1,139 research outputs found
Stochastic resonance in electrical circuits—II: Nonconventional stochastic resonance.
Stochastic resonance (SR), in which a periodic signal in a nonlinear system can be amplified by added noise, is discussed. The application of circuit modeling techniques to the conventional form of SR, which occurs in static bistable potentials, was considered in a companion paper. Here, the investigation of nonconventional forms of SR in part using similar electronic techniques is described. In the small-signal limit, the results are well described in terms of linear response theory. Some other phenomena of topical interest, closely related to SR, are also treate
Episodic synchronization in dynamically driven neurons
We examine the response of type II excitable neurons to trains of synaptic
pulses, as a function of the pulse frequency and amplitude. We show that the
resonant behavior characteristic of type II excitability, already described for
harmonic inputs, is also present for pulsed inputs. With this in mind, we study
the response of neurons to pulsed input trains whose frequency varies
continuously in time, and observe that the receiving neuron synchronizes
episodically to the input pulses, whenever the pulse frequency lies within the
neuron's locking range. We propose this behavior as a mechanism of rate-code
detection in neuronal populations. The results are obtained both in numerical
simulations of the Morris-Lecar model and in an electronic implementation of
the FitzHugh-Nagumo system, evidencing the robustness of the phenomenon.Comment: 7 pages, 8 figure
Multisensory Congruency as a Mechanism for Attentional Control over Perceptual Selection
The neural mechanisms underlying attentional selection of competing neural signals for awareness remains an unresolved issue. We studied attentional selection, using perceptually ambiguous stimuli in a novel multisensory paradigm that combined competing auditory and competing visual stimuli. We demonstrate that the ability to select, and attentively hold, one of the competing alternatives in either sensory modality is greatly enhanced when there is a matching cross-modal stimulus. Intriguingly, this multimodal enhancement of attentional selection seems to require a conscious act of attention, as passively experiencing the multisensory stimuli did not enhance control over the stimulus. We also demonstrate that congruent auditory or tactile information, and combined auditory–tactile information, aids attentional control over competing visual stimuli and visa versa. Our data suggest a functional role for recently found neurons that combine voluntarily initiated attentional functions across sensory modalities. We argue that these units provide a mechanism for structuring multisensory inputs that are then used to selectively modulate early (unimodal) cortical processing, boosting the gain of task-relevant features for willful control over perceptual awareness
A cross-modal investigation into the relationships between bistable perception and a global temporal mechanism
When the two eyes are presented with sufficiently different images, Binocular Rivalry (BR) occurs. BR is a form of bistable perception involving stochastic alternations in awareness between distinct images shown to each eye. It has been suggested that the dynamics of BR are due to the activity of a central temporal process and are linked to involuntary mechanisms of selective attention (aka exogenous attention). To test these ideas, stimuli designed to evoke exogenous attention and central temporal processes were employed during BR observation. These stimuli included auditory and visual looming motion and streams of transient events of varied temporal rate and pattern. Although these stimuli exerted a strong impact over some aspects of BR, they were unable to override its characteristic stochastic pattern of alternations completely. It is concluded that BR is subject to distributed influences, but ultimately, is achieved in neural processing areas specific to the binocular conflict
Computational models of auditory perception from feature extraction to stream segregation and behavior
This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: This is a review study, and as such did not generate any new data.Audition is by nature dynamic, from brainstem processing on sub-millisecond time scales, to segregating and tracking sound sources with changing features, to the pleasure of listening to music and the satisfaction of getting the beat. We review recent advances from computational models of sound localization, of auditory stream segregation and of beat perception/generation. A wealth of behavioral, electrophysiological and imaging studies shed light on these processes, typically with synthesized sounds having regular temporal structure. Computational models integrate knowledge from different experimental fields and at different levels of description. We advocate a neuromechanistic modeling approach that incorporates knowledge of the auditory system from various fields, that utilizes plausible neural mechanisms, and that bridges our understanding across disciplines.Engineering and Physical Sciences Research Council (EPSRC
Fundamental activity constraints lead to specific interpretations of the connectome
The continuous integration of experimental data into coherent models of the
brain is an increasing challenge of modern neuroscience. Such models provide a
bridge between structure and activity, and identify the mechanisms giving rise
to experimental observations. Nevertheless, structurally realistic network
models of spiking neurons are necessarily underconstrained even if experimental
data on brain connectivity are incorporated to the best of our knowledge.
Guided by physiological observations, any model must therefore explore the
parameter ranges within the uncertainty of the data. Based on simulation
results alone, however, the mechanisms underlying stable and physiologically
realistic activity often remain obscure. We here employ a mean-field reduction
of the dynamics, which allows us to include activity constraints into the
process of model construction. We shape the phase space of a multi-scale
network model of the vision-related areas of macaque cortex by systematically
refining its connectivity. Fundamental constraints on the activity, i.e.,
prohibiting quiescence and requiring global stability, prove sufficient to
obtain realistic layer- and area-specific activity. Only small adaptations of
the structure are required, showing that the network operates close to an
instability. The procedure identifies components of the network critical to its
collective dynamics and creates hypotheses for structural data and future
experiments. The method can be applied to networks involving any neuron model
with a known gain function.Comment: J. Schuecker and M. Schmidt contributed equally to this wor
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