Oliviers, G. | Search bioRxiv

bioRxiv2024

Response of neuronal populations to phase-locked stimulation: model-based predictions and validation

BackgroundModulation of neuronal oscillations holds promise for the treatment of neurological disorders. Nonetheless, stimulating neuronal populations in a continuous open-loop manner can lead to side effects and suboptimal efficiency. Closed-loop strategies such as phase-locked stimulation aim to address these shortcomings by offering a more targeted modulation. While theories have been developed to understand the neural response to stimulation, their predictions have not been thoroughly tested using experimental data. ObjectiveWe aimed to test the predictions of a mathematical model regarding the response of neuronal populations to phase-locked stimulation. MethodsUsing a coupled oscillator model, we expanded on two key predictions describing the response to stimulation as a function of the phase and amplitude of ongoing neural activity. To investigate these predictions, we analyzed electrocorticogram (ECoG) recordings from a previously conducted study in Parkinsonian rats, and extracted the corresponding phase and response curves. ResultsWe demonstrated that the amplitude response to stimulation is strongly correlated to the derivative of the phase response ({rho} > 0.8) in all animals except one, thereby validating a key model prediction. The second prediction postulated that the stimulation becomes ineffective when the network synchrony is high, a trend that appeared missing in the data. Our analysis explained this discrepancy by showing that the neural populations in Parkinsonian rats did not reach the level of synchrony for which the theory would predict ineffective stimulation. ConclusionsOur results highlight the potential of fine-tuning stimulation paradigms informed by mathematical models that consider both the ongoing phase and amplitude of the targeted neural oscillation.

neuroscience↗

bioRxiv2024

Learning probability distributions of sensory inputswith Monte Carlo Predictive Coding

It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, predictive coding theory proposed how the probabilistic models can be learned by networks of neurons employing local synaptic plasticity. On the other hand, neural sampling theories have demonstrated how stochastic dynamics enable neural circuits to represent the posterior distributions of latent states of the environment. Here, we bring together these two lines of theoretic work by introducing Monte Carlo predictive coding (MCPC). We demonstrate that the integration of predictive coding with neural sampling results in a neural network that learns precise generative models using local computation and plasticity. The neural dynamics of MCPC infer the posterior distributions of the latent states in the presence of sensory inputs, and can generate likely inputs in their absence. Furthermore, MCPC captures the experimental observations on the variability of neural activity during perceptual tasks. By combining predictive coding and neural sampling, MCPC offers a unifying theory of cortical computation which can account for both sets of neural data that previously had been explained by these individual frameworks.

neuroscience↗