Multi-Timescale Perceptual History Resolves Visual Ambiguity

When visual input is inconclusive, does previous experience aid the visual system in attaining an accurate perceptual interpretation? Prolonged viewing of a visually ambiguous stimulus causes perception to alternate between conflicting interpretations. When viewed intermittently, however, ambiguous stimuli tend to evoke the same percept on many consecutive presentations. This perceptual stabilization has been suggested to reflect persistence of the most recent percept throughout the blank that separates two presentations. Here we show that the memory trace that causes stabilization reflects not just the latest percept, but perception during a much longer period. That is, the choice between competing percepts at stimulus reappearance is determined by an elaborate history of prior perception. Specifically, we demonstrate a seconds-long influence of the latest percept, as well as a more persistent influence based on the relative proportion of dominance during a preceding period of at least one minute. In case short-term perceptual history and long-term perceptual history are opposed (because perception has recently switched after prolonged stabilization), the long-term influence recovers after the effect of the latest percept has worn off, indicating independence between time scales. We accommodate these results by adding two positive adaptation terms, one with a short time constant and one with a long time constant, to a standard model of perceptual switching

Dynamics of temporally interleaved percept-choice sequences: interaction via adaptation in shared neural populations

At the onset of visually ambiguous or conflicting stimuli, our visual system quickly ‘chooses’ one of the possible percepts. Interrupted presentation of the same stimuli has revealed that each percept-choice depends strongly on the history of previous choices and the duration of the interruptions. Recent psychophysics and modeling has discovered increasingly rich dynamical structure in such percept-choice sequences, and explained or predicted these patterns in terms of simple neural mechanisms: fast cross-inhibition and slow shunting adaptation that also causes a near-threshold facilitatory effect. However, we still lack a clear understanding of the dynamical interactions between two distinct, temporally interleaved, percept-choice sequences—a type of experiment that probes which feature-level neural network connectivity and dynamics allow the visual system to resolve the vast ambiguity of everyday vision. Here, we fill this gap. We first show that a simple column-structured neural network captures the known phenomenology, and then identify and analyze the crucial underlying mechanism via two stages of model-reduction: A 6-population reduction shows how temporally well-separated sequences become coupled via adaptation in neurons that are shared between the populations driven by either of the two sequences. The essential dynamics can then be reduced further, to a set of iterated adaptation-maps. This enables detailed analysis, resulting in the prediction of phase-diagrams of possible sequence-pair patterns and their response to perturbations. These predictions invite a variety of future experiments

Spike-Interval Triggered Averaging Reveals a Quasi-Periodic Spiking Alternative for Stochastic Resonance in Catfish Electroreceptors

<div><p>Catfish detect and identify invisible prey by sensing their ultra-weak electric fields with electroreceptors. Any neuron that deals with small-amplitude input has to overcome sensitivity limitations arising from inherent threshold non-linearities in spike-generation mechanisms. Many sensory cells solve this issue with stochastic resonance, in which a moderate amount of intrinsic noise causes irregular spontaneous spiking activity with a probability that is modulated by the input signal. Here we show that catfish electroreceptors have adopted a fundamentally different strategy. Using a reverse correlation technique in which we take spike interval durations into account, we show that the electroreceptors generate a supra-threshold bias current that results in quasi-periodically produced spikes. In this regime stimuli modulate the interval between successive spikes rather than the instantaneous probability for a spike. This alternative for stochastic resonance combines threshold-free sensitivity for weak stimuli with similar sensitivity for excitations and inhibitions based on single interspike intervals.</p> </div

