Biology subjects

Dadarlat, M.

Publications and source records attributed to Dadarlat, M..

3 recordsLinked to original sources

bioRxiv2025

Rapid learning and integration of artificial sensation

1Humans rely on both proprioceptive and visual feedback during reaching, integrating these two sensory streams to improve movement accuracy and precision [1, 2]. Patients using a Brain-Machine interface (BMI) will similarly require artificial proprioceptive feedback in addition to vision to finely control a prosthesis [3, 4]. Intracortical microstimulation (ICMS) elicits sensory perceptions that could replace the lost proprioceptive signal. However, some learning may be required for encoding artificial sensation [5], as current technology does not give access to neurons with all of the desired encoding properties [6]. We developed a freely-moving mouse behavioral task in which to test learning and integration of artificial sensory information. Five mice were implanted with a 16-channel microwire array in primary somatosensory cortex. Mice were trained to navigate to randomly-selected targets upon the floor of a custom behavioral training cage. Target location was encoded with visual and/or patterned multi-channel ICMS feedback. Mice received multi-modal feedback from the beginning of training of the behavioral task, achieving 75% on multimodal trials after approximately 1000 training trials. Mice also quickly learned to use the ICMS signal to locate invisible targets, achieving 75% proficiency on ICMS-only trials when tested. Critically, we found that performance on multimodal trials significantly exceeded unimodal performance (vision or ICMS), demonstrating that animals rapidly learned to integrate natural vision with artificial sensation. 2 SignificanceMultisensory integration of visual and proprioceptive information facilitates accurate and precise movements. Intracortical microstimulation (ICMS) elicits perceptions that could supplement visual information for patients controlling a prostheses. Here, we developed a freely-moving mouse behavioral task to examine how ICMS can be used to encode multi-variable task-relevant information. Mice implanted with a cortical microwire array were trained to interpret patterned multi-channel ICMS to navigate to targets upon the floor of a custom behavioral training cage. Mice quickly learned to use the ICMS signal to locate invisible targets and integrated the artificial signal with natural vision, improving task performance. This protocol can be applied to efficiently develop and test algorithms to encode artificial proprioception for neural prostheses.

neuroscience↗

bioRxiv2025

Population-level encoding of somatosensation in mouse sensorimotor cortex

Somatosensation constructs the bodys dynamic sense of state and allows for dexterous and precise movements. The heterogeneous responses of single neurons in sensorimotor cortex to so-matosensation have led to disparate views of the computational role of this brain area in sensori-motor processing. Here, we use population-level analyses of neural activity recorded during passive limb movements to assess the structure and to summarize the properties of neural encoding in sensorimotor cortex. We used 2-photon imaging to record the activity of thousands of neurons in eight anesthetized mice during passive deflections of each limb. We additionally analyzed neu-ral responses to passive limb movements in eight awake mice, sourced from an open dataset [1]. We employed principal component analysis on the neural activity in each dataset and found that a small fraction of principal components explained a large fraction of variance in the neural re-sponses. Low-dimensional representations of limb movements were well conserved across animals, including the orthogonal representations of ipsilateral and contralateral limbs. This organization of somatosensory information mirrors the well-known structure of neural encoding of motor com-mands in sensorimotor cortex. Furthermore, neural populations dually encoded both changes in joint angles during movements and more abstract information, i.e., the direction of limb movement. Increasing the size of the neural population improved encoding of both types of movement infor-mation and better differentiated representations from movements in opposing directions. Together, these results demonstrate that population-level encoding of somatosensory information in mouse sensorimotor cortex is structured to facilitate sensorimotor integration across the brain.

neuroscience↗

bioRxiv2023

Decoding multi-limb movements from low temporal resolution calcium imaging using deep learning

Two-photon imaging has been a critical tool for dissecting brain circuits and understanding brain function. However, relating slow two-photon calcium imaging data to fast behaviors has been challenging due to relatively low imaging sampling rates, thus limiting potential applications to neural prostheses. Here, we show that a recurrent encoder-decoder network with an output length longer than the input length can accurately decode limb trajectories of a running mouse from two-photon calcium imaging data. The encoder-decoder model could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere. Furthermore, neurons that were important for decoding were found to be well-tuned to both ipsilateral and contralateral limb movements, showing that artificial neural networks can be used to understand the function of the brain by identifying sub-networks of neurons that correlate with behaviors of interest.

neuroscience↗