Biology subjects

Krichmar, J. L.

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

6 recordsLinked to original sources

bioRxiv2025

Episodic-Like Memory in a Simulation of Cuttlefish Behavior

Episodic memory involves remembering the what, when, and where components of an event. It has been observed in humans, other vertebrates, and cephalopods. In clever behavioral experiments, cuttlefish have been shown to have episodic-like memory, where they demonstrate the ability to remember when and where a preferred food source will appear. The present work replicates this behavior with a parsimonious model of episodic memory. To further test this model and explore episodic-like memory, we introduce a predator-prey scenario in which the agent must remember what creatures (e.g. predator, desirable prey, or less desirable prey) appear at a given time and region of the model environment. This simulates similar situations that cephalopods face in the wild. They will typically hide when predators are in the area, and hunt for prey when available. When the memory model is queried for an action (e.g., hunt or hide), the cuttlefish agent hunts for preferred food, like shrimp, when available, and hides at other times when a predator appears. When the memory model is queried for a place, the cuttlefish agent acts opportunistically, seeking less-preferred food (e.g., crabs) if it is located farther from a predator. These differences show how behavior can be altered depending on how memory is accessed. Querying the model over time might mimic mental time travel, a hallmark of episodic memory. Although developed with cephalopods in mind, the model shares similarities with the hippocampal indexing theory and captures aspects of vertebrate episodic memory. This suggests that the underlying mechanisms supporting episodic-like behavior in the present model may not be unique to cephalopods.

animal behavior and cognition↗

bioRxiv2025

Neural Mechanisms Linking Global Maps to First-Person Perspectives

Humans and many animals possess the remarkable ability to navigate environments by seamlessly switching between first-person perspectives (FPP) and global map perspectives (GMP). However, the neural mechanisms that underlie this transformation remain poorly understood. In this study, we developed a variational autoencoder (VAE) model, enhanced with recurrent neural networks (RNNs), to investigate the computational principles behind perspective transformations. Our results reveal that temporal sequence modeling is crucial for maintaining spatial continuity and improving transformation accuracy when switching between FPPs and GMPs. The models latent variables capture many representational forms seen in the distributed cognitive maps of the mammalian brain, such as head direction cells, place cells, corner cells, and border cells, but notably not grid cells, suggesting that perspective transformation engages multiple brain regions beyond the hippocampus and entorhinal cortex. Furthermore, our findings demonstrate that landmark encoding, particularly proximal environmental cues such as boundaries and objects, play a critical role in enabling successful perspective shifts, whereas distal cues are less influential. These insights on perspective linking provide a new computational framework for understanding spatial cognition and offer valuable directions for future animal and human studies, highlighting the significance of temporal sequences, distributed representations, and proximal cues in navigating complex environments. Significance StatementUnderstanding how the brain transforms between different spatial perspectives is crucial for advancing our knowledge of spatial cognition and navigation. This study presents a novel computational approach that bridges the gap between neural recordings and behavior, offering insights into the underlying mechanisms of perspective transformation. Our findings suggest how the brain integrates temporal sequences, distributed representations, and environmental cues to maintain a coherent sense of space. By demonstrating the importance of proximal cues and temporal context, our computational model provides testable predictions for future neurophysiological studies in humans and animals.

neuroscience↗

bioRxiv2021

Flexible Path Planning through Vicarious Trial and Error

AO_SCPLOWBSTRACTC_SCPLOWFlexible planning is necessary for reaching goals and adapting when conditions change. We introduce a biologically plausible path planning model that learns its environment, rapidly adapts to change, and plans efficient routes to goals. Unlike prior models of hippocampal replay, our model addresses the decision-making process when faced with uncertainty. We tested the model in simulations of human and rodent navigation in mazes. Like the human and rat, the model was able to generate novel shortcuts, and take detours when familiar routes were blocked. Similar to rodent hippocampus recordings, the neural activity of the model resembles neural correlates of Vicarious Trial and Error (VTE) during early learning or during uncertain conditions. Similar to rodent studies, after learning, the neural activity resembles forward replay or preplay predicting a future route, and VTE activity decreases. We suggest that VTE, in addition to weighing possible outcomes, is a way in which an organism may gather information for future use.

neuroscience↗

bioRxiv2019

Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons - another neuromodulatory system known for its major implication in reinforcement learning and decision-making. In this paper, we propose a leaky-integrate-and-fire model of this mechanism. It implements a softmax-like selection with an uncertainty bonus by a cholinergic drive to dopaminergic neurons, which in turn influence synaptic currents of downstream neurons. The model is able to reproduce experimental data in two decision-making tasks. It also predicts that i) in the absence of cholinergic input, dopaminergic activity would not correlate with uncertainty, and that ii) the adaptive advantage brought by the implemented uncertainty-seeking mechanism is most useful when sources of reward are not highly uncertain. Moreover, this modeling work allows us to propose novel experiments which might shed new light on the role of acetylcholine in both random and directed exploration. Overall, this study thus contributes to a more comprehensive understanding of the roles of the cholinergic system and its involvement in decision-making in particular.

neuroscience↗

bioRxiv2018

A Neural Model of Schemas and Memory Consolidation

The ability to behave differently according to the situation is essential for survival in a dynamic environment. This requires past experiences to be encoded and retrieved alongside the contextual schemas in which they occurred. The complementary learning systems theory suggests that these schemas are acquired through gradual learning via the neocortex and rapid learning via the hippocampus. However, it has also been shown that new information matching a preexisting schema can bypass the gradual learning process and be acquired rapidly, suggesting that the separation of memories into schemas is useful for flexible learning. While there are theories of the role of schemas in memory consolidation, we lack a full understanding of the mechanisms underlying this function. For this reason, we created a biologically plausible neural network model of schema consolidation that studies several brain areas and their interactions. The model uses a rate-coded multilayer neural network with contrastive Hebbian learning to learn context-specific tasks. Our model suggests that the medial prefrontal cortex supports context-dependent behaviors by learning representations of schemas. Additionally, sparse random connections in the model from the ventral hippocampus to the hidden layers of the network gate neuronal activity depending on their involvement within the current schema, thus separating the representations of new and prior schemas. Contrastive Hebbian learning may function similarly to oscillations in the hippocampus, alternating between clamping and unclamping the output layer of the network to drive learning. Lastly, the model shows the vital role of neuromodulation, as a neuromodulatory area detects the certainty of whether new information is consistent with prior schemas and modulates the speed of memory encoding accordingly. Along with the insights that this model brings to the neurobiology of memory, it further provides a basis for creating context-dependent memories while preventing catastrophic forgetting in artificial neural networks.

neuroscience↗

bioRxiv2017

Sparse coding and dimensionality reduction in cortex

Supported by recent computational studies, sparse coding and dimensionality reduction are emerging as a ubiquitous coding strategy across brain regions and modalities, allowing neurons to achieve nonnegative sparse coding (NSC) by efficiently encoding high-dimensional stimulus spaces using a sparse and parts-based population code. Reducing the dimensionality of complex, multimodal sensory streams is critically important for metabolically constrained brain areas to represent the world. In this article, we provide an overview of NSC, summarize evidence for its role in neural computation in disparate regions of the brain, ranging from visual processing to spatial navigation, and speculate that specific forms of synaptic plasticity and homeostatic modulation may underlie its implementation. We suggest that NSC may be an organizing principle in the nervous system.

neuroscience↗