Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish

Redundant Input Cancellation by a Bursting Neural Network

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs

<i>In vivo</i> electrophysiological observations.

<p>(A) Response of SP cells to AM stimuli, for the case in which the signal is local (black) or global (gray). For each case, a single extracellular recording trial and a raster plot are shown. (B) Cancellation of global stimuli for different AM frequencies. Upper row corresponds to local stimulation while lower row corresponds to global stimulation. In each panel, the mean PSTH ( neurons from several fish) for contrasts of (red), (green), (blue) and (violet) is displayed. The inset in the lower left panel shows the extent of the typical error bars for one of these curves (). (C) Percentage of cancellation as a function of input contrast, for different AM frequencies. (D) Degradation of the signal cancellation, averaged over all AM frequencies considered, as a function of input contrast.</p

Fitting of the model to local response.

<p>(A) Maximum firing rate (solid black line) reached by the model SP neuron as a function of the amplitude of the sine wave resembling the input from P-units. The AM frequency is , and similar responses were found for other frequencies. Colored lines indicate the maximum firing rate observed in the experiments for four different AM contrasts. (B) By considering the crossing points between the black line and the colored lines in panel A, we establish a dependence between AM contrasts (i.e. input to P-units) and amplitude of the signal arriving at the SP cell, , and thus obtaining an AM input-output function for the P-units. Points denote frequency-averaged quantities, while bars denote the standard deviation of each average. (C) Once this P-unit nonlinear feature is considered, the model (solid lines) is able to properly fit the experimental observations (points, shown previously in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003180#pcbi-1003180-g001" target="_blank">Fig. 1B</a>) for different input frequencies and contrasts of the local stimulus. For panels A and C, the color code for contrast is red ( contrast), green (), blue () and violet ().</p

Cancellation of global stimulus with feedback saturation.

<p>(A) Model predictions of the level of cancellation of global signals as a function of contrast, for different signal AM frequencies (colored lines) and different learning contrasts considered (different panels). We have introduced here a factor which takes into account the saturation of the feedback pathway for high stimulus contrasts. The gray line indicates the frequency-average cancellation levels measured experimentally (from data in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003180#pcbi-1003180-g001" target="_blank">Fig. 1C</a>). (B) Degradation level (defined as the quantity , once frequency is averaged) as a function of the contrast for different learning contrasts (red , green , blue , violet ). Symbols denote experimental data. As we can see, the optimal learning contrast is around .</p

Schematic diagram of the model considered.

<p>The network architecture considered in the model involves the feedforward circuit (in green) and the indirect feedback pathway (in blue), which is active only for global stimulus.</p

Cancellation at different frequencies and contrasts.

<p>Comparison between experimental data (points) and model predictions (lines) for the cancellation of a global signal for different AM frequencies (columns) and contrasts (red contrast, green contrast, blue contrast, violet contrast). In each panel, the corresponding experimentally measured SP response to local stimulus is also shown in gray. The learning contrast chosen for the model was , which optimizes the agreement between model predictions and experimental data.</p

Parameter values for the equations of the model which approximate experimental data.

<p>Parameter values for the equations of the model which approximate experimental data.</p