Motor primitives in space and time via targeted gain modulation in cortical networks

Stroud, Jake P.; Hennequin, Guillaume; Porter, Mason A.; Vogels, Tim P.

doi:10.1101/451054

Cited by 36 publications

(54 citation statements)

References 54 publications

(138 reference statements)

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“…Similar approaches have been used in retinal modeling (Maheswaranathan et al, 2018) , and recent studies incorporating recurrence into CNNs Kietzmann et al, 2019; . In parallel, advances have been made in understanding motor cortex by modeling it as a dynamical system (Churchland et al, 2012;Shenoy et al, 2013) implemented as a recurrent neural network (RNN) (Hennequin et al, 2014;Michaels et al, 2016;Stroud et al, 2018;Sussillo et al, 2015) . In these models, and likely in motor cortex (Churchland et al, 2012) , preparatory activity sets initial conditions that unfold predictably to control muscles during reaching.…”

Section: Introductionmentioning

confidence: 99%

A modular neural network model of grasp movement generation

Michaels

Schaffelhofer

Agudelo-Toro

et al. 2019

Preprint

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One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and using visual features to generate the required muscle dynamics to grasp objects would explain the neural and computational basis of the grasping circuit. Modular networks with object feature input and sparse inter-module connectivity outperformed other models at explaining neural data and the inter-area relationships present in the biological circuit, despite the absence of neural data during network training. Network dynamics were governed by simple rules, and targeted lesioning of modules produced deficits similar to those observed in lesion studies, providing a potential explanation for how grasping movements are generated.

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Section: Introductionmentioning

confidence: 99%

A modular neural network model of grasp movement generation

Michaels

Schaffelhofer

Agudelo-Toro

et al. 2019

Preprint

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“…In other words, if one can find a (potentially time-varying) coordinate system in which the network is contracting—in the sense that its Jacobian in the new coordinates is negative definite-this implies contraction for all coordinate systems. This makes contraction analysis useful for analyzing systems where exponential convergence of trajectories is preceded by transient divergence (Figure 1) as in recent models of motor cortex 23,24 . In this case, it is usually possible to find a coordinate system in which the convergence of trajectories is ‘pure’.…”

Section: Resultsmentioning

confidence: 97%

Achieving stable dynamics in neural circuits

Kozachkov

Lundqvist

Slotine

et al. 2020

Preprint

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10The brain consists of many interconnected networks with time-varying, partially 11 autonomous activity. There are multiple sources of noise and variation yet activity has to 12 eventually converge to a stable, reproducible state (or sequence of states) for its 13 computations to make sense. We approached this problem from a control-theory 14 perspective by applying contraction analysis to recurrent neural networks. This allowed 15 us to find mechanisms for achieving stability in multiple connected networks with 16 biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These 17 mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, 18 synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable 19 computations might be achieved despite biological complexity.20 21 2 25often vary between identical trials 1,2 . This can be due to a variety of factors including 26 variability in membrane potentials, inputs, plastic changes due to recent experience and 27 so on. Yet, in spite of these fluctuations, brain networks must achieve computational 28 stability: despite being "knocked around" by plasticity and noise, the behavioral output of 29 the brain on two experimentally identical trials needs to be similar. How is this stability 30 achieved? 31Stability has played a central role in computational neuroscience since the 1980's, 32 with the advent of models of associative memory that stored neural activation patterns as 33 stable point attractors 3-7 , although researchers were thinking about the brain's stability 34 since as early as the 1950's 8 . The vast majority of this work is concerned with the stability 35 of activity around points, lines, or planes in neural state space 9,10 . However, recent 36 neurophysiological studies have revealed that in many cases, single-trial neural activity 37 is highly dynamic, and therefore potentially inconsistent with a static attractor viewpoint 38 1,11 . Consequently, there has been a number of recent studies-both computational and 39 experimental-which focus more broadly on the stability of neural trajectories 12,13 . 40 While these studies provide important empirical results and intuitions, they do not 41 offer analytical insight into mechanisms for achieving stable trajectories in recurrent 42 neural networks. Nor do they offer insights into achieving such stability in plastic (or multi-43 modal) networks. Here we focus on finding conditions that guarantee stable trajectories 44 in recurrent neural networks and thus shed light onto how stable trajectories might be 45 achieved in vivo. 48 the population activity of a contracting network will converge towards the same trajectory, 49 thus achieving stable dynamics (Figure 1). One way to understand contraction is to 50 represent the state of a network at a given time as a point in the network's 'state-space', 51 for instance the space spanned by the possible firing rates of all the networks' neurons. 52 This state-space has the same num...

