like process as characterized by computational modeling. Interestingly, at time of choice, scalp potentials continue to appear parametrically modulated by the amount of sensory evidence rather than converging to a fixed decision boundary as predicted by our model. We show that trial-to-trial fluctuations in these response-locked signals exert independent leverage on behavior compared with the rate of evidence accumulation earlier in the trial. These results suggest that in addition to accumulator signals, population responses on the scalp reflect the influence of other decision-related signals that continue to covary with the amount of evidence at time of choice

    Single-trial analysis of EEG during rapid visual discrimination: enabling cortically-coupled computer vision

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    We describe our work using linear discrimination of multi-channel electroencephalography for single-trial detection of neural signatures of visual recognition events. We demonstrate the approach as a methodology for relating neural variability to response variability, describing studies for response accuracy and response latency during visual target detection. We then show how the approach can be utilized to construct a novel type of brain-computer interface, which we term cortically-coupled computer vision. In this application, a large database of images is triaged using the detected neural signatures. We show how ‘corticaltriaging’ improves image search over a strictly behavioral response

    elPBN neurons regulate rVLM activity through elPBN-rVLM projections during activation of cardiac sympathetic afferent nerves.

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    The external lateral parabrachial nucleus (elPBN) within the pons and rostral ventrolateral medulla (rVLM) contributes to central processing of excitatory cardiovascular reflexes during stimulation of cardiac sympathetic afferent nerves (CSAN). However, the importance of elPBN cardiovascular neurons in regulation of rVLM activity during CSAN activation remains unclear. We hypothesized that CSAN stimulation excites the elPBN cardiovascular neurons and, in turn, increases rVLM activity through elPBN-rVLM projections. Compared with controls, in rats subjected to microinjection of retrograde tracer into the rVLM, the numbers of elPBN neurons double-labeled with c-Fos (an immediate early gene) and the tracer were increased after CSAN stimulation (P < 0.05). The majority of these elPBN neurons contain vesicular glutamate transporter 3. In cats, epicardial bradykinin and electrical stimulation of CSAN increased the activity of elPBN cardiovascular neurons, which was attenuated (n = 6, P < 0.05) after blockade of glutamate receptors with iontophoresis of kynurenic acid (Kyn, 25 mM). In separate cats, microinjection of Kyn (1.25 nmol/50 nl) into the elPBN reduced rVLM activity evoked by both bradykinin and electrical stimulation (n = 5, P < 0.05). Excitation of the elPBN with microinjection of dl-homocysteic acid (2 nmol/50 nl) significantly increased basal and CSAN-evoked rVLM activity. However, the enhanced rVLM activity induced by dl-homocysteic acid injected into the elPBN was reversed following iontophoresis of Kyn into the rVLM (n = 7, P < 0.05). These data suggest that cardiac sympathetic afferent stimulation activates cardiovascular neurons in the elPBN and rVLM sequentially through a monosynaptic (glutamatergic) excitatory elPBN-rVLM pathway

    Output Stream of Binding Neuron with Feedback

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    The binding neuron model is inspired by numerical simulation of Hodgkin-Huxley-type point neuron, as well as by the leaky integrate-and-fire model. In the binding neuron, the trace of an input is remembered for a fixed period of time after which it disappears completely. This is in the contrast with the above two models, where the postsynaptic potentials decay exponentially and can be forgotten only after triggering. The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modeling. Recently, the finiteness is utilized for exact mathematical description of the output stochastic process if the binding neuron is driven with the Poissonian input stream. In this paper, the simplest networking is considered for binding neuron. Namely, it is expected that every output spike of single neuron is immediately fed into its input. For this construction, externally fed with Poissonian stream, the output stream is characterized in terms of interspike interval probability density distribution if the binding neuron has threshold 2. For higher thresholds, the distribution is calculated numerically. The distributions are compared with those found for binding neuron without feedback, and for leaky integrator. Sample distributions for leaky integrator with feedback are calculated numerically as well. It is oncluded that even the simplest networking can radically alter spikng statistics. Information condensation at the level of single neuron is discussed.Comment: Version #1: 4 pages, 5 figures, manuscript submitted to Biological Cybernetics. Version #2 (this version): added 3 pages of new text with additional analytical and numerical calculations, 2 more figures, 11 more references, added Discussion sectio
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