Optimal decision bounds for probabilistic population codes and time varying evidence

Decision making under time constraints requires the decision maker to trade off between making quick, inaccurate decisions and gathering more evidence for more accurate, but slower decisions. We have previously shown that, under rather general settings, optimal behavior can be described by a time-dependent decision bound on the decision maker’s belief of being correct (Drugowitsch, Moreno-Bote, Pouget, 2009). In cases where the reliability of sensory information remains constant over time, we have shown how to design diffusion models (DMs) with time-changing boundaries that feature such behavior. Such theories can be easily mapped onto simple neural models of decision making with two perfectly anti-correlated neurons, where they predict the existence of a stopping bound on the most active neurons. It is unclear however how the stopping bound would be implemented with more realistic neural population codes, particularly when the reliability of the evidence changes over time.
Here we show that, under certain realistic conditions, we can apply the theory of optimal decision making to the biologically more plausible probabilistic population codes (PPCs; Ma et al. 2006). Our analysis shows that, with population codes, the optimal decision bounds are a function of the neural activity of all neurons in the population, rather than a previously postulated bound on its maximum activity. This theory predicts that the bound on the most active neurons would appear to shift depending on the firing rate of other neurons in the population, a puzzling behavior under the drift diffusion model as it would wrongly suggest that subjects change their stopping rule across conditions. This theory also applies to the case of time varying evidence, a case that cannot be handled by drift diffusion models without unrealistic assumptions

Maximizing decision rate in multisensory integration

Effective decision-making in an uncertain world requires making use of all available information, even if distributed across different sensory modalities, as well as trading off the speed of a decision with its accuracy. In tasks with a fixed stimulus presentation time, animal and human subjects have previously been shown to combine information from several modalities in a statistically optimal manner. Furthermore, for easily discriminable stimuli and under the assumption that reaction times result from a race-to-threshold mechanism, multimodal reaction times are typically faster than predicted from unimodal conditions when assuming independent (parallel) races for each modality. However, due to a lack of adequate ideal observer models, it has remained unclear whether subjects perform optimal cue combination when they are allowed to choose their response times freely.
Based on data collected from human subjects performing a visual/vestibular heading discrimination task, we show that the subjects exhibit worse discrimination performance in the multimodal condition than predicted by standard cue combination criteria, which relate multimodal discrimination performance to sensitivity in the unimodal conditions. Furthermore, multimodal reaction times are slower than those predicted by a parallel race model, opposite to what is commonly observed for easily discriminable stimuli.
Despite violating the standard criteria for optimal cue combination, we show that subjects still accumulate evidence optimally across time and cues, even when the strength of the evidence varies with time. Additionally, subjects adjust their decision bounds, controlling the trade-off between speed and accuracy of a decision, such that they feature correct decision rates close to the maximum achievable value

Optimal policy for value-based decision-making

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down

Probabilistic Synapses

Learning, especially rapid learning, is critical for survival. However, learning is hard: a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights - not just point estimates - is the optimal strategy. Here we hypothesize that synapses take that optimal strategy: they do not store just the mean weight; they also store their degree of uncertainty - in essence, they put error bars on the weights. They then use that uncertainty to adjust their learning rates, with higher uncertainty resulting in higher learning rates. We also make a second, independent, hypothesis: synapses communicate their uncertainty by linking it to variability, with more uncertainty leading to more variability. More concretely, the value of a synaptic weight at a given time is a sample from its probability distribution. These two hypotheses cast synaptic plasticity as a problem of Bayesian inference, and thus provide a normative view of learning. They are consistent with known learning rules, offer an explanation for the large variability in the size of post-synaptic potentials, and make several falsifiable experimental predictions

VirtualEnaction: A Platform for Systemic Neuroscience Simulation.

