An official website of the United States government
A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient’s own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that decoders trained in one context did not generalize well to other contexts, leading to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either the decoder training task context or the hand’s physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts. This shift in neural activity possibly explains biases to off-context kinematic predictions and suggests a feature that could help predict different magnitude muscle activations while producing similar kinematics.
more »
« less
Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity
Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.
more »
« less
- Award ID(s):
- 1707398
- PAR ID:
- 10338029
- Date Published:
- Journal Name:
- eLife
- Volume:
- 11
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)The neural code relates the activity of the nervous system to the activity of the muscles to the generation of behavior. To decipher it, it would be ideal to comprehensively measure the activity of the entire nervous system and musculature in a behaving animal. As a step in this direction, we used the cnidarian Hydra vulgaris to explore how physiological and environmental conditions alter simple contractile behavior and its accompanying neural and muscle activity. We used whole-body calcium imaging of neurons and muscle cells and studied the effect of temperature, media osmolarity, nutritional state, and body size on contractile behavior. In mounted Hydra preparations, changes in temperature, nutrition state, or body size did not have a major effect on neural or muscle activity, or on contractile behavior. But changes in media osmolarity systematically altered contractile behavior and foot detachments, increasing their frequency in hypo-osmolar media solutions and decreasing it in hyperosmolar media. Similar effects were seen in ectodermal, but not in endodermal muscle. Osmolarity also bidirectionally changed the activity of contraction burst (CB) neurons, but did not affect the network of rhythmic potential (RP) neurons in the ectoderm. These findings show osmolarity-dependent changes in the activity of CB neurons and ectodermal muscle, consistent with the hypothesis that CB neurons respond to media hypo-osmolarity, activating ectodermal muscle to generate CBs. This dedicated reflex could serve as an excretory system to prevent osmotic injury. This work demonstrates the feasibility of studying an entire neuronal and muscle activity in a behaving animal.more » « less
-
Gutkin, Boris S. (Ed.)Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.more » « less
-
Natural language generators for task-oriented dialog should be able to vary the style of the output utterance while still effectively realizing the system dialog actions and their associated semantics. While the use of neural generation for training the response generation component of conversational agents promises to simplify the process of producing high quality responses in new domains, to our knowledge, there has been very little investigation of neural generators for task-oriented dialog that can vary their response style and we know of no experiments on models that can generate responses that are different in style from those seen during training, while still maintaining semantic fidelity to the input meaning representation. Here, we show that a model that is trained to achieve a single stylistic personality target can produce outputs that combine stylistic targets. We carefully evaluate the multivoice outputs for both semantic fidelity and for similarities to and differences from the linguistic features that characterize the original training style. We show that contrary to our predictions, the learned models do not always simply interpolate model parameters, but rather produce styles that are distinct and novel from the personalities they were trained on.more » « less
-
Mechanosensory feedback of the internal reproductive state drives decisions about when and where to reproduce. For instance, stretch in the Drosophila reproductive tract produced by artificial distention or from accumulated eggs regulates the attraction to acetic acid to ensure optimal oviposition. How such mechanosensory feedback modulates neural circuits to coordinate reproductive behaviors is incompletely understood. We previously identified a stretch-dependent homeostat that regulates egg laying in Caenorhabditis elegans. Sterilized animals lacking eggs show reduced Ca2+ transient activity in the presynaptic HSN command motoneurons that drive egg-laying behavior, while animals forced to accumulate extra eggs show dramatically increased circuit activity that restores egg laying. Interestingly, genetic ablation or electrical silencing of the HSNs delays, but does not abolish, the onset of egg laying, with animals recovering vulval muscle Ca2+ transient activity upon egg accumulation. Using an acute gonad microinjection technique to mimic changes in pressure and stretch resulting from germline activity and egg accumulation, we find that injection rapidly stimulates Ca2+ activity in both neurons and muscles of the egg-laying circuit. Injection-induced vulval muscle Ca2+ activity requires L-type Ca2+ channels but is independent of presynaptic input. Conversely, injection-induced neural activity is disrupted in mutants lacking the vulval muscles, suggesting "bottom-up" feedback from muscles to neurons. Direct mechanical prodding activates the vulval muscles, suggesting that they are the proximal targets of the stretch-dependent stimulus. Our results show that egg-laying behavior in C. elegans is regulated by a stretch-dependent homeostat that scales postsynaptic muscle responses with egg accumulation in the uterus.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.67620
