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 high-density 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.

Artificial intelligence is finding greater use in society through its ability to process data in new ways. One particularly useful approach known as convolutional neural networks is typically used for image analysis, such as face recognition. This type of artificial intelligence could help neuroscientists analyze data produced by new technologies that record brain activity with higher resolution. Advanced processing could potentially identify events in the brain in real-time. For example, signals called sharp-wave ripples are produced by the hippocampus, a brain region involved in forming memories. Detecting and interacting with these events as they are happening would permit a better understanding of how memory works. However, these signals can vary in form, so it is necessary to detect several distinguishing features to recognize them. To achieve this, Navas-Olive, Amaducci et al. trained convolutional neural networks using signals from electrodes placed in a region of the mouse hippocampus that had already been analyzed, and 'telling' the neural networks whether they got their identifications right or wrong. Once the networks learned to identify sharp-wave ripples from this data, they could then apply this knowledge to analyze other recordings. These included datasets from another part of the mouse hippocampus, the rat brain, and ultra-dense probes that simultaneously assess different brain regions. The convolutional networks were able to recognize sharp-wave ripple events across these diverse circumstances by identifying unique characteristics in the shapes of the waves. These results will benefit neuroscientists by providing new tools to explore brain signals. For instance, this could allow them to analyze the activity of the hippocampus in real-time and potentially discover new aspects of the processes behind forming memories.

The Dorsal Striatum Energizes Motor Routines.

The 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 reward-oriented 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.

Immature Dentate Granule Cells Require Ntrk2/Trkb for the Formation of Functional Hippocampal Circuitry.

Early in brain development, impaired neuronal signaling during time-sensitive windows triggers the onset of neurodevelopmental disorders. GABA, through its depolarizing and excitatory actions, drives early developmental events including neuronal circuit formation and refinement. BDNF/TrkB signaling cooperates with GABA actions. How these developmental processes influence the formation of neural circuits and affect adult brain function is unknown. Here, we show that early deletion of Ntrk2/Trkb from immature mouse hippocampal dentate granule cells (DGCs) affects the integration and maturation of newly formed DGCs in the hippocampal circuitry and drives a premature shift from depolarizing to hyperpolarizing GABAergic actions in the target of DGCs, the CA3 principal cells of the hippocampus, by reducing the expression of the cation-chloride importer Nkcc1. These changes lead to the disruption of early synchronized neuronal activity at the network level and impaired morphological maturation of CA3 pyramidal neurons, ultimately contributing to altered adult hippocampal synaptic plasticity and cognitive processes.

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

How animals adapt their behavior according to regular time intervals between events is not well understood, especially when intervals last several seconds. One possibility is that animals use disembodied internal neuronal representations of time to decide when to initiate a given action at the end of an interval. However, animals rarely remain immobile during time intervals but tend to perform stereotyped behaviors, raising the possibility that motor routines improve timing accuracy. To test this possibility, we used a task in which rats, freely moving on a motorized treadmill, could obtain a reward if they approached it after a fixed interval. Most animals took advantage of the treadmill length and its moving direction to develop, by trial-and-error, the same motor routine whose execution resulted in the precise timing of their reward approaches. Noticeably, when proficient animals did not follow this routine, their temporal accuracy decreased. Then, naïve animals were trained in modified versions of the task designed to prevent the development of this routine. Compared to rats trained in the first protocol, these animals didn't reach a comparable level of timing accuracy. Altogether, our results indicate that timing accuracy in rats is improved when the environment affords cues that animals can incorporate into motor routines.

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

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 LFPs were rereferenced against a striatal recording electrode and the imaginary coherence between LFPs recorded at different locations within the striatum was null. Second, 8% of the recorded neurons had their spiking activity phase-locked to the theta rhythm. Third, Granger causality analyses between LFPs simultaneously recorded in the cortex and the striatum revealed that the interdependence between these two signals in the theta range was mostly accounted for by a common external source. The most parsimonious interpretation of these results is that theta oscillations observed in striatal LFPs are largely contaminated by volume-conducted signals. We propose that striatal LFPs are not optimal proxies of network dynamics in the striatum and should be interpreted with caution.

Learning capabilities and CA1-prefrontal synaptic plasticity in a mice model of accelerated senescence.

SAMP8 mice represent a suitable model of accelerated senescence as compared with SAMR1 animals presenting normal aging. Five-month-old SAMP8 mice presented reflex eyelid responses like those of SAMR1 controls, but were incapable of acquiring classically-conditioned eye blink responses in a trace (230 milliseconds [ms] of interstimulus interval) paradigm. Although SAMP8 mice presented a normal paired-pulse facilitation of the hippocampal CA1-medial prefrontal synapse, an input/output curve study revealed smaller field excitatory postsynaptic potentials (fEPSPs) in response to strong stimulations of the CA1-prefrontal pathway. Moreover, SAMP8 mice did not show any activity-dependent potentiation of the CA1-prefrontal synapse across the successive conditioning sessions shown by SAMR1 animals. In addition, SAMP8 mice presented a functional deficit during an object recognition test, continuing to explore the familiar object when controls moved to the novel one. Alert behaving SAMP8 mice presented a significant deficit in long-term potentiation (LTP) at the CA1-medial prefrontal synapse. According to the present results, SAMP8 mice present noticeable functional deficits in hippocampal and prefrontal cortical circuits directly related with the acquisition and storage of new motor and cognitive abilities.