Adapting to time: why nature evolved a diverse set of neurons

Evolution has yielded a diverse set of neurons with varying morphologies and physiological properties that impact their processing of temporal information. In addition, it is known empirically that spike timing is a significant factor in neural computations. However, despite these two observations, most neural network models deal with spatially structured inputs with synchronous time steps, while restricting variation to parameters like weights and biases. In this study, we investigate the relevance of adapting temporal parameters, like time constants and delays, in feedforward networks that map spatio-temporal spike patterns. In this context, we show that networks with richer potential dynamics are able to more easily and robustly learn tasks with temporal structure. Indeed, when adaptation was restricted to weights, networks were unable to solve most problems. We also show strong interactions between the various parameters and the advantages of adapting temporal parameters when dealing with noise in inputs and weights, which might prove useful in neuromorphic hardware design.

MindSet: Vision. A toolbox for testing DNNs on key psychological experiments

Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses to naturalistic images that have not been manipulated to test hypotheses regarding how DNNs or humans perceive and identify objects. Here we introduce the toolbox MindSet: Vision, consisting of a collection of image datasets and related scripts designed to test DNNs on 30 psychological findings. In all experimental conditions, the stimuli are systematically manipulated to test specific hypotheses regarding human visual perception and object recognition. In addition to providing pre-generated datasets of images, we provide code to regenerate these datasets, offering many configurable parameters which greatly extend the dataset versatility for different research contexts, and code to facilitate the testing of DNNs on these image datasets using three different methods (similarity judgments, out-of-distribution classification, and decoder method), accessible at https://github.com/MindSetVision/mindset-vision. We test ResNet-152 on each of these methods as an example of how the toolbox can be used.