Flexible and efficient simulation-based inference for models of decision-making.

Inferring parameters of computational models that capture experimental data are a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model-however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making, but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator, and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations, and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.

Training deep neural density estimators to identify mechanistic models of neural dynamics.

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-trained using model simulations-to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.

Computational neuroscientists use mathematical models built on observational data to investigate what's happening in the brain. Models can simulate brain activity from the behavior of a single neuron right through to the patterns of collective activity in whole neural networks. Collecting the experimental data is the first step, then the challenge becomes deciding which computer models best represent the data and can explain the underlying causes of how the brain behaves. Researchers usually find the right model for their data through trial and error. This involves tweaking a model's parameters until the model can reproduce the data of interest. But this process is laborious and not systematic. Moreover, with the ever-increasing complexity of both data and computer models in neuroscience, the old-school approach of building models is starting to show its limitations. Now, Gonçalves, Lueckmann, Deistler et al. have designed an algorithm that makes it easier for researchers to fit mathematical models to experimental data. First, the algorithm trains an artificial neural network to predict which models are compatible with simulated data. After initial training, the method can rapidly be applied to either raw experimental data or selected data features. The algorithm then returns the models that generate the best match. This newly developed machine learning tool was able to automatically identify models which can replicate the observed data from a diverse set of neuroscience problems. Importantly, further experiments showed that this new approach can be scaled up to complex mechanisms, such as how a neural network in crabs maintains its rhythm of activity. This tool could be applied to a wide range of computational investigations in neuroscience and other fields of biology, which may help bridge the gap between 'data-driven' and 'theory-driven' approaches.

Pre-stimulus phase and amplitude regulation of phase-locked responses are maximized in the critical state.

Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics-a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

Can Serial Dependencies in Choices and Neural Activity Explain Choice Probabilities?

During perceptual decisions the activity of sensory neurons covaries with choice, a covariation often quantified as "choice-probability". Moreover, choices are influenced by a subject's previous choice (serial dependence) and neuronal activity often shows temporal correlations on long (seconds) timescales. Here, we test whether these findings are linked. Using generalized linear models, we analyze simultaneous measurements of behavior and V2 neural activity in macaques performing a visual discrimination task. Both, decisions and spiking activity show substantial temporal correlations and cross-correlations but seem to reflect two mostly separate processes. Indeed, removing history effects using semipartial correlation analysis leaves choice probabilities largely unchanged. The serial dependencies in choices and neural activity therefore cannot explain the observed choice probability. Rather, serial dependencies in choices and spiking activity reflect two predominantly separate but parallel processes, which are coupled on each trial by covariations between choices and activity. These findings provide important constraints for computational models of perceptual decision-making that include feedback signals.SIGNIFICANCE STATEMENT Correlations, unexplained by the sensory input, between the activity of sensory neurons and an animal's perceptual choice ("choice probabilities") have received attention from both a systems and computational neuroscience perspective. Conversely, whereas temporal correlations for both spiking activity ("non-stationarities") and for a subject's choices in perceptual tasks ("serial dependencies") have long been established, they have typically been ignored when measuring choice probabilities. Some accounts of choice probabilities incorporating feedback predict that these observations are linked. Here, we explore the extent to which this is the case. We find that, contrasting with these predictions, choice probabilities are largely independent of serial dependencies, which adds new constraints to accounts of choice probabilities that include feedback.

Spatiotemporal dynamics of random stimuli account for trial-to-trial variability in perceptual decision making.

Decisions in everyday life are prone to error. Standard models typically assume that errors during perceptual decisions are due to noise. However, it is unclear how noise in the sensory input affects the decision. Here we show that there are experimental tasks for which one can analyse the exact spatio-temporal details of a dynamic sensory noise and better understand variability in human perceptual decisions. Using a new experimental visual tracking task and a novel Bayesian decision making model, we found that the spatio-temporal noise fluctuations in the input of single trials explain a significant part of the observed responses. Our results show that modelling the precise internal representations of human participants helps predict when perceptual decisions go wrong. Furthermore, by modelling precisely the stimuli at the single-trial level, we were able to identify the underlying mechanism of perceptual decision making in more detail than standard models.

Ostracism Online: A social media ostracism paradigm.

We describe Ostracism Online, a novel, social media-based ostracism paradigm designed to (1) keep social interaction experimentally controlled, (2) provide researchers with the flexibility to manipulate the properties of the social situation to fit their research purposes, (3) be suitable for online data collection, (4) be convenient for studying subsequent within-group behavior, and (5) be ecologically valid. After collecting data online, we compared the Ostracism Online paradigm with the Cyberball paradigm (Williams & Jarvis Behavior Research Methods, 38, 174-180, 2006) on need-threat and mood questionnaire scores (van Beest & Williams Journal of Personality and Social Psychology 91, 918-928, 2006). We also examined whether ostracized targets of either paradigm would be more likely to conform to their group members than if they had been included. Using a Bayesian analysis of variance to examine the individual effects of the different paradigms and to compare these effects across paradigms, we found analogous effects on need-threat and mood. Perhaps because we examined conformity to the ostracizers (rather than neutral sources), neither paradigm showed effects of ostracism on conformity. We conclude that Ostracism Online is a cost-effective, easy to use, and ecologically valid research tool for studying the psychological and behavioral effects of ostracism.

p53 Regulates the neuronal intrinsic and extrinsic responses affecting the recovery of motor function following spinal cord injury.

Following spinal trauma, the limited physiological axonal sprouting that contributes to partial recovery of function is dependent upon the intrinsic properties of neurons as well as the inhibitory glial environment. The transcription factor p53 is involved in DNA repair, cell cycle, cell survival, and axonal outgrowth, suggesting p53 as key modifier of axonal and glial responses influencing functional recovery following spinal injury. Indeed, in a spinal cord dorsal hemisection injury model, we observed a significant impairment in locomotor recovery in p53(-/-) versus wild-type mice. p53(-/-) spinal cords showed an increased number of activated microglia/macrophages and a larger scar at the lesion site. Loss- and gain-of-function experiments suggested p53 as a direct regulator of microglia/macrophages proliferation. At the axonal level, p53(-/-) mice showed a more pronounced dieback of the corticospinal tract (CST) and a decreased sprouting capacity of both CST and spinal serotoninergic fibers. In vivo expression of p53 in the sensorimotor cortex rescued and enhanced the sprouting potential of the CST in p53(-/-) mice, while, similarly, p53 expression in p53(-/-) cultured cortical neurons rescued a defect in neurite outgrowth, suggesting a direct role for p53 in regulating the intrinsic sprouting ability of CNS neurons. In conclusion, we show that p53 plays an important regulatory role at both extrinsic and intrinsic levels affecting the recovery of motor function following spinal cord injury. Therefore, we propose p53 as a novel potential multilevel therapeutic target for spinal cord injury.