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Background: Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. Objective: To incorporate this approach into closed-loop stimulation. Methods: Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel (8 x 8) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was initiated by the detection of a heel prick, and induced dorsiflexion. The detection of dorsiflexion triggered stimulation to induce plantarflexion, and vice versa. A single trial began with a heel prick and ended when an incorrect state transition occurred or when a second heel prick was detected. Results: Closed-loop stimulation was successfully demonstrated in 6 subjects. Number of successful trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. Conclusion: This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.