Biology subjects

Leveroni, Z. M.

Publications and source records attributed to Leveroni, Z. M..

3 recordsLinked to original sources

bioRxiv2025

Distinct goal location beta frequency dynamics in hippocampus and prefrontal cortex across learning

Neural activity at goal locations contributes to learning by providing feedback on the success of preceding actions. This period engages neocortical and hippocampal networks, which serve distinct functions in reward processing and in forming associations between experience and reward. A neocortical network signature for reward feedback processing is beta oscillations (15-30 Hz). Beta oscillations are thought to coordinate distributed neural processes across brain regions. However, it is unknown whether beta oscillations coordinate hippocampal-neocortical networks during the goal period, or how their dynamics relate to learning. Here, we show that beta oscillations occur in both hippocampal CA1 and the prefrontal cortex (PFC) when rats reach goal locations in spatial navigation tasks. Despite the presence of beta oscillations in both regions after goal entry, beta activity in each region differed in spectral and temporal properties. These differences suggest that the hippocampus and PFC are weakly coupled at the beta frequency. We found that across learning, the strengths of PFC and CA1 beta oscillations were inversely related: PFC beta power increased and CA1 beta power decreased. Beta burst properties in PFC also had an inverse relationship to those of hippocampal sharp wave-ripples (SWRs), a prominent hippocampal process required for learning. We found a subset of PFC neurons modulated by both beta and hippocampal SWRs, which had distinct task-related firing patterns. Our results suggest that during outcome processing at goal locations, the neocortex and hippocampus are locally modulated at the beta frequency and then become coordinated for memory-related processes during SWRs.

neuroscience↗

bioRxiv2024

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specificneuromodulation

AbstractClosed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

neuroscience↗

bioRxiv2023

Experience shapes initial exploration for non-generalizable spatial learning

Experience can change how individuals learn. Learning to solve a new problem can be accelerated by generalizing known rules in the new context, but the impact of experience on solving problems where generalization cannot be applied remains unclear. To study the impact of experience on solving new problems that are distinct from previously learned tasks, we examined how rats learned a new spatial navigation task after having previously learned different sets of spatial navigation tasks. The new task differed from the previous tasks in spatial layout and navigation rule, and could not be solved by applying previously learned rules. We found that different experience histories did not impact task performance in the new task. However, by examining navigation choices made by rats, we found exploration patterns during the early stage of learning in the new task was dependent on experience history. We identified these behavioral differences by analyzing each rats navigation choices and by modeling their choice sequences with a modified distance dependent Chinese restaurant process. We further pinpointed the behavioral difference to sequential turn/no turn decisions made at choice points. Our results indicate that experience can influence problem-solving strategies when learning to solve new problems. Individuals with distinct experience histories can approach new problems from different starting points but converge on the same solution.

neuroscience↗