Biology subjects

Anderson, W. S.

Publications and source records attributed to Anderson, W. S..

9 recordsLinked to original sources

bioRxiv2026

Cellular code for mnemonic pattern separation in the human hippocampus is revealed by false memories

Theoretical models propose that pattern separation, a computation thought to be implemented by the hippocampus, allows us to differentiate between familiar and novel items. However, whether pattern separation operates in the human hippocampus remains contested, with no established single-cell correlate. We recorded the single neuron activity of 3506 neurons in the human brain while 97 patients performed a recognition memory task in which novel images similar to previously seen images led to false memories and associated behavioral errors. We identified two kinds of memory selective neurons distributed across the brain: those responding differently to falsely recognized novel and correctly recognized familiar images in a manner compatible with pattern separation, and the other signaling the subject's choice. At the population level, these cells predicted mnemonic ground truth in the hippocampus and the decision in the pre-supplementary motor area, illustrating the progression from mnemonic signals to decisions. Removing pattern separation-signaling cells abolished the continuous memory strength gradient present in the hippocampus, suggesting a role of these cells in separating memories of different strength. These results establish a single cell correlate for mnemonic pattern separation in the human hippocampus and show its behavioral relevance in episodic memory.

neuroscience↗

bioRxiv2026

GPU-Accelerated Optimization Investigates Synaptic Reorganization Underlying Pathological Beta Oscillations in a Basal Ganglia Network Model

ObjectivePathological beta-band oscillations (13 to 30 Hz) in the subthalamic nucleus (STN) are a hallmark of Parkinsons disease and a primary target for deep brain stimulation therapy, yet the specific pattern of synaptic reorganization that drives their emergence remains incompletely understood. We developed a GPU-accelerated computational framework to systematically investigate combinations of synaptic changes across basal ganglia pathways that produce Parkinsonian beta oscillations while satisfying literature-based electrophysiology constraints. ApproachWe implemented a biophysically detailed spiking network model of the STN, external globus pallidus (GPe), and internal globus pallidus (GPi) in JAX (a high-performance numerical computing Python library), achieving a 490-fold speedup over conventional CPU-based simulation. Using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) we optimized 10 network parameters across two stages: first establishing a healthy baseline matching primate electrophysiology data, then searching within biologically motivated bounds for synaptic modifications that reproduce Parkinsonian firing rates and beta power. Fixed in-degree connectivity ensured optimized parameters produced scale-invariant dynamics from 450 to 45000 neurons. All simulations ran on a single cloud GPU instance at 84 cents per hour. Main ResultsThe optimizer converged on a coordinated pattern of synaptic reorganization dominated by asymmetric changes within the STN-GPe reciprocal loop: STN to GPe excitation increased 2.21-fold while GPe to STN inhibition collapsed to 0.11-fold of its healthy value. STN to GPi and GPe to GPi pathways changed minimally (1.06-fold and 1.45-fold respectively). This configuration transformed asynchronous firing (beta: 0.4 percent of spectral power) into synchronized bursting with prominent beta oscillations (49.4 percent), with firing rate changes matching experimental observations. Network dynamics were invariant across a 100-fold range of network sizes (firing rate deviation less than 2.4 Hz; all metrics p less than 0.001 across 10 random seeds at 45000 neurons). We implemented a simplified deep brain stimulation model for validation purposes, which achieved complete beta suppression (49.4 percent to 0.0 percent) and restored GPi output to healthy levels. SignificanceThese results suggest that pathological beta oscillations emerge from a specific pattern of synaptic reorganization, namely the reduction of GPe inhibitory feedback to STN. The GPU-accelerated optimization framework, running on commodity cloud infrastructure, demonstrates an accessible platform for parameter exploration in neural circuit models and a foundation for generating synthetic training data for adaptive deep brain stimulation algorithms.

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bioRxiv2025

Stimulus-specific recruitment of human amygdala neurons predicts episodic memory encoding success

Controlling whether a given experience is encoded into long-term memory and thus later remembered is a crucial component of our memory system whose failure is often at the root of memory disorders. One brain area that takes part in controlling which experiences are remembered is the amygdala, but the mechanisms by which it does so remain poorly understood. Here we examined single-neuron activity and local field potentials as human participants performed recognition memory tasks with visual stimuli. Category-selective amygdala neurons exhibited elevated firing rates during encoding of later remembered items versus forgotten items. This subsequent memory effect was restricted to images of the preferred category of a given cell, was stronger and appeared earlier in the amygdala compared to the hippocampus, and did not depend on the valence and arousal of the stimuli. In contrast, category selective cells immediately upstream in the ventral temporal cortex did not exhibit a subsequent memory effect, highlighting specificity to the amygdala. Successful memory formation was accompanied by enhanced spike-field coherence between the activity of category cells in the amygdala and hippocampal field potentials. These findings, replicated in two large independent datasets with two different tasks, demonstrate that recruitment of stimulus-specific amygdala representations predicts episodic memory formation, particularly in the right amygdala. This data suggests category cells in the right amygdala as a cellular target for interventions to treat memory disorders in humans.

