Harnessing behavioral diversity to understand neural computations for cognition

Musall, Simon; Urai, Anne E; Sussillo, David; Churchland, Anne K.

doi:10.1016/j.conb.2019.09.011

Cited by 50 publications

(41 citation statements)

References 113 publications

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“…Early studies of the link between the brain and behavior focused on simple sensory and motor functions and documented how single neurons encode variables such as visual orientation [ 1 ] and movement endpoint [ 2 ]. More recently, the field has begun to move toward richer tasks in which the stimulus-response relationships are more complex [ 3 ] and can vary with unobservable (“latent”) internal variables. For instance, in decision-making tasks, reaction times depend on a latent decision threshold that is adjusted internally based on prior expectations and costs [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Engineering recurrent neural networks from task-relevant manifolds and dynamics

Pollock

Jazayeri

2020

PLoS Comput Biol

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Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons.

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Section: Introductionmentioning

confidence: 99%

Engineering recurrent neural networks from task-relevant manifolds and dynamics

Pollock

Jazayeri

2020

PLoS Comput Biol

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“…Previous work in the motor system and in prefrontal cortex has shown that insight into the computational mechanisms that underlie complex tasks can be gained by treating neural populations as a dynamical system and studying how their trajectories evolve with time ( Barak et al, 2013 , Botvinick et al, 2019 , Buonomano and Maass, 2009 , Carnevale et al, 2015 , Chaisangmongkon et al, 2017 , Gallego et al, 2017 , Gallego et al, 2018 , Hennequin et al, 2014 , Mante et al, 2013 , Musall et al, 2019 , Rabinovich et al, 2008 , Rajan et al, 2016 , Remington et al, 2018 , Richards et al, 2019 , Shenoy et al, 2013 , Sussillo and Abbott, 2009 , Sussillo and Barak, 2013 , Sussillo et al, 2015 , Sutskever, 2013 , Wang et al, 2018 , Wang et al, 2017 , Yang et al, 2018 ). In prefrontal cortex this work has helped shed light on how task execution is driven by dynamics around fixed and slow points in neural population space ( Chaisangmongkon et al, 2017 , Mante et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Learning to select actions shapes recurrent dynamics in the corticostriatal system

2020

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Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, revealing how dynamics in the corticostriatal system support task learning.

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“…Indeed, today nearly all of the neurons in the brains of the worm C. elegans and the zebrafish D. rerio can be recorded simultaneously, allowing a large fraction of the brain's neural dynamics to be observed (Cong et al, 2017;Kim et al, 2017;Nguyen et al, 2016;Symvoulidis et al, 2017;Venkatachalam et al, 2016). Given that subtle movements can have pervasive effects on neural dynamics, obtaining unbiased and holistic measurements of an animal's behavior -even in restrained animals -will be important to understanding dense neural activity (Musall et al, 2019;Stringer et al, 2019). We further propose that making sense of highdimensional neural data will ultimately be facilitated by access to behaviors whose complexity and dimensionality is closer to that of the neural space that is concurrently being surveyed.…”

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confidence: 99%

Computational Neuroethology: A Call to Action

Datta

Anderson

Branson

et al. 2019

Neuron

351

308

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Box 1. GlossaryBehavioral representation-a quantitative distillation of any aspect of the time-varying behavior exhibited by an animal in an experiment. Such representations can vary in form from classical ethograms to low-dimensional plots capturing the trajectory of an animal in space. Naturalistic-as with ''ecologically relevant'' (see below), there are many definitions for naturalistic, and indeed most experiments in behavioral neuroscience can justifiably be argued to be naturalistic at some level. Here, we take the word ''naturalistic'' to mean behaviors that are representative of actions generated during real-world tasks, like exploring new environments, obtaining food, finding shelter, and identifying mates; naturalistic behaviors as referred to herein are also largely self-motivated and expressed freely without physical restraint. This definition is meant to distinguish such behaviors from those that are imposed by researchers on animals through overtraining or those that are more constrained due to, e.g., head fixation (although, as mentioned above, there are contexts in which those types of behaviors are quite reasonably referred to as ''naturalistic''). Ecologically relevant-as with ''naturalistic'' (see above), ''ecologically relevant'' is an adjective whose meaning is in the eye of the beholder; again, this term can be appropriately applied to many kinds of behavioral experiments, including those in which animals are subject to training and restraint. Here, we take ''ecologicallyrelevant'' to mean a set of behaviors that support tasks animals have to address as part of the existential challenge of living in a particular environmental or ecological niche. Behavioral label-a behavioral label is a descriptor applied to an epoch of behavioral data. Behavioral labels can cover descriptions of behavior at many levels of granularity and run the gamut from ''a twitch of motor unit 72 in the soleus muscle'' to ''hibernating.'' Behavioral motif-a stereotyped and re-used unit of movement. The terms ''motif,'' ''moveme,'' ''module,'' ''primitive,'' and ''syllable'' have all been used interchangeably, and none of these terms is linked to a rigorous definition of the spatiotemporal scale at which a unit of behavior is organized. Similarly, action and behavior here and elsewhere are used to refer to collections of units of behavior, but again, there is no rigorous line that separates these or related terms. Perona and Anderson have argued for a taxonomy in which moveme is the simplest movement associated with a behavior, an action is a sequence of movemes, and an activity is a species-characteristic set of movemes and actions (Anderson and Perona, 2014). Behavioral sequence-an epoch in which more than one behavioral motif is expressed; sequences of motifs can be either deterministic (e.g., motif A always follows motif B) or probabilistic (e.g., motif A follows motif B fifty percent of the time). Artificial neural network-class of machine learning algorithms that operate by simulating a network of simplified neuron...

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Harnessing behavioral diversity to understand neural computations for cognition

Cited by 50 publications

References 113 publications

Engineering recurrent neural networks from task-relevant manifolds and dynamics

Engineering recurrent neural networks from task-relevant manifolds and dynamics

Learning to select actions shapes recurrent dynamics in the corticostriatal system

Computational Neuroethology: A Call to Action

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