Grace W. Lindsay's research while affiliated with University College London and other places

Publications (19)

Preprint
Behavioral studies suggest that recurrence in the visual system is important for processing degraded stimuli. There are two broad anatomical forms this recurrence can take, lateral or feedback, each with different assumed functions. Here we add four different kinds of recurrence — two of each anatomical form — to a feedforward convolutional neural...
Preprint
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Neuroscientists apply a range of common analysis tools to recorded neural activity in order to glean insights into how neural circuits implement computations. Despite the fact that these tools shape the progress of the field as a whole, we have little empirical evidence that they are effective at quickly identifying the phenomena of interest. Here...
Preprint
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Artificial neural systems trained using reinforcement, supervised, and unsupervised learning all acquire internal representations of high dimensional input. To what extent these representations depend on the different learning objectives is largely unknown. Here we compare the representations learned by eight different convolutional neural networks...
Article
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Neuromatch Academy (NMA) designed and ran a fully online 3-week Computational Neuroscience Summer School for 1757 students with 191 teaching assistants (TAs) working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity...
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[This corrects the article DOI: 10.3389/fncom.2020.00029.].
Preprint
Full-text available
Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and univers...
Preprint
Full-text available
Selective visual attention modulates neural activity in the visual system in complex ways and leads to enhanced performance on difficult visual tasks. Here, we show that a simple circuit model, the stabilized supralinear network, gives a unified account of a wide variety of effects of attention on neural responses. We replicate results from studies...
Article
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Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between...
Article
Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neurosc...
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Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neurosc...
Article
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, th...
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How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visu...
Preprint
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How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visu...
Article
Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent be...
Preprint
Full-text available
Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by prefrontal cortex (PFC). Neural activity in PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear ‘mixed’ selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors....
Article
Physical features of sensory stimuli are fixed, but sensory perception is context dependent. The precise mechanisms that govern contextual modulation remain unknown. Here, we trained mice to switch between two contexts: passively listening to pure tones and performing a recognition task for the same stimuli. Two-photon imaging showed that many exci...

Citations

... Moreover, several good practices and step-by-step modeling guides have been published to ensure the reproducibility of models (Blohm et al. 2020;Kording et al. 2018;Novére et al. 2005). Lastly, initiatives such as the Neuromatch Academy courses and conferences van Viegen et al. 2021) provide an unprecedented opportunity to build an accessible, democratic, inclusive, international and interdisciplinary community aiming at using computational approaches to improve our understanding of brain function. ...
... In addition, experimental evidence supporting the presence of inhibition of return both at the behavioral and neural levels remains controversial (19,20). On the other hand, neural circuit models for explaining the neural effects of top-down attention often treat them as static inhibitory (21) or excitatory inputs (22,23) to local circuits; doing so, thus, completely ignores the dynamical fluctuations of attention. Therefore, despite widespread investigations, the fundamental questions of the neural circuit mechanism underlying attention fluctuations and their functional role remain unclear. ...
... However, they are challenging to implement in object recognition of mutually exclusive targets. From a computer science perspective, future work on recurrent architectures (Mnih et al., 2014) and soft spatial attention (Lindsay, 2020), reweighting solely the representations and not directly entire image parts, may be able to further push the boundaries of such a data-driven approach to design more human-like models without the reported disadvantages. We hope that our novel approach excites both fields and sparks innovative ideas for future computational models of vision. ...
... Usually, the shallower convolutional layers at the front end of CNN can learn local features, such as image texture with a smaller receptive field. e deep convolutional layer at the back end uses a larger receptive field to learn abstract features, such as the size and orientation of objects in the image [17]. e convolution operation is shown in ...
... Second, the current teaching model in many medical schools is relatively traditional, with the teacher at the core forcibly instilling knowledge and students being forced to accept it, a situation that has yet to be fully reformed [6]. Teachers simply follow the content of the textbook and ignore the students' ability to comprehend it, resulting in a boring and rigid classroom atmosphere, which suppresses students' interest in learning, thus making students' learning effect poor [7]. ...
... Because these flow-fields shape population trajectories, and because empirical population trajectories are readily plotted and analyzed in state-space, this affords a means of comparing data with predictions. Goal-driven networks -that is, networks trained to perform a computation intended to be analogous to that performed by a biological neural population (Zipser and Andersen, 1988;Yamins et al., 2014;Lindsay and Miller, 2018) -are increasingly used to model computations requiring internal or external feedback (Mante et al., 2013;Sussillo et al., 2015;Maheswaranathan et al., 2019;Sohn et al., 2019;Kao et al., 2020;Michaels et al., 2019;Rajan et al., 2016;Perich and Rajan, 2020). Although such models typically lack detailed anatomical realism, they yield solutions that can be understood through reverse engineering and afford comparisons with data. ...
... Remarkably, a simple generic model offers a clear-cut mathematical explanation of a wealth of empirical evidence related to in vivo recordings of "grandmother" cells and rapid learning at the level of individual neurons. It also sheds light on the question of why Hebbian learning may give rise to neuronal selectivity in the prefrontal cortex (Lindsay et al., 2017) and explain why adding single neurons to deep layers of ANNs is an efficient tool to acquire novel information while preserving previously trained data representations (Draelos et al., 2017). ...
... Since whisking is not the sole factor explaining the odorrelated activity in S1, we examined if it could result from cholinergic inputs to the cortex. Cholinergic inputs to the cortex from the basal forebrain have very specific effects on excitatory and inhibitory neurons [23][24][25][26] and are known to play a role in attentional modulation based on the behavioral context [27][28][29][30] . To assess whether they play a role in odor-related modulation of barrel cortex activity, we performed dual local injections of a nicotinic and a muscarinic receptor antagonist (1 mM injection of atropine and mecamylamine, Fig. 5a) in the barrel cortex shortly before running our olfactory-tactile stimulation protocol in animals with their facial nerve sectioned. ...