International audienceConsidering the experimental study of systemic models of the brain as a whole (in contrast to models of one brain area or aspect), there is a real need for tools designed to realistically simulate these models and to experiment them. We explain here why a robotic setup is not necessarily the best choice, and what are the general requirements for such a bench-marking platform. A step further, we describe an effective solution, freely available on line and already in use to validate functional models of the brain. This solution is a digital platform where the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital somesthetic cues, complex survival behaviors can be experimented. The platform is also complemented with algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier.Dans le domaine de l'étude expérimentale des modèles systémiques du cerveau pris dans son ensemble (par opposition aux modèles de la zone du cerveau ou une image), il y a un réel besoin d'outils conçus pour simuler de manière réaliste ces modèles et les expérimenter. Nous expliquons ici pourquoi une installation robotique n'est pas nécessairement le meilleur choix, et quelles sont les exigences générales d'une telle plateforme en terme de benchmarking. Nous décrivons alors une solution efficace, disponible gratuitement en ligne et déjà utilisées pour valider les modèles fonctionnels du cerveau. Cette solution est une plate-forme numérique où un "brainy-bot" implémente le modèle étudié et permet son intégration dans un environnement de survie contrôlé, simplifié, mais réaliste. Des entrées visuelles, tactiles et olfactive, un corps très simplifié, un bras et une commandande mootrice des yeux, en plus de la somesthésie des variavles vitales sont disponibles. De ce fait, des comportements de survie complexes peuvent être expérimentées. La plate-forme est également complétée par des modules algorithmiques de simulation de fonctions cognitives de haut niveau, facilitant le travail de construction de comportement biologiquement plausibles

Microwave dielectric study of spin-Peierls and charge ordering transitions in (TMTTF)2_2PF6_6 salts

We report a study of the 16.5 GHz dielectric function of hydrogenated and deuterated organic salts (TMTTF)$_2$PF$_6$. The temperature behavior of the dielectric function is consistent with short-range polar order whose relaxation time decreases rapidly below the charge ordering temperature. If this transition has more a relaxor character in the hydrogenated salt, charge ordering is strengthened in the deuterated one where the transition temperature has increased by more than thirty percent. Anomalies in the dielectric function are also observed in the spin-Peierls ground state revealing some intricate lattice effects in a temperature range where both phases coexist. The variation of the spin-Peierls ordering temperature under magnetic field appears to follow a mean-field prediction despite the presence of spin-Peierls fluctuations over a very wide temperature range in the charge ordered state of these salts.Comment: 7 pages, 6 figure

From biological to numerical experiments in systemic neuroscience: a simulation platform

International audienceStudying and modeling the brain as a whole is a real challenge. For such systemic models (in contrast to models of one brain area or aspect), there is a real need for new tools designed to perform complex numerical experiments, beyond usual tools distributed in the computer science and neuroscience communities. Here, we describe an effective solution, freely available on line and already in use, to validate such models of the brain functions. We explain why this is the best choice, as a complement to robotic setup, and what are the general requirements for such a benchmarking platform. In this experimental setup, the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital interoceptive cues, complex survival behaviors can be experimented. We also discuss here algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier. The key point is to possibly alternate the use of symbolic representation and of complementary and usual neural coding. As a consequence, algorithmic principles have to be considered at higher abstract level, beyond a given data representation, which is an interesting challenge

CommsChem

Connecting chemical properties to various wine characteristics is of great interest to the science of olfaction as well as the wine industry. We explored whether Bordeaux wine chemical identities and vintages (harvest year) can be inferred from a common and affordable chemical analysis, namely, a combination of gas chromatography (GC) and electron ionization mass spectrometry. Using 12 vintages (within the 1990–2007 range) from 7 estates of the Bordeaux region, we report that, remarkably, nonlinear dimensionality reduction techniques applied to raw gas chromatograms recover the geography of the Bordeaux region. Using machine learning, we found that we can not only recover the estate perfectly from gas chromatograms, but also the vintage with up to 50% accuracy. Interestingly, we observed that the entire chromatogram is informative with respect to geographic location and age, thus suggesting that the chemical identity of a wine is not defined by just a few molecules but is distributed over a large chemical spectrum. This study demonstrates the remarkable potential of GC analysis to explore fundamental questions about the origin and age of wine. © 2023, The Author(s)