Activation-threshold tuning in an affinity model for the T-cell repertoire

Naive T cells respond to peptides from foreign proteins and remain tolerant to self peptides from endogenous proteins. It has been suggested that self tolerance comes about by a 'tuning' mechanism, i.e. by increasing the T-cell activation threshold upon interaction with self peptides. Here, we explore how such an adaptive mechanism of T-cell tolerance would influence the reactivity of the T-cell repertoire to foreign peptides. We develop a computer simulation model in which T cells are tolerized by increasing their activation-threshold dependent on the affinity with which they see self peptides presented in the thymus. Thus, different T cells acquire different activation thresholds (i.e. different cross-reactivities). In previous mathematical models, T-cell tolerance was deletional and based on a fixed cross-reactivity parameter, which was assumed to have evolved to an optimal value. Comparing these two different tolerance-induction mechanisms, we found that the tuning model performs somewhat better than an optimized deletion model in terms of the reactivity to foreign antigens. Thus, evolutionary optimization of clonal cross-reactivity is not required. A straightforward extension of the tuning model is to delete T-cell clones that obtain a too high activation threshold, and to replace these by new clones. The reactivity of the immune repertoires of such a replacement model is enchanced compared with the basic tuning model. These results demonstrate that activation-threshold tuning is a functional mechanism for self tolerance induction

Two-interval SITAs.

<p>Each panel shows a single class of intervals subdivided according to the preceding interval. Interval durations are indicated by the insets in each graph. The black horizontal lines in the inset show the mean duration of the interval immediately preceding the trigger spike (at t = 0). The colored lines represent mean interval durations for the preceding intervals, with colors corresponding to the different curves. Color codes are similar to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032786#pone-0032786-g001" target="_blank">Figure 1</a>. Thick lines correspond to measured data, thin lines to predictions based on linear summation of separate and independent SITAs for the two consecutive intervals. In calculating the linear sum of the SITAs for the first of the two intervals we used a time shift equal to the mean interval duration for the second interval (black horizontal line in insets). Linear predictions and actual measurements are highly similar, indicating that adding a second interval to the analysis provides no information that was not already present in the single interval analysis.</p

Reverse correlation results at different stimulus amplitudes.

<p>Experimental data (left column) and model simulations (right column). Model predictions were based on simulations with model parameters that were obtained by fitting the model to data from a standard reverse correlation experiment (third row of data, amplitude of 1). Noise amplitudes were varied by a factor of two between successive rows. Model simulations and actual measurements show very similar effects. At small stimulus amplitudes, SITAs for long and short intervals have similar shapes and comparable latencies. At higher stimulus amplitudes, shapes and latencies for different interval classes change drastically. Typically, inhibitory deflections become delayed relative to excitatory deflections and they may generate a short latency excitatory peak.</p

The Filter-LIF model.

<p>(A) Schematic diagram. The model consists of a linear band-pass filter (<i>F(t)</i>), taking the stimulus (<i>S(t)</i>) plus added noise (<i>N₁(t)</i>) as its input, followed by a standard LIF spike generation mechanism. The LIF spike generator performs a leaky integration of the filter output plus a second noise source (<i>N₂(t)</i>). This second noise source corresponds to a high frequency noise on the spike threshold. If the integrated signal crosses the threshold level (<i>h</i>), a spike is generated and the integrator is reset to a value of −100. (B) Model behavior for quasi-periodic spiking. (C) The behavior in a regime where spike generation is strongly driven by noise and external input signals. The linear filter stages were the same for both simulations, except for a gain factor. The top panels show examples of output signals from the linear filters. The second row displays the course of the ‘membrane potential’, including reset and post-spike recovery. The two regimes differ in the setting for the threshold (blue line) relative to the resting level (green line) and reset level (red line). For deterministic firing (B) the threshold is set well below the resting level, whereas for input-driven firing (C) it is set above the resting level. The bottom row panels represent the SITAs, with interval classes corresponding to the colors in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032786#pone-0032786-g001" target="_blank">Fig. 1</a>.</p

Bode plots comparing reverse correlation data and sinusoidal stimulation data for an example electroreceptor.

<p>(A) Amplitudes, (B) Phases. Grey lines with symbols show experimental measurements for sinusoidal stimulation. The other lines are based on Fourier transforms of STA and SITA data (see legend). The inset in (A) shows the example SITAs from the reverse correlation data that were used for these Fourier transforms.</p