Abstract
Functional coupling networks are widely used to characterize collective patterns of activity in neural populations. Here, we ask whether functional couplings reflect the subtle changes, such as in physiological interactions, believed to take place during learning. We infer functional network models reproducing the spiking activity of simultaneously recorded neurons in prefrontal cortex (PFC) of rats, during the performance of a cross-modal rule shift task (task epoch), and during preceding and following sleep epochs. A large-scale study of the 96 recorded sessions allows us to detect, in about 20% of sessions, effective plasticity between the sleep epochs. These coupling modifications are correlated with the coupling values in the task epoch, and are supported by a small subset of the recorded neurons, which we identify by means of an automatized procedure. These potentiated groups increase their coativation frequency in the spiking data between the two sleep epochs, and, hence, participate to putative experience-related cell assemblies. Study of the reactivation dynamics of the potentiated groups suggests a possible connection with behavioral learning. Reactivation is largely driven by hippocampal ripple events when the rule is not yet learned, and may be much more autonomous, and presumably sustained by the potentiated PFC network, when learning is consolidated.
Highlights
Functional couplings: Effective interactions between recorded units explaining the observed pattern of correlations in the recorded population neural activity
Each one of the 96 recording sessions is divided into three 30-minute epochs: a Task epoch in which the rat had to learn a cross-modal rule, which was changed as soon as the rat had learned it, and two Sleep epochs, one before (Sleep Pre) and one after (Sleep Post) the Task epoch
Through spike sorting one can identify the same neurons recorded in the different epochs (Sleep Pre, Task, Sleep Post) of a session; the number N of neurons reliably mapped in all three epochs varies from 3 to 56 depending on the session
Summary
Five Long-Evans male rats weighing 250–300 g at arrival were implanted with tetrodes in the prelimbic (PL) subdivision of the medial prefrontal cortex, and in the intermediate-ventral hippocampus. PL tetrodes were used for recording of single units: signals were band-pass filtered between 600 and 6,000 Hz, and spikes were detected whenever the filtered signal exceeded a manually set threshold. The resulting waveform (1.3 ms long) was fed into an automated spike sorting algorithm (KlustaKwik; Kadir, Goodman, & Harris, 2014). Hippocampal tetrodes were only used for local field potentials, for the detection of theta rhythms and sharp waves. Non-REM was automatically detected, based on power in the cortical delta band (1–4 Hz), hippocampal theta (5–10 Hz), cortical spindles (10–20 Hz), and speed of head motion, by means of a clustering algorithm. A quality check on sleep epochs ensures the absence of systematic biases in Sleep Pre with respect to Sleep Post; see Figure S1 (Tavoni et al, 2017)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
