Biology subjects

Petrovici, M. A.

Publications and source records attributed to Petrovici, M. A..

3 recordsLinked to original sources

bioRxiv2024

Increased perceptual reliability reduces membrane potential variability in cortical neurons

Uncertainty is omnipresent. While humans and other animals take uncertainty into account during decision making, it remains unclear how it is represented in cortex. To investigate the effect of stimulus reliability on uncertainty representation in cortical neurons, we analyzed single unit activity data recorded in mouse PPC, while animals performed a multisensory change detection task. We further used simulation-based inference (SBI) to infer membrane potential statistics underlying the spiking activity. Our analysis shows that stimulus changes increase spiking rate while decreasing its variability. The inferred membrane potential statistics suggest that PPC neurons decrease their membrane potential variability in response to task relevant stimuli. Furthermore, more perceptually reliable stimuli lead to a larger decrease in membrane potential variability than less reliable ones. These findings suggest that individual cortical neurons track uncertainty, providing Bayesian benefits for downstream computations.

neuroscience↗

bioRxiv2023

Uncertainty-modulated prediction errors in cortical microcircuits

Understanding the variability of the environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model of the world. The basis for updating the model are prediction errors that arise from a difference between the current model and new sensory experiences. Although prediction error neurons have been identified in layer 2/3 of diverse brain areas, how uncertainty modulates these errors and hence learning is, however, unclear. Here, we use a normative approach to derive how uncertainty should modulate prediction errors and postulate that layer 2/3 neurons represent uncertainty-modulated prediction errors (UPE). We further hypothesise that the layer 2/3 circuit calculates the UPE through the subtractive and divisive inhibition by different inhibitory cell types. By implementing the calculation of UPEs in a microcircuit model, we show that different cell types can compute the means and variances of the stimulus distribution. With local activity-dependent plasticity rules, these computations can be learned context-dependently, and allow the prediction of upcoming stimuli and their distribution. Finally, the mechanism enables an organism to optimise its learning strategy via adaptive learning rates.

neuroscience↗

bioRxiv2023

A neuronal least-action principle for real-time learning in cortical circuits

AO_SCPLOWBSTRACTC_SCPLOWOne of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal least-action principle for cortical processing of sensory streams to produce appropriate behavioural outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimizes the local somato-dendritic mismatch error within individual neurons. For output neurons, the principle implies minimizing an instantaneous behavioural error. For deep network neurons, it implies the prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory input and the motor feedback during the ongoing sensory-motor transform. Online synaptic plasticity reduces the somato-dendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic laws for global real-time computation and learning in the brain.

neuroscience↗