Biology subjects

Truccolo, W.

Publications and source records attributed to Truccolo, W..

5 recordsLinked to original sources

bioRxiv2024

From spiking neuronal networks to interpretable dynamics: a diffusion-approximation framework

Modeling and interpreting the complex recurrent dynamics of neuronal spiking activity is essential to understanding how networks implement behavior and cognition. Nonlinear Hawkes process models can capture a large range of spiking dynamics, but remain difficult to interpret, due to their discontinuous and stochastic nature. To address this challenge, we introduce a novel framework based on a piecewise deterministic Markov process representation of the nonlinear Hawkes process (NH-PDMP) followed by a diffusion approximation. We analytically derive stability conditions and dynamical properties of the obtained diffusion processes for single-neuron and network models. We established the accuracy of the diffusion approximation framework by comparing it with exact continuous-time simulations of the original neuronal NH-PDMP models. Our framework offers an analytical and geometric account of the neuronal dynamics repertoire captured by nonlinear Hawkes process models, both for the canonical responses of single-neurons and neuronal-network dynamics, such as winner-take-all and traveling wave phenomena. Applied to human and nonhuman primate recordings of neuronal spiking activity during speech processing and motor tasks, respectively, our approach revealed that task features can be retrieved from the dynamical landscape of the fitted models. The combination of NH-PDMP representations and diffusion approximations thus provides a novel dynamical analysis framework to reveal single-neuron and neuronal-population dynamics directly from models fitted to spiking data.

neuroscience↗

bioRxiv2023

Low-dimensional neuronal population dynamics in anterior superior temporal gyrus reactivate phonetic representations during semantic processing

AbstractSpeech processing involves a hierarchy of cognitive layers from low-level phonetic to high-level seman-tic representations. However, interactions are not only feed-forward; Feedback mechanisms are crucial for real-time, accurate speech recognition. Yet how distant levels interface during speech processing remains unclear. Here, we analyzed intracortical recordings from 624 neurons across three human participants implanted with microelectrode arrays in the anterior superior temporal gyrus during an auditory semantic categorization task and natural speech perception. We identified distinct neural subspaces, or manifolds, for semantic and phonetic features, with a functional separation of the corresponding low-dimensional dynamics. We contrasted a bottom-up cumulative and a top-down predictive hypothesis on phonetic-semantic temporal alignment, and found phonetic alignment to word-level semantic representations, signaling top-down prediction. These effects were consistent across participants at the spiking level, and remained undetected in adjacent ECoG recordings. These findings demonstrate the reorganization of neuronal population dynamics supporting phonetic representations during semantic prediction.

neuroscience↗

bioRxiv2022

Emergence of distinct neural subspaces in motor cortical dynamics during volitional adjustments of ongoing locomotion

3The brain is capable of simultaneously carrying out multiple functions, such as making different types of movements at the same time. One example is how we are able to both carry out stereotyped walking or running movements, while concurrently performing precise, target-directed movements such as kicking a ball in a soccer match. Recently, research has shown that different computations within the same population of neurons can be carried out without disrupting each other by confining the processes into separate subspaces. Whether this strategy is used to precisely control our limbs while maintaining locomotion is still an open question. Here, we recorded the activity of primary motor cortex in nonhuman primates during obstacle avoidance on a treadmill. We found that the same neural population was active during both basic unobstructed locomotion and volitional obstacle avoidance movements. Additionally, we identified the neural modes spanning the subspace of the low-dimensional dynamics in M1 using both supervised and unsupervised techniques. We found that motor cortex employs a subspace that consistently maintains the same cyclic activity throughout obstacle stepping, despite large changes in the movement itself. All the variance corresponding to the large change in movement during the obstacle avoidance is confined to its own distinct subspace. Our findings suggest that M1 utilizes different activity subspaces to coordinate the maintenance of ongoing locomotor-related neural dynamics and fast volitional gait adjustments during complex locomotion. 4 Significance StatementOur ability to modulate our ongoing walking gait with precise, voluntary adjustments is what allows us to navigate complex terrains. Locomotion and precise, goal-directed movements, such as reaching are two distinct movement modalities and have been shown to have differing requirements of motor cortical input. It is unknown how these two movements are represented in M1 low dimensional dynamics when both are carried out at the same time, such as during obstacle avoidance. We developed a novel obstacle avoidance paradigm in freely-moving non-human primates and discovered that the strategy employed by motor cortex is to confine the rhythmic locomotion-related dynamics and the voluntary, gait-adjustment movement into separate subspaces.

neuroscience↗

bioRxiv2019

Decoding Complex Sounds Using Broadband Population Recordings from Secondary Auditory Cortex of Macaques

Direct electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The results illuminated a view of the auditory cortex as a spatially distributed network and a general purpose processor of complex sounds. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation.

neuroscience↗

bioRxiv2019

Intracortical neural activity distal to seizure-onset-areas predicts human focal seizures

The apparent unpredictability of epileptic seizures has a major impact in the quality of life of people with pharmacologically resistant seizures. Here, we present initial results and a proof-of-concept of how focal seizures can be predicted early in advance based on intracortical signals recorded from small neocortical patches away from identified seizure onset areas. We show that machine learning algorithms can discriminate between interictal and preictal periods based on multiunit activity (i.e. thresholded action potential counts) and multi-frequency band local field potentials recorded via 4 X 4 mm2 microelectrode arrays. Microelectrode arrays were implanted in 5 patients undergoing neuromonitoring for resective surgery. Post-implant analysis revealed arrays were outside the seizure onset areas. Preictal periods were defined as the 1-hour period leading to a seizure. A 5-minute gap between the preictal period and the putative seizure onset was enforced to account for potential errors in the determination of actual seizure onset times. We used extreme gradient boosting and long short-term memory networks for prediction. Prediction accuracy based on the area under the receiver operating characteristic curves reached 90% for at least one feature type in each patient. Importantly, successful prediction could be achieved based exclusively on multiunit activity. This result indicates that preictal activity in the recorded neocortical patches involved not only subthreshold postsynaptic potentials, perhaps driven by the distal seizure onset areas, but also neuronal spiking in distal recurrent neocortical networks. Beyond the commonly identified seizure onset areas, our findings point to the engagement of large-scale neuronal networks in the neural dynamics building up toward a seizure. Our initial results obtained on currently available human intracortical recordings warrant new studies on larger datasets, and open new perspectives for seizure prediction and control by emphasizing the contribution of multiscale neural signals in large-scale neuronal networks.

neuroscience↗