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bioRxiv2025

Hippocampal stimulation reveals causal role of persistent neural activity in human working memory

Working memory (WM) enables the temporary maintenance and manipulation of information, supporting flexible, goal-directed behavior. While converging evidence suggests that persistent activity in the hippocampus and other areas of the brain contributes to WM storage, this link has not been examined by manipulating neural activity experimentally. Here, we combined simultaneous intracranial single-neuron recordings with focal electrical stimulation in the human hippocampus to test the role of memoranda-selective persistent neural activity for WM. Thirty patients with implanted hybrid depth electrodes performed a WM task with images as memoranda. Electrical stimulation (2 s, 50 Hz, 1 mA) was delivered to the hippocampus during the maintenance period on a subset of trials. Behaviorally, stimulation impaired WM performance, increasing response times especially in low-load conditions. Neuronally, stimulation reduced memoranda-selective activity in hippocampus and ventral temporal cortex (VTC), thereby disrupting content-specific neural representations of WM content. The extent of stimulation-induced change in firing rate mediated trial-by-trial impaired WM-related behavior, linking disrupted neural selectivity to impaired WM. At the population level, stimulation shifted neural trajectories farther from attractor states, consistent with degraded mnemonic fidelity. Together, these data provide evidence that selective persistent activity of individual neurons in hippocampus and VTC supports WM maintenance in humans. Our results demonstrate that hippocampal stimulation disrupts both single-neuron coding and population-level attractor stability, linking cellular mechanisms to behavior and highlighting the contributions of persistent activity to WM maintenance.

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bioRxiv2024

Ultrastructural membrane dynamics of mouse and human cortical synapses

Live human brain tissues provide unique opportunities for understanding synaptic transmission. Investigations have been limited to anatomy, electrophysiology, and protein localization--while crucial parameters such as synaptic vesicle dynamics were not visualized. Here we utilize zap-and-freeze time-resolved electron microscopy to overcome this hurdle. First, we validate the approach with acute mouse brain slices to demonstrate that slices can be stimulated to produce calcium signaling. Next, we show that synaptic vesicle endocytosis is induced in both mouse and human brain slices. Crucially, clathrin-free endocytic pits appear immediately next to the active zone, where ultrafast endocytosis normally occurs, and can be trapped at this location by a dynamin inhibitor. In both species a protein essential for ultrafast endocytosis, Dynamin 1xA, localizes to the region peripheral to the active zone, the putative endocytic zone, indicating a possible conserved mechanism between mouse and human. This approach has the potential to reveal dynamic, high-resolution information about synaptic membrane trafficking in intact human brain slices.

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bioRxiv2024

Persistent activity during working memory maintenance predicts long-term memory formation in the human hippocampus

Working Memory (WM) and Long-Term Memory (LTM) are often viewed as separate cognitive systems. Little is known about how these systems interact when forming memories. We recorded single neurons in the human medial temporal lobe while patients maintained novel items in WM and a subsequent recognition memory test for the same items. In the hippocampus but not the amygdala, the level of WM content-selective persist activity during WM maintenance was predictive of whether the item was later recognized with high confidence or forgotten. In contrast, visually evoked activity in the same cells was not predictive of LTM formation. During LTM retrieval, memory-selective neurons responded more strongly to familiar stimuli for which persistent activity was high while they were maintained in WM. Our study suggests that hippocampal persistent activity of the same cell supports both WM maintenance and LTM encoding, thereby revealing a common single-neuron component of these two memory systems.

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bioRxiv2023

Phase-Amplitude Coupling Detection and Analysis of Human 2-Dimensional Neural Cultures in Multi-well Microelectrode Array in Vitro

Human induced pluripotent stem cell (hiPSC)- derived neurons offer the possibility of studying human-specific neuronal behaviors in physiologic and pathologic states in vitro. However, it is unclear whether these cultured neurons can achieve the fundamental network behaviors that are required to process information in the human brain. Investigating neuronal oscillations and their interactions, as occurs in cross-frequency coupling (CFC), is potentially a relevant approach. Microelectrode array culture plates provide a controlled framework to study populations of hiPSC-derived cortical neurons (hiPSC-CNs) and their electrical activity. Here, we examined whether networks of two-dimensional cultured hiPSC-CNs recapitulate the CFC that is present in networks in vivo. We analyzed the electrical activity recorded from hiPSC-CNs grown in culture with hiPSC-derived astrocytes. We employed the modulation index method for detecting phase-amplitude coupling (PAC) and used an offline spike sorting method to analyze the contribution of a single neurons spiking activities to network behavior. Our analysis demonstrates that the degree of PAC is specific to network structure and is modulated by external stimulation, such as bicuculine administration. Additionally, the shift in PAC is not driven by a single neurons properties but by network-level interactions. CFC analysis in the form of PAC explores communication and integration between groups of nearby neurons and dynamical changes across the entire network. In vitro, it has the potential to capture the effects of chemical agents and electrical or ultrasound stimulation on these interactions and may provide valuable information for the modulation of neural networks to treat nervous system disorders in vivo. SignificancePhase amplitude coupling (PAC) analysis demonstrates that the complex interactions that occur between neurons and network oscillations in the human brain, in vivo, are present in 2-dimensional human cultures. This coupling is implicated in normal cognitive function as well as disease states. Its presence in vitro suggests that PAC is a fundamental property of neural networks. These findings offer the possibility of a model to understand the mechanisms and of PAC more completely and ultimately allow us to understand how it can be modulated in vivo to treat neurologic disease.

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bioRxiv2022

Task-specific neural processes underlying conflict resolution during cognitive control

Cognitive control involves flexibly combining multiple sensory inputs with task-dependent goals during decision making. Several tasks have been proposed to examine cognitive control, including Stroop, Eriksen-Flanker, and the Multi-source interference task. Because these tasks have been studied independently, it remains unclear whether the neural signatures of cognitive control reflect abstract control mechanisms or specific combinations of sensory and behavioral aspects of each task. To address this question, here we recorded invasive neurophysiological signals from 16 subjects and directly compared the three tasks against each other. Neural activity patterns in the theta and high-gamma frequency bands differed between incongruent and congruent conditions, revealing strong modulation by conflicting task demands. These neural signals were specific to each task, generalizing within a task but not across tasks. These results highlight the complex interplay between sensory inputs, motor outputs, and task demands and argue against a universal and abstract representation of conflict.

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bioRxiv2018

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

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