Biology subjects

O'Reilly, R. C.

Publications and source records attributed to O'Reilly, R. C..

7 recordsLinked to original sources

bioRxiv2026

Synaptic Plasticity as a Function of the Temporal Derivative

A major outstanding question in neuroscience is whether the neocortex uses the same powerful learning algorithm as current AI models: error backpropagation. One way this could be accomplished is as a function of the temporal derivative (i.e., differences in neural activity states over time), which can closely approximate the backpropagated error gradient. We tested the hypothesis that the direction of synaptic plasticity is a function of the temporal derivative in synaptic activity over the course of a 200 ms (5 Hz) theta cycle. Using mouse hippocampal slices, we drove presynaptic activity across the two 100 ms halves of a 200 ms window at either 25 Hz or 50 Hz, combined with corresponding low and high magnitudes of postsynaptic depolarization, testing all four 2x2 combinations of these low and high activity levels, while measuring the resulting effects on synaptic efficacy (as measured by EPSP amplitude to standard test probes). Consistent with the computational hypothesis, a positive temporal derivative (low to high) resulted in LTP (increased synaptic strength), while a negative temporal derivative (high to low) resulted in LTD. Critically, both no-change conditions (stable low or high across 200 ms) resulted in no net synaptic change, even though the high no-change condition had the highest overall synaptic activity levels. Possible biochemical mechanisms that could support these results are discussed.

neuroscience↗

bioRxiv2025

Fronto-Hippocampal Synchronization in Rapid Spatial Learning in Humans

Achieving goals in real-life situations--from fetching a glass of water to landing a dream job--often requires planning based on experience and executing a sequence of actions. Neurophysiological research in animal models has indicated that the orbitofrontal cortex (OFC) mediates relationships between memory, actions, and outcomes and the hippocampus and other medial temporal lobe (MTL) regions are known to be critical for rapid learning, but little is known about how these areas interact to support rapid learning and retrieval of goal-directed action sequences in humans. Here, we leverage a rare opportunity to investigate human OFC gamma oscillations and examine the coordination between the OFC and MTL during a continuous multi-step task that requires applying recently acquired experience to guide behavior. We used multisite intracranial electroencephalography (iEEG) recordings while participants searched for a hidden goal in an animated game to study neural activity in both brain areas during goal-directed behavior. Hippocampal ripples--brief high-frequency oscillations reflecting synchronized neuronal firing--are known to support memory consolidation during sleep, but their role during active memory retrieval and updating remains unclear. We found that OFC gamma activity was modulated by both memory demands and ripples in the hippocampus and adjacent structures. Notably, ripple-coupled OFC gamma during exploration was associated with subsequent task performance. We propose that hippocampal ripples mark a narrow window, supporting hippocampal-cortical communication required for successful goal encoding for future behaviors.

neuroscience↗

bioRxiv2025

Not a global map, but a local hash: grid cells decorrelate the representation of position and scramble long-range distance information

Grid cells in the medial entorhinal cortex construct an intriguing multiperiodic representation of space whose properties have been the subject of much theoretical speculation. Here we combine modeling with analyses of neural population data from mice and rats to show that the grid cell representation is ideally set up to decorrelate and assign easily distinguishable labels to inputs, thus acting as a pattern separation device, much like a hash function in computing rather than a global map or metric. The multiple modules of the grid cell system allow the threshold for pattern separation to be controlled. We also extend these arguments to show how grid cells can perform pattern separation in abstract and higher-dimensional spaces. This pattern separation ability may serve to enhance episodic memory in the hippocampal formation by reducing interference between similar patterns.

neuroscience↗

bioRxiv2024

Recurrent Inhibitory Dynamics in the Entorhinal Cortex Support Pattern Separation

The entorhinal cortex (EC) provides the major input to the hippocampus (HPC). Numerous computational models on the EC propose that its grid cells serve as a spatial metric, supporting path integration and efficient generalization. However, little is known about how these cells could contribute to episodic memory, which emphasizes episode-specific representations that align with pattern separation. Taking into consideration anatomical specifications of EC inputs to the HPC and computational principles underlying the EC-HPC memory system, we argue that EC layer IIa (EC2a) supports pattern separation and EC layer IIb/III (EC2b/3) supports generalization. Utilizing recurrent inhibition and the nature of single EC2a neurons binding converging inputs from the neocortex (i.e., conjunctive coding), we built a biologically-based neural network model of the EC-HPC system for episodic memory. By examining how EC2a transformed its cortical inputs and output them to the trisynaptic pathway (EC2a - Dentate Gyrus - CA3 - CA1), we found that instead of systematically generalizing across similar inputs, recurrent inhibition and conjunctive coding in EC2a neurons support strong pattern separation and increase mnemonic discrimination. Furthermore, lesioning EC2a neurons in the model resembled memory impairments found in people with Alzheimers Disease, suggesting an intertwined relationship between memory and the majority of pure grid cells in the EC. On the other hand, the topographically organized monosynaptic pathway (EC2b/3 - CA1) is computationally more suitable for efficient factorization and generalization. This model provides novel anatomically-based predictions regarding the computational roles of EC cells in pattern separation and generalization, which together form a critical computational framework for both episodic memory and spatial navigation.

