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1. Episodic memory in nonhumans: An approach to understand an evolutionary function of consciousness

   Crystal, Jonathon D (January 2025, Philosophical transactions Royal Society Biological sciences)

   A fundamental question in comparative cognition concerns the evolution of consciousness. It is unlikely that consciousness appeared in humans without any precursors in other animals. Yet, the concepts that dominate our understanding of consciousness are inherently human centered, focusing on subjective experiences with a rich use of language. This understanding of consciousness is likely empirically intractable in studies of nonhumans. An alternative approach focuses on adopting a functional perspective. What function does consciousness serve? What can an animal capable of such a function do via its behavior? In this connection, I review the development of animal models of episodic memory. Episodic memory involves recalling the past and in humans is described as the phenomenological conscious experience of projecting oneself (autonoesis) in time (chronesthesia). Because there are no agreed upon empirical approaches to investigate subjective experiences in nonhumans, efforts to develop animal models of episodic memory have focused on the contents of episodic memory. I review experiments using rats that suggest that, at the moment of a memory assessment, the animal remembers back in time to an earlier event or episode. I conclude by evaluating implications of episodic memory in rats as a functional window into the evolution of consciousness. 

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2. Temporal foundations of episodic memory

   https://doi.org/10.3758/s13420-023-00608-x  

   Crystal, Jonathon D. (March 2024, Learning & Behavior)

   A fundamental question in the development of animal models of episodic memory concerns the role of temporal processes in episodic memory. Gallistel (1990) developed a framework in which animals remember specific features about an event, including the time of occurrence of the event and its location in space. Gallistel proposed that timing is based on a series of biological oscillators, spanning a wide range of periods. Accordingly, a snapshot of the phases of multiple oscillators provides a representation of the time of occurrence of the event. I review research on basic timing mechanisms that may support memory for times of occurrence. These studies suggest that animals use biological oscillators to represent time. Next, I describe recently developed animal models of episodic memory that highlight the importance of temporal representations in memory. One line of research suggests that an oscillator representation of time supports episodic memory. A second line of research highlights the flow of events in time in episodic memory. Investigations that integrate time and memory may advance the development of animal models of episodic memory. 

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3. Validation of a rodent model of episodic memory replay

   Sheridan, Cassandra L; Panoz-Brown, Danielle; Shiffrin, Richard M; Crystal, Jonathon D (June 2024, Learning & Behavior)

   Vivid episodic memories in humans have been described as the replay of the flow of past events in sequential order. Recently, Panoz-Brown et al. (2018) developed an olfactory memory task in which rats were presented with a list of trial-unique odors in an encoding context; next, in a distinctive memory assessment context, the rats were rewarded for choosing the second to last item from the list while avoiding other items from the list. In a different memory assessment context, the fourth to last item was rewarded. According to the episodic memory replay hypothesis, the rat remembers the list items and searches these items to find the item at the targeted locations in the list. However, events presented sequentially differ in memory trace strength, allowing a rat to use the relative familiarity of the memory traces, instead of episodic memory replay, to solve the task. Here, we directly manipulated memory trace strength by manipulating the odor intensity of target odors in both the list presentation and memory assessment. The rats relied on episodic memory replay to solve the memory assessment in conditions in which reliance on memory trace strength is ruled out. We conclude that rats are able to replay episodic memories. 

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4. Predictive attractor models

   Mounir, Ramy; Sarkar, Sudeep (December 2024, Curran Associate Inc)

   Sequential memory, the ability to form and accurately recall a sequence of events or stimuli in the correct order, is a fundamental prerequisite for biological and artificial intelligence as it underpins numerous cognitive functions (e.g., language comprehension, planning, episodic memory formation, etc.) However, existing methods of sequential memory suffer from catastrophic forgetting, limited capacity, slow iterative learning procedures, low-order Markov memory, and, most importantly, the inability to represent and generate multiple valid future possibilities stemming from the same context. Inspired by biologically plausible neuroscience theories of cognition, we propose Predictive Attractor Models (PAM), a novel sequence memory architecture with desirable generative properties. PAM is a streaming model that learns a sequence in an online, continuous manner by observing each input only once. Additionally, we find that PAM avoids catastrophic forgetting by uniquely representing past context through lateral inhibition in cortical minicolumns, which prevents new memories from overwriting previously learned knowledge. PAM generates future predictions by sampling from a union set of predicted possibilities; this generative ability is realized through an attractor model trained alongside the predictor. We show that PAM is trained with local computations through Hebbian plasticity rules in a biologically plausible framework. Other desirable traits (e.g., noise tolerance, CPU-based learning, capacity scaling) are discussed throughout the paper. Our findings suggest that PAM represents a significant step forward in the pursuit of biologically plausible and computationally efficient sequential memory models, with broad implications for cognitive science and artificial intelligence research. Illustration videos and code are available on our project page: https://ramymounir.com/publications/pam. 

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5. DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation

   https://doi.org/10.1145/3357384.3357870  

   Fu, Tao-yang; Lee, Wang-Chien (November 2019, Proceedings of the 28th ACM International Conference on Information and Knowledge Management)

   Estimating the travel time for a given path is a fundamental problem in many urban transportation systems. However, prior works fail to well capture moving behaviors embedded in paths and thus do not estimate the travel time accurately. To fill in this gap, in this work, we propose a novel neural network framework, namely Deep Image-based Spatio-Temporal network (DeepIST), for travel time estimation of a given path. The novelty of DeepIST lies in the following aspects:1) we propose to plot a path as a sequence of -generalized images"which include sub-paths along with additional information, such as traffic conditions, road network and traffic signals, in order to harness the power of convolutional neural network model (CNN)on image processing; 2) we design a novel two-dimensional CNN, namely PathCNN, to extract spatial patterns for lines in images by regularization and adopting multiple pooling methods; and 3) we apply a one-dimensional CNN to capture temporal patterns among the spatial patterns along the paths for the estimation. Empirical results show that DeepIST soundly outperforms the state-of-the-art travel time estimation models by 24.37% to 25.64% of mean absolute error (MAE) in multiple large-scale real-world datasets. 

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