Abstract Objective. Brain-machine interfaces (BMIs) aim to restore sensorimotor function to
individuals suffering from neural injury and disease. A critical step in implementing
a BMI is to decode movement intention from recorded neural activity patterns in
sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging,
is an attractive approach for recording large-scale neural activity with high spatial
resolution using a minimally-invasive technique. However, relating slow twophoton
calcium imaging data to fast behaviors is challenging due to the relatively low
optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium
imaging has been used to decode information about stereotyped single-limb
movements and to control BMIs. Here, we expand upon prior work by applying deep
learning to decode multi-limb movements of running mice from 2p calcium imaging
data.
Approach. We developed a recurrent encoder-decoder network (LSTM-encdec) in
which the output is longer than the input.
Main results. LSTM-encdec 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.
Significance. Our approach provides interpretability measures to validate
decoding accuracy and expands the utility of BMIs by establishing the groundwork for
control of multiple limbs. Our work contributes to the advancement of neural decoding
techniques and the development of next-generation optical BMIs.