A modular neural network model of grasp movement generation

Michaels, Jonathan A.; Schaffelhofer, Stefan; Agudelo-Toro, Andres; Scherberger, Hansjörg

doi:10.1101/742189

Cited by 20 publications

(28 citation statements)

References 73 publications

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“…To determine if the observed low-dimensional trajectories are sufficient to perform the tasks, we trained artificial RNNs to mimic the behavior of monkeys performing the four tasks analyzed in this paper. The inputs to the RNN are time-varying signals representing sensory stimuli, and we adjusted the parameters of the RNN so its time-varying outputs are the desired behavioral responses (8,(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37). In these artificial RNNs, we have complete information about the network connectivity and moment-by-moment firing patterns and know, by design, that these are the only computational mechanisms being used to solve the tasks.…”

Section: Our Decisions Often Depend On Multiple Sensory Experiences Smentioning

confidence: 99%

Low-dimensional dynamics for working memory and time encoding

Cueva

Saez

Marcos

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

110

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Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

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Section: Our Decisions Often Depend On Multiple Sensory Experiences Smentioning

confidence: 99%

Low-dimensional dynamics for working memory and time encoding

Cueva

Saez

Marcos

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

110

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show abstract

“…Sussillo et al showed that regularized RNNs that are trained to reproduce EMG signals learn transient signals that are reminiscent of motor cortical activity (50). Michaels et al combined CNNs and RNNs to build a model for visually guided reaching (51). Lilicrap and Scott assumed that neural activity in motor cortex is optimized for controlling the biomechanics of the limb and they could explain the non-uniform distribution of tuning directions in motor neurons (52).…”

Section: Informing Biological Studiesmentioning

confidence: 99%

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Sandbrink

Mamidanna

Michaelis³

et al. 2020

Preprint

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Biological motor control is versatile and efficient. Muscles are flexible and undergo continuous changes requiring distributed adaptive control mechanisms. How proprioception solves this problem in the brain is unknown. Here we pursue a task-driven modeling approach that has provided important insights into other sensory systems. However, unlike for vision and audition where large annotated datasets of raw images or sound are readily available, data of relevant proprioceptive stimuli are not. We generated a large-scale dataset of human arm trajectories as the hand is tracing the alphabet in 3D space, then using a musculoskeletal model derived the spindle firing rates during these movements. We propose an action recognition task that allows training of hierarchical models to classify the character identity from the spindle firing patterns. Artificial neural networks could robustly solve this task, and the networks' units show directional movement tuning akin to neurons in the primate somatosensory cortex. The same architectures with random weights also show similar kinematic feature tuning but do not reproduce the diversity of preferred directional tuning nor do they have invariant tuning across 3D space. Taken together our model is the first to link tuning properties in the proprioceptive system to the behavioral level. Proprioception | Goal-driven modeling | Handwritten character recognition | Deep neural networks | Musculoskeletal models | Somatosensory cortex | S1 | Cuneate NucleusHighlights:• We provide a normative approach to derive neural tuning of proprioceptive features from behaviorallydefined objectives.

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“…To investigate which factors influence learning within and outside of the neural manifold, we used a recurrent neural network trained with a machine learning algorithm. Although this method of modelling neural dynamics lacks biological details, using RNNs has been surprisingly useful to understand neural phenomena [Sussillo et al, 2015, Barak, 2017, Mastrogiuseppe and Ostojic, 2018, Michaels et al, 2019, Masse et al, 2019. Using this approach, we could identify feedback learning as a potential bottleneck differentiating between learning within versus outside the original manifold.…”

Section: Discussionmentioning

confidence: 99%

Neural manifold under plasticity in a goal driven learning behaviour

Feulner

Clopath

2020

Preprint

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Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within-and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.

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A modular neural network model of grasp movement generation

Cited by 20 publications

References 73 publications

Low-dimensional dynamics for working memory and time encoding

Low-dimensional dynamics for working memory and time encoding

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Neural manifold under plasticity in a goal driven learning behaviour

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