Rapid Integration of Artificial Sensory Feedback during Operant Conditioning of Motor Cortex Neurons

Abstract

SummaryNeuronal motor commands, whether generating real or neuroprosthetic movements, are shaped by ongoing sensory feedback from the displacement being produced. Here we asked if cortical stimulation could provide artificial feedback during operant conditioning of cortical neurons. Simultaneous two-photon imaging and real-time optogenetic stimulation were used to train mice to activate a single neuron in motor cortex (M1), while continuous feedback of its activity level was provided by proportionally stimulating somatosensory cortex. This artificial signal was necessary to rapidly learn to increase the conditioned activity, detect correct performance, and maintain the learned behavior. Population imaging in M1 revealed that learning-related activity changes are observed in the conditioned cell only, which highlights the functional potential of individual neurons in the neocortex. Our findings demonstrate the capacity of animals to use an artificially induced cortical channel in a behaviorally relevant way and reveal the remarkable speed and specificity at which this can occur.

The sensory representation of causally controlled objects

“…Previous work indicates that subjects can learn to control neuroprosthetic devices using single cells or bulk electrophysiological signals (Fetz, 1969;Bakay and Kennedy, 1998;Nicolelis, 2001;Serruya et al, 2002;Carmena et al, 2003;Weiskopf et al, 2003;Sitaram et al, 2007;Koralek et al, 2012;Hochberg et al, 2012;Collinger et al, 2013;Clancy et al, 2014;Sadtler et al, 2014;Prsa et al, 2017;Sitaram et al, 2017;Trautmann et al, 2019), but this is the first work, to our knowledge, to employ control using imaged population calcium signals. This technique allowed us to monitor much of the dorsal cortical network as animals learned neuroprosthetic control, whereas previous BMI work has been limited to recording from neighbouring neurons (Koralek et al, 2012;Clancy et al, 2014;Sadtler et al, 2014;Prsa et al, 2017). Using population signals, rather than individual neurons, to manipulate neuroprosthetic devices might afford more stable and minimally invasive control, robust to losing signals from single control cells.…”

Parsing learning in networks using brain–machine interfaces

“…A recent study by Prsa and colleagues showed that feedback via optogenetic neural stimulation is sufficient to drive BMI learning [25]**. This demonstrates a BMI where sensory feedback is delivered to specific, experimenter-defined nodes within the brain, paired with experimenter- defined command nodes (Fig.…”

“…Activity of corticomotor-neuronal cells can be divorced from muscle activity with feedback training [21] and subjects can learn decoders with arbitrary relationships between neural activity and movement [15]. BMI learning is also associated with differential modulation of command nodes relative to nearby nodes [14,18,22–25] and cortico-striatal interactions specific to command nodes [26]. While the precise neural mechanisms of neurofeedback learning are not fully understood (e.g.…”

“…Knowledge about the decoder can be used to interpret learning-related changes in command nodes [14,17,30–33,35,39]. Moreover, by defining the command nodes, BMI studies can explore the specificity of learning changes within command areas (direct versus indirect nodes) [22–25,30,40]. Functional connectivity measures like spike-field coherence [41] between command nodes and other parts of the brain can allow identification of functional networks participating in learning (e.g.…”

“…These results suggest that BMI may involve multiple control strategies—both predictive feed-forward control and feedback-based control. (b) Prsa et al [25]** developed a BMI where decoder output drove optogenetic stimulation (channelrhodopsin, ChR2). This creates a system where both the command and feedback nodes can be precisely defined (left).…”