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Hendler, O.

Publications and source records attributed to Hendler, O..

3 recordsLinked to original sources

Decision-Making Dynamics Mask the True Psychophysical Capacity of Archerfish

To quantify animals perceptual capabilities, studies typically assess behavioral accuracy, the proportion of correct choices accumulated over trials on a given task. However, recent works on humans and rodents have shown that task decisions exhibit dynamic shifts from trial to trial, thus casting doubt on the reliability of behavioral accuracy as a true measure of capabilities. Here, these decision-making dynamics were tested on archerfish, an animal lacking a fully developed cortex, whose behavioral decisions are easy to read out. We conducted a series of experiments involving a two-alternative choice task where a target and a non-target shape were randomly placed in two possible positions. Fitting dynamic generalized linear models to each fishs binary choice data revealed that target position strongly affected accuracy and that this effect fluctuated over a timescale of [~]100 trials. The archerfish often repeated their choices regardless of the reward: they frequently selected one target or non-target shape on numerous consecutive trials, which is suggestive of high object recognition capacity. Importantly, the findings indicated that similar latent decision variables underlying mammalian decision-making, such as choice history, were also operational in the archerfish. Then, to investigate behavioral accuracy in more detail, we introduced unrewarded probe trials. Unlike the findings reported in rodents, archerfish performance remained stable during these unrewarded trials. Finally, a decision-making paradigm with stimuli at multiple locations yielded results that were consistent with the simpler task variant. More generally, these findings suggest that an animals decision-making dynamics can mask its true perceptual capabilities when performing an object recognition task, with broad implications for the ways in which behavioral assays are designed and animal performance is interpreted across taxa.

neuroscience↗

Limits of optimal decoding under synaptic coarse-tuning

Sensory information propagates through successive processing stages in the brain, where synaptic weight patterns between stations determine how downstream neurons decode information from upstream populations. Although optimized synaptic connectivity can enhance information transmission, it requires precise weight tuning. Recent evidence depicting substantial synaptic volatility raises two fundamental questions: How does coarse-tuning of synaptic connectivity affect information transmission? What strategies could the nervous system employ to maintain reliable communication despite synaptic fluctuations? We addressed these questions by analyzing the signal-to-noise ratio (SNR) for binary stimulus discrimination under two decoding schemes: a naive population average and an optimized linear decoder. For the naive decoder, we found that SNR remains largely insensitive to synaptic imprecision, since performance is already limited by correlated noise in neuronal responses. For the optimal decoder, we identified three distinct regimes, that is, weak, moderate and strong coarse-tuning. Under weak coarse-tuning, SNR2 scales linearly with population size N. Under moderate coarse-tuning, scaling becomes sublinear. Strikingly, under strong coarse-tuning, the regime most consistent with observed neuronal heterogeneity, SNR saturates and can not be improved by recruiting larger populations. This limitation persists even when incorporating feedforward or recurrent network architectures. These findings suggest that in the biologically relevant regime of strong coarse-tuning, naive and optimal decoders can achieve qualitatively similar performance. The analysis shows that effective readout under synaptic volatility is constrained to an invariant low-dimensional manifold aligned with the naive decoder, potentially pointing to a fundamental principle for robust neural computation in the face of ongoing synaptic remodeling.

neuroscience↗

Winner-take-all fails to account for pop out accuracy

Visual search involves active scanning of the environment to locate objects of interest against a background of irrelevant distractors. One widely accepted theory posits that pop out visual search is computed by a winner-take-all (WTA) competition between contextually modulated cells that form a saliency map. However, previous studies have shown that the ability of WTA mechanisms to accumulate information from large populations of neurons is limited, thus raising the question of whether WTA can underlie pop out visual search. To address this question, we conducted a modeling study to investigate how accurately the WTA mechanism can detect the deviant stimulus in a pop out task. We analyzed two architectures of WTA networks: single-best-cell WTA, where the decision is made based on a single winning cell, and a generalized population-based WTA, where the decision is based on the winning population of similarly tuned cells. Our results show that WTA performance cannot account for the high accuracy found in behavioral experiments. On the one hand, inherent neuronal heterogeneity prevents the single-best-cell WTA from accumulating information even from large populations. On the other, the accuracy of the generalized population-based WTA algorithm is negatively affected by the widely reported noise correlations. These findings suggest the need for revisiting current understandings of the underlying mechanism of pop out visual search put forward to account for observed behavior.

neuroscience↗