In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.
All code is provided freely and is available at the following links: https://github.com/lnccbrown/lans/tree/master/hddmnn-tutorial, https://github.com/lnccbrown/lans/tree/master/al-mlp and https://github.com/lnccbrown/lans/tree/master/al-cnn.
- Michael J Frank
- Michael J Frank
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Valentin Wyart, École normale supérieure, PSL University, INSERM, France
© 2021, Fengler et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Melanopsin signaling within intrinsically photosensitive retinal ganglion cell (ipRGC) subtypes impacts a broad range of behaviors from circadian photoentrainment to conscious visual perception. Yet, how melanopsin phototransduction within M1-M6 ipRGC subtypes impacts cellular signaling to drive diverse behaviors is still largely unresolved. The identity of the phototransduction channels in each subtype is key to understanding this central question but has remained controversial. In this study, we resolve two opposing models of M4 phototransduction, demonstrating that hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are dispensable for this process and providing support for a pathway involving melanopsin-dependent potassium channel closure and canonical transient receptor potential (TRPC) channel opening. Surprisingly, we find that HCN channels are likewise dispensable for M2 phototransduction, contradicting the current model. We instead show that M2 phototransduction requires TRPC channels in conjunction with T-type voltage-gated calcium channels, identifying a novel melanopsin phototransduction target. Collectively, this work resolves key discrepancies in our understanding of ipRGC phototransduction pathways in multiple subtypes and adds to mounting evidence that ipRGC subtypes employ diverse phototransduction cascades to fine-tune cellular responses for downstream behaviors.
Analysis of neuronal activity in the hippocampus of behaving animals has revealed cells acting as ‘Time Cells’, which exhibit selective spiking patterns at specific time intervals since a triggering event, and ‘Distance Cells’, which encode the traversal of specific distances. Other neurons exhibit a combination of these features, alongside place selectivity. This study aims to investigate how the task performed by animals during recording sessions influences the formation of these representations. We analyzed data from a treadmill running study conducted by Kraus et al., 2013, in which rats were trained to run at different velocities. The rats were recorded in two trial contexts: a ‘fixed time’ condition, where the animal ran on the treadmill for a predetermined duration before proceeding, and a ‘fixed distance’ condition, where the animal ran a specific distance on the treadmill. Our findings indicate that the type of experimental condition significantly influenced the encoding of hippocampal cells. Specifically, distance-encoding cells dominated in fixed-distance experiments, whereas time-encoding cells dominated in fixed-time experiments. These results underscore the flexible coding capabilities of the hippocampus, which are shaped by over-representation of salient variables associated with reward conditions.