Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivative-free optimizers, on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, including `vanilla' BO, showing great promise for advanced BO techniques, and BADS in particular, as a general model-fitting tool.

Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivative-free optimizers, on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, including `vanilla' BO, showing great promise for advanced BO techniques, and BADS in particular, as a general model-fitting tool.

Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary ------- The authors study stochastic optimal control problems with incomplete state information. In particular, they consider problems where the sensors are adaptive. They compare sensor configurations which are optimal under a standard signal detection paradigm with sensor configurations that are optimal for a given control problem. Comments -------- Optimal sensor adaptation has a long history in computational neuroscience.

Users of a naturally occurring psychedelic drug are convinced they've encountered real alien beings, including'machine elves,' which inhabit a realm beyond our Earth. These machine elves, described as chattering, mischievous entities, consistently appear in the visions of those who take DMT, which one neuroscientist suggested could mean users are actually entering a shared alien reality. DMT (or N,N-Dimethyltryptamine) is present in thousands of plants, including ayahuasca, which is used in religious ceremonies, but is also present in small amounts within the human body. Dr Andrew Gallimore, who has a PhD was in biological chemistry and has studied computational neuroscience, said he encountered these beings firsthand after being transported to a hyper-dimensional world teeming with intelligent lifeforms. Unlike earthly creatures, these beings - ranging from insectoids to God-like figures -seem to exist in a space that defies our three-dimensional understanding.

I'm mainly going to comment on the execution of the paper since I'm currently not very knowledgeable in the computational neuroscience of navigation in the brain: -Although it is easy to understand the paper content at a high level, I found it quite difficult to understand some important details, requiring multiple passes over the text to make sense of them. Examples: i) There are non-bold letters that denote continuous distributions over space (G, P), and boldfaced versions of them that represent "discretized" vectors that are grid and place cell responses. Is this mapping a simple discretization of the support of the probability functions? If not, what is the mapping? I guess this is a discretization at landmark locations for place cells (one landmark per place cell). Is it the same thing for the grid cells?

This paper presents a biologically plausible construction of Gaussian process regression. The 4 reviewers were split into two camps (two strong accepts and two rejects), where one argued that the paper was an exciting and significant contribution to computational neuroscience and the other arguing that the GP construction and empirical evaluation were insufficient for an ML paper. There was extensive discussion, with the ML camp agreeing that they wouldn't argue strongly against acceptance if the work is indeed interesting to computational neuroscience. As NeurIPS includes computational neuroscience as a focus area and the reviewers focusing on that aspect found the work very exciting, it would seem this paper could be quite interesting to researchers in that sub-community.

Major comments: This paper contains a nice idea, namely, a weight matrix which is architecturally constrained to use a certain learning rule and update itself at various points during processing. This general scheme seems likely to lead to many variants in the future. The performance on the tasks considered is solid, and makes the technique worthy of further consideration. This paper makes a solid contribution to machine learning, but the results in the paper do not support the claim in the conclusion that "the main contribution is to computational neuroscience and cognitive science." The paper makes no contact with experimental data, whether neural or psychological.

Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivativefree optimizers, on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, including'vanilla' BO, showing great promise for advanced BO techniques, and BADS in particular, as a general model-fitting tool.