Deep learning-based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWRs), one of the most synchronous events of the brain. SWRs reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. While spectral analysis have permitted advances, the surge of ultra-dense recordings now call for new automatic detection strategies. Here, we show how one-dimensional convolutional networks operating over highdensity LFP hippocampal recordings allowed for automatic identification of SWR from the rodent hippocampus. When applied without retraining to new datasets and ultra-dense hippocampus-wide recordings, we discovered physiologically relevant processes associated to the emergence of SWR, prompting for novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied in feature-based specialization, which permit identification of spatially segregated oscillations and deflections, as well as synchronous population firing typical of replay. Thus, using deep learning-based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.This work is supported by grants from Fundación La Caixa (LCF/PR/HR21/52410030; DeepCode). Access to the Artemisa high-performance computing infrastructure (NeuroConvo project) is supported by Universidad de Valencia and co-funded by the European Union through the 2014–2020 FEDER Operative Programme (IDIFEDER/2018/048). ANO and RA are supported by PhD fellowships from the Spanish Ministry of Education (FPU17/03268) and Universidad Autónoma de Madrid (FPI-UAM-2017), respectively. We thank Elena Cid for help with histological confirmation of the probe tracks and Pablo Varona for feedback and discussion. We also thank Aarón Cuevas for clarifications and support while developing the Open Ephys Plugin for online detection

Local or Not Local: Investigating the Nature of Striatal Theta Oscillations in Behaving Rats

International audienceVisual Abstract In the cortex and hippocampus, neuronal oscillations of different frequencies can be observed in local field potentials (LFPs). LFPs oscillations in the theta band (6-10 Hz) have also been observed in the dorsolateral striatum (DLS) of rodents, mostly during locomotion, and have been proposed to mediate behaviorally-relevant interactions between striatum and cortex (or between striatum and hippocampus). However, it is unclear if these theta oscillations are generated in the striatum. To address this issue, we recorded LFPs and spiking activity in the DLS of rats performing a running sequence on a motorized treadmill. We observed an increase in rhythmical activity of the LFP in the theta-band during run compared to rest periods. However, several observations suggest that these oscillations are mainly generated outside of the striatum. First, theta oscillations disappeared when Significance Statement In the cortex and hippocampus, neuronal network oscillations can be observed in the local field potentials (LFPs) and contribute to information transfer between brain regions. LFP oscillations can also be recorded in the striatum, even if, unlike the cortex and hippocampus, this brain region's anatomic organization does not favor the generation of dipolar sources. It is therefore unclear if these striatal oscillations are locally generated or reflect volume-conducted signals generated distally from the striatum. Here, we provide evidence that striatal theta oscillations of the LFPs recorded while rats performed a running sequence are largely contaminated by volume-conducted signals. We propose that theta LFP oscillations in the striatum do not accurately reflect local cellular activity and should be interpreted with caution

The Dorsal Striatum Energizes Motor Routines

International audienceThe dorsal striatum (dS) has been implicated in storing procedural memories and controlling movement kinematics. Since procedural memories are expressed through movements, the exact nature of the dS function has proven difficult to delineate. Here we challenged rats in complementary locomotion-based tasks designed to alleviate this confound. Surprisingly, dS lesions did not impair the rats' ability to remember the procedure for the successful completion of motor routines. However, the speed and initiation of the rewardoriented phase of the routines were irreversibly altered by the dS lesion. Further behavioral analyses combined with modeling in the optimal control framework indicated that these kinematic alterations were well-explained by an increased sensitivity to effort. Our work provides evidence supporting a primary role of the dS in modulating the kinematics of reward-oriented actions, a function that may be related to the optimization of the energetic costs of moving

Presynaptic GABAB Receptors Regulate Hippocampal Synapses during Associative Learning in Behaving Mice

GABAB receptors are the G-protein-coupled receptors for GABA, the main inhibitory neurotransmitter in the central nervous system. Pharmacological activation of GABAB receptors regulates neurotransmission and neuronal excitability at pre- and postsynaptic sites. Electrophysiological activation of GABAB receptors in brain slices generally requires strong stimulus intensities. This raises the question as to whether behavioral stimuli are strong enough to activate GABAB receptors. Here we show that GABAB1a-/- mice, which constitutively lack presynaptic GABAB receptors at glutamatergic synapses, are impaired in their ability to acquire an operant learning task. In vivo recordings during the operant conditioning reveal a deficit in learning-dependent increases in synaptic strength at CA3-CA1 synapses. Moreover, GABAB1a-/- mice fail to synchronize neuronal activity in the CA1 area during the acquisition process. Our results support that activation of presynaptic hippocampal GABAB receptors is important for acquisition of a learning task and for learning-associated synaptic changes and network dynamics

Turning the body into a clock: Accurate timing is facilitated by simple stereotyped interactions with the environment

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