Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task

Area of Science:

• Neuroscience
• Neural Engineering
• Cognitive Science

Background:

• Electrical microstimulation offers precise neural circuit control for research and functional restoration.
• Understanding how neural responses to artificial stimulation change with learning is crucial but remains largely unknown.

Purpose of the Study:

• To investigate the evolution of neural responses to intracortical microstimulation (ICMS) during a learning task.
• To differentiate between direct neuronal activation and network-mediated responses during learning and their impact on behavior.

Main Methods:

• Utilized ultraflexible electrodes for stable ICMS in a detection task.
• Employed longitudinal in vivo imaging and electrophysiological recordings to track neural changes over weeks.
• Monitored behavioral changes through detection thresholds.

Main Results:

• Behavioral learning was evidenced by decreased detection thresholds.
• Chronic imaging revealed expanded stimulus-evoked neuronal recruitment at a fixed current, with a stable core of behaviorally relevant neurons.
• A subset of neurons showed enhanced modulation and reduced response latency with learning.
• Electrophysiology distinguished two adaptation forms: strengthened excitability in directly activated neurons and increased, behaviorally predictive recruitment in polysynaptic pathways.

Conclusions:

• Learning in an ICMS task induces plasticity that is dependent on the mechanism of activation (direct vs. polysynaptic).
• Cortical circuits dynamically reshape in response to ICMS and learning.
• Future stimulation paradigms should consider both cell-intrinsic and network dynamics for adaptive interventions.