16 research outputs found
Dynamical criticality in the collective activity of a population of retinal neurons
Recent experimental results based on multi-electrode and imaging techniques
have reinvigorated the idea that large neural networks operate near a critical
point, between order and disorder. However, evidence for criticality has relied
on the definition of arbitrary order parameters, or on models that do not
address the dynamical nature of network activity. Here we introduce a novel
approach to assess criticality that overcomes these limitations, while
encompassing and generalizing previous criteria. We find a simple model to
describe the global activity of large populations of ganglion cells in the rat
retina, and show that their statistics are poised near a critical point. Taking
into account the temporal dynamics of the activity greatly enhances the
evidence for criticality, revealing it where previous methods would not. The
approach is general and could be used in other biological networks
On the special role of class-selective neurons in early training
It is commonly observed that deep networks trained for classification exhibit
class-selective neurons in their early and intermediate layers. Intriguingly,
recent studies have shown that these class-selective neurons can be ablated
without deteriorating network function. But if class-selective neurons are not
necessary, why do they exist? We attempt to answer this question in a series of
experiments on ResNet-50s trained on ImageNet. We first show that
class-selective neurons emerge during the first few epochs of training, before
receding rapidly but not completely; this suggests that class-selective neurons
found in trained networks are in fact vestigial remains of early training. With
single-neuron ablation experiments, we then show that class-selective neurons
are important for network function in this early phase of training. We also
observe that the network is close to a linear regime in this early phase; we
thus speculate that class-selective neurons appear early in training as
quasi-linear shortcut solutions to the classification task. Finally, in causal
experiments where we regularize against class selectivity at different points
in training, we show that the presence of class-selective neurons early in
training is critical to the successful training of the network; in contrast,
class-selective neurons can be suppressed later in training with little effect
on final accuracy. It remains to be understood by which mechanism the presence
of class-selective neurons in the early phase of training contributes to the
successful training of networks
Progress and Limitations of Deep Networks to Recognize Objects in Unusual Poses
Deep networks should be robust to rare events if they are to be successfully deployed in high-stakes real-world applications. Here we study the capability of deep networks to recognize objects in unusual poses. We create a synthetic dataset of images of objects in unusual orientations, and evaluate the robustness of a collection of 38 recent and competitive deep networks for image classification. We show that classifying these images is still a challenge for all networks tested, with an average accuracy drop of 29.5% compared to when the objects are presented
upright. This brittleness is largely unaffected by various design choices, such as training losses, architectures, dataset modalities, and data-augmentation schemes. However, networks trained on very large datasets substantially outperform others, with the best network testedâNoisy Student trained on JFT-300Mâshowing a relatively small accuracy drop of only 14.5% on unusual poses. Nevertheless, a visual inspection of the failures of Noisy Student reveals a remaining gap in robustness with humans. Furthermore, combining multiple object transformationsâ3D-rotations and scalingâfurther degrades the performance of all networks. Our results provide another measurement of the robustness of deep networks to consider when using them in the real world. Code and datasets are available at https://github.com/amro-kamal/ObjectPose
Predicting synchronous firing of large neural populations from sequential recordings
International audienceA major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cellsâ responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible
Nonlinear decoding of a complex movie from the mammalian retina.
Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed "pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains
Nonlinear decoding outperforms linear decoding.
<p><b>A</b>: Luminance trace (red) with linear (blue) and nonlinear KRR (green) and neural network (grey) predictions. <b>B</b>: Average decoder performance (± SD across sites), achievable using increasing numbers of cells with highest L1 filter norm. For nonlinear decoding, âAllâ is the optimal subset that maximizes performance (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006057#pcbi.1006057.s007" target="_blank">S7 Fig</a>). Since the neural network (grey point with an error bar) simultaneously decodes the movie at all sites, it only makes sense to train it using âAllâ cells. <b>C</b>: Average ROC across all testing movie frames. <b>D</b>: Fractional improvement (average ± SEM across sites) of nonlinear KRR versus linear decoders for test stimuli with different numbers of discs. All decoders were trained only on the 10-disc stimulus. <b>E</b>: Decoding error (MSE; average ± SEM across sites) in fluctuating and constant epochs is significantly larger for linear decoders (p<0.001) relative to nonlinear KRR and the neural network.</p