neuroscience↗

bioRxiv2023

The representational geometry of cognitive maps under dynamic cognitive control

Recent work has shown that the brain abstracts non-spatial relationships between entities or task states into representations called cognitive maps. Here, we investigate how cognitive control enables flexible top-down selection of goal-relevant information from multidimensional cognitive maps retrieved from memory. We examine the relationship between cognitive control and representational geometry by conducting parallel analyses of functional magnetic resonance imaging data and recurrent neural network models trained to perform the same task. We find both stable map-like representations in a medial temporal lobe and orbitofrontal cortical network that reflect both task-relevant and irrelevant dimensions and context-dependent, orthogonal representations of only task-relevant dimensions in a frontoparietal network. These representational motifs also emerge with distinct temporal profiles over the course of training in the recurrent neural network, with map-like representations appearing first. We further show that increasing control demands due to incongruence (conflicting responses) between current task-relevant and irrelevant dimensions impact the geometry of subjective representations, and the degree of this effect further accounts for individual differences in cognitive control. Taken together, our findings show how complementary representational geometries balance stability and behavioral flexibility, and reveal an intricate relationship between cognitive control and cognitive map geometry.

neuroscience↗

bioRxiv2022

Memory out of context: Spacing effects and decontextualization in a computational model of the medial temporal lobe

Some neural representations change across multiple timescales. Here we argue that modeling this "drift" could help explain the spacing effect (the long-term benefit of distributed learning), whereby differences between stored and current temporal context activity patterns produce greater error-driven learning. We trained a neurobiologically realistic model of the entorhinal cortex and hippocampus to learn paired associates alongside temporal context vectors that drifted between learning episodes and/or before final retention intervals. In line with spacing effects, greater drift led to better model recall after longer retention intervals. Dissecting model mechanisms revealed that greater drift increased error-driven learning, strengthened weights in slower-drifting temporal context neurons (temporal abstraction), and improved direct cue-target associations (decontextualization). Intriguingly, these results suggest that decontextualization -- generally ascribed only to the neocortex -- can occur within the hippocampus itself. Altogether, our findings provide a mechanistic formalization for established learning concepts such as spacing effects and errors during learning.

neuroscience↗

bioRxiv2021

Correcting the Hebbian Mistake: Toward a Fully Error-Driven Hippocampus

The hippocampus plays a critical role in the rapid learning of new episodic memories. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i.e., "cells that fire together, wire together"). However, Hebbian learning is computationally suboptimal as it modifies weights unnecessarily beyond what is actually needed to achieve effective retrieval, causing more interference and resulting in a lower learning capacity. Our previous computational models have utilized a powerful, biologically plausible form of error-driven learning in hippocampal CA1 and entorhinal cortex (EC) (functioning as a sparse autoencoder) by contrasting local activity states at different phases in the theta cycle. Based on specific neural data and a recent abstract computational model, we propose a new model called Theremin (Total Hippocampal ERror MINimization) that extends error-driven learning to area CA3 -- the mnemonic heart of the hippocampal system. In the model, CA3 responds to the EC monosynaptic input prior to the EC disynaptic input through dentate gyrus (DG), giving rise to a temporal difference between these two activation states, which drives error-driven learning in the EC[->]CA3 and CA3{leftrightarrow}CA3 projections. In effect, DG serves as a teacher to CA3, correcting its patterns into more pattern-separated ones, thereby reducing interference. Results showed that Theremin, compared with our original model, has significantly increased capacity and learning speed. The model makes several novel predictions that can be tested in future studies. Author SummaryExemplified by the famous case of patient H.M. (Henry Molaison) who had his hippocampus surgically removed, the hippocampus is critical for learning and remembering about everyday events -- what is typically called "episodic memory." The dominant theory for how it learns is based on the intuitive principle stated by Donald Hebb in 1949, that neurons that "fire together, wire together" -- when two neurons are active at the same time, the strength of their connection increases. We show in this paper that using a different form of learning based on correcting errors (error-driven learning) results in significantly improved episodic memory function in a biologically-based computational model of the hippocampus. This model also provides a significantly better account of behavioral data on the testing effect, where learning by testing with partial cues is better than learning with the complete set of information.

neuroscience↗