Biology subjects

Jurado-Parras, M.-T.

Publications and source records attributed to Jurado-Parras, M.-T..

4 recordsLinked to original sources

bioRxiv2026

Low-latency neuromorphic closed-loop control of hippocampal ripples in vivo

Real-time closed-loop neuromodulation, in which stimulation is precisely timed to ongoing brain dynamics, holds transformative potential for treating neurological disorders and probing neural circuit function. However, it requires low-latency, energy-efficient processing of high-bandwidth neural signals that conventional computing architectures struggle to deliver. Neuromorphic computing, which emulates the event-driven and massively parallel operation of biological neural circuits, offers a compelling alternative. Yet, its integration into closed-loop frameworks validated in vivo for fast, transient oscillations has not been demonstrated. Here, we present a fully integrated neuromorphic framework for real-time detection and manipulation of hippocampal ripples: brief (30-100 ms), high-frequency (100-250 Hz) oscillations that are critical for memory consolidation and implicated in neurological disorders. We train compact spiking neural networks comprising 41 neurons and 530 parameters using surrogate-gradient backpropagation, achieving detection performance competitive with deep learning models across 23 recording sessions while consuming up to 200-fold less energy when deployed on SpiNNaker neuromorphic hardware. Integration with the open-source Open Ephys platform yields total closed-loop latencies of approximately 50 ms, enabling intra-event stimulation in up to 80% of ripples. Validating the complete sensing-processing-stimulation pipeline in awake, head-fixed mice, we demonstrate that neuromorphic-triggered optogenetic inhibition significantly alters ripple dynamics and reduces oscillatory energy. This work establishes a practical and accessible neuromorphic framework for low-latency closed-loop control of fast brain dynamics in vivo.

neuroscience↗

bioRxiv2022

Deep learning based feature extraction for prediction and interpretation of sharp- wave ripples

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWR), one of the most synchronous events of the brain. SWR reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. Current spectral methods fail to capture their mechanistic complexity, thus limiting progress. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for automatic identification of SWR. When applied to 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. Thus, using deep learning based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.

neuroscience↗

bioRxiv2020

The dorsal striatum sets the sensitivity to effort

The dorsal striatum (dS) has been implicated in storing and retrieving procedural memories and controlling movement kinematics (e.g., speed). 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 tasks designed to alleviate this performance confound. Surprisingly, dS lesions spared task-specific procedural memories but altered the kinematics of their expression in motor routines. Further behavioral analyses combined with simulations in the optimal control framework indicated that these alterations reflected an increased sensitivity to effort with preserved reward-seeking and ability to modulate movement speed. By setting the sensitivity to effort, the dS contributes to the optimization of the energy invested into voluntary movements. Such an elementary function of the dS might explain its implication in both procedural decisions and the control of movement speed.

neuroscience↗

bioRxiv2019

The Embodied Nature of Well-Timed Behavior

How animals adapt their movements to take advantage of behaviorally-relevant time intervals is not well understood, especially in the supra-second timescale. It has been proposed that motor timing depends on the emergence of self-sustained dynamics across ensembles of neurons. Alternatively, evidence from operant conditioning suggests that animals can develop motor routines to adapt their behavior to fixed temporal constraints. But it is unclear whether animals can accurately time their behavior without the help of motor routines. To address this issue, 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, a unique motor routine whose execution resulted in the precise timing of their reward approaches. Noticeably, when proficient animals occasionally failed to follow this routine, the timing of their reward approaches was systematically poor. In a second step, we trained naive animals in modified versions of the task specifically designed to prevent the development of this motor strategy. Compared to rats trained in the first protocol, these animals never reached a comparable level of timing accuracy. We conclude that motor timing critically depends on the ability of animals to develop motor routines adapted to the structure of their environment. Our work also suggests that self-sustained neuronal activity alone may not be sufficient to support motor timing, at least in the supra-second timescale.

animal behavior and cognition↗