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“…The literature on recurrent neural networks includes, of course, various complementary approaches that have each achieved some of the same goals (83)(84)(85)(86)(87)(88)(89)(90)(91)(92)(93)(94)(95). But none of the previously published models offer the same breadth of application of ORGaNICs.…”

Section: Discussionmentioning

confidence: 99%

ORGaNICs: A Canonical Neural Circuit Computation

Heeger

Mackey

2018

Preprint

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A theory of cortical function is proposed, based on a family of recurrent neural circuits, called OR-GaNICs (Oscillatory Recurrent GAted Neural Integrator Circuits). Here, the theory is applied to working memory and motor control. Working memory is a cognitive process for temporarily maintaining and manipulating information. Most empirical neuroscience research on working memory has measured sustained activity during delayed-response tasks, and most models of working memory are designed to explain sustained activity. But this focus on sustained activity (i.e., maintenance) ignores manipulation, and there are a variety of experimental results that are difficult to reconcile with sustained activity. OR-GaNICs can be used to explain the complex dynamics of activity, and ORGaNICs can be use to manipulate (as well as maintain) information during a working memory task. The theory provides a means for reading out information from the dynamically varying responses at any point in time, in spite of the complex dynamics. When applied to motor systems, ORGaNICs can be used to convert spatial patterns of premotor activity to temporal profiles of motor control activity: different spatial patterns of premotor activity evoke different motor control dynamics. ORGaNICs offer a novel conceptual framework; Rethinking cortical computation in these terms should have widespread implications, motivating a variety of experiments.! 1 December 25, 2018 ing active only transiently (20-25), or they exhibit complex dynamics during the delay periods (25-31), not just constant, sustained activity. Second, complex dynamics (including oscillations) are evident also in the synchronous activity (e.g., as measured with local field potentials) of populations of neurons (32, 33). Third, some of the same neurons exhibit activity that is dependent on task demands (34-37). Fourth, some of these neurons appear to contribute to different cognitive processes (controlling attention, decision making, , motor control), in addition to working memory, either for different tasks or during different phases of task execution over time (38)(39)(40)(41)(42). ORGaNICsLong Short Term Memory units (LSTMs) are machine learning (ML) / artificial intelligence (AI) algorithms that are capable of representing and manipulating long-term dependencies (43), in a manner that is analogous to the concept of working memory in psychology. LSTMs are a class of recurrent neural networks (RNNs). A number of variants of the basic LSTM architecture have been developed and tested for a variety of AI applications including language modeling, neural machine translation, and speech recognition (44)(45)(46)(47)(48)(49)(50)(51)(52). (See also: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ and http://karpathy.github.io/2015/05/21/rnn-effectiveness/.) In these and other tasks, the input stimuli contain information across multiple timescales, but the ongoing presentation of stimuli makes it difficult to correctly combine that information over time (53,54). An LSTM handles this pr...

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Motor primitives in space and time via targeted gain modulation in cortical networks

Cited by 36 publications

References 54 publications

A modular neural network model of grasp movement generation

A modular neural network model of grasp movement generation

Achieving stable dynamics in neural circuits

ORGaNICs: A Canonical Neural Circuit Computation

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