Biology subjects

McClelland, J. L.

Publications and source records attributed to McClelland, J. L..

5 recordsLinked to original sources

bioRxiv2025

Capturing Rapid Behavioral Adaptation in an Updated Theory of the Hippocampal Cognitive Map and its Neural Implementation

Animals sometimes adapt their behavior after a single exposure to novel information. Here, we integrate abstract theory with recent mechanistic insights to understand this and other examples of rapid adaptation. We ground our investigation in studies of animal learning in spatial contexts, where it is widely held that learning depends on the formation of a cognitive map reflected in learned representations in hippocampal areas CA1 and CA3. We extend the successor representation (SR) theory of the cognitive map, incorporating the weighting of this representation by perceived salience (PS) signals, and using it to represent future availability of environmental features that allow behavior to be guided flexibly by an animals goals and needs at later times. To capture rapid adaptation, we rely on behavioral time-scale synaptic plasticity (BTSP), a form of plasticity that captures one-shot formation of new place fields. Through simulations, we show that our hippocampal model network can learn the PS-weighted SR rapidly and set up the ability to replay experiences in the environment after a single exposure through BTSP. Spontaneous replay activity during subsequent offline periods strengthens the PS-weighted SR. Predictions of environmental features also develop rapidly, and behavioral simulations show how these learned representations give rise to need-dependent choice, rapid behavioral adaptation to changes in the availability of reward, and one-trial shock avoidance learning.

neuroscience↗

bioRxiv2023

Retrieval induced forgetting in a non-monotonic hippocampal model

Retrieval induced forgetting (RIF) occurs when the retrieval of one item negatively impacts the recall probability of related items stored in memory (Anderson et al., 1994). Recently, Ritvo et al. (2023) demonstrated RIF emerges in a neural network model equipped with non-monotonic plasticity. Their finding supports the non-monotonic plasticity hypothesis (NMPH; Ritvo et al., 2019): the theory that connection changes in the brain follow a "U" shaped function of post-synaptic stimulation. Here, we apply a unique implementation of non-monotonic plasticity to a neural network model of an idealized hippocampus (HPC) and evaluate it with an adaptation of a classic RIF task. The model evidences the behavioral and representational characteristics of RIF, replicating Ritvo et al. (2023). As a monotonic baseline model failed these tests, we provide evidence of non-monotonic plasticitys sufficiency for RIF. In addition to demonstrating the NMPH is robust to multiple implementations and evaluative paradigms, we conduct additional analysis to provide a mechanistic explanation for how non-monotonic plasticity brings about RIF. Lastly, we evaluate the model with an expansion of RIF: reverse RIF. The model fails this final test, raising questions for future research on the necessary parameters of non-monotonic plasticity and whether it must pair with complementary processes in the brain.

neuroscience↗

bioRxiv2021

A weighted constraint satisfaction approach to human goal-directed decision making

When we plan for long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must also be considered. But when a subgoal must be resolved first, irrelevant future information should not interfere with the processing of more proximal, subgoal-relevant information. We explore the idea that decision making in both situations relies on the flexible modulation of the degree to which different pieces of information under consideration are weighted, rather than explicitly decomposing a problem into smaller parts and solving each part independently. We asked participants to find the shortest goal-reaching paths in mazes and modeled their initial path choices as a noisy, weighted information integration process. In a base task where choosing the optimal initial path required weighting starting-point and goal-proximal factors equally, participants did take both constraints into account, with participants who made more accurate choices tending to exhibit more balanced weighting. The base task was then embedded as an initial subtask in a larger maze, where the same two factors constrained the optimal path to a subgoal, and the final goal position was irrelevant to the initial path choice. In this more complex task, participants choices reflected predominant consideration of the subgoal-relevant constraints, but also some influence of the initially-irrelevant final goal. More accurate participants placed much less weight on the optimality-irrelevant goal and again tended to weight the two initially-relevant constraints more equally. These findings suggest that humans may rely on a graded, task-sensitive weighting of multiple constraints to generate approximately optimal decision outcomes in both hierarchical and non-hierarchical goal-directed tasks. Author SummaryDifferent problems require the consideration of different information sources, including often useful long-range, future information that may impact our immediate decisions. However, when future information is irrelevant to a key subgoal, it can be desirable to focus on achieving the subgoal first. We suggest that humans rely on appropriately weighting relevant information over irrelevant information to generate decision outcomes in both types of situations. We conducted behavioral experiments and fitted models of decision processes to understand to what extent people considered various task factors in choosing the initial path in different mazes, both when a simple maze occurred alone or was embedded as an initial part in a larger maze. Our results show that people approximate the optimal decision outcomes in both tasks by modulating the weighting of different factors during planning, and that people who made more accurate initial path choices modulated these weightings more successfully than those who made less accurate choices.

animal behavior and cognition↗

bioRxiv2020

Integration of New Information in Memory: New Insights from a Complementary Learning Systems Perspective

According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop a theoretical understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks that capture the qualitative aspects of the learning dynamics of the more complex non-linear networks used in previous work. The time course of learning in these networks can be linked to the hierarchical structure in the training data, captured mathematically as a set of dimensions that correspond to the branches in the hierarchy. In this context, a new item to be learned can be characterized as having aspects that project onto previously known dimensions, and others that require adding a new branch/dimension. The projection onto the known dimensions can be learned rapidly without interleaving, but learning the new dimension requires gradual interleaved learning. When a new item only overlaps with items within one branch of a hierarchy, interleaving can focus on the previously-known items within this branch, resulting in faster integration with less inter-leaving overall. The discussion considers how the brain might exploit these facts to make learning more efficient and highlights predictions about what aspects of new information might be hard or easy to learn.

neuroscience↗

bioRxiv2017

I Like Coffee With Cream And Dog? Change In An Implicit Probabilistic Representation Captures Meaning Processing In The Brain

The N400 component of the event-related brain potential has aroused much interest because it is thought to provide an online measure of meaning processing in the brain. Yet, the underlying process remains incompletely understood and actively debated. Here, we present a computationally explicit account of this process and the emerging representation of sentence meaning. We simulate N400 amplitudes as the change induced by an incoming stimulus in an implicit and probabilistic representation of meaning captured by the hidden unit activation pattern in a neural network model of sentence comprehension, and we propose that the process underlying the N400 also drives implicit learning in the network. The model provides a unified account of 16 distinct findings from the N400 literature and connects human language processing with successful deep learning approaches to language processing.

neuroscience↗