Dynamics Of Memory Retrieval Research Articles

Forgetting Leads to Chaos in Attractor Networks

Attractor networks are an influential theory for memory storage in brain systems. This theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. In this work, we study a sparsely connected attractor network where memories are learned according to a Hebbian synaptic plasticity rule. After recapitulating known results for the continuous, sparsely connected Hopfield model, we investigate a model in which new memories are learned continuously and old memories are forgotten, using an online synaptic plasticity rule. We show that for a forgetting timescale that optimizes storage capacity, the qualitative features of the network’s memory retrieval dynamics are age dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations. Therefore, fixed-point and chaotic attractors coexist in the network phase space. The network presents a continuum of statistically distinguishable memory states, where chaotic fluctuations appear abruptly above a critical age and then increase gradually until the memory disappears. We develop a dynamical mean field theory to analyze the age-dependent dynamics and compare the theory with simulations of large networks. We compute the optimal forgetting timescale for which the number of stored memories is maximized. We found that the maximum age at which memories can be retrieved is given by an instability at which old memories destabilize and the network converges instead to a more recent one. Our numerical simulations show that a high degree of sparsity is necessary for the dynamical mean field theory to accurately predict the network capacity. To test the robustness and biological plausibility of our results, we study numerically the dynamics of a network with learning rules and transfer function inferred from in vivo data in the online learning scenario. We found that all aspects of the network’s dynamics characterized analytically in the simpler model also hold in this model. These results are highly robust to noise. Finally, our theory provides specific predictions for delay response tasks with aging memoranda. In particular, it predicts a higher degree of temporal fluctuations in retrieval states associated with older memories, and it also predicts fluctuations should be faster in older memories. Overall, our theory of attractor networks that continuously learn new information at the price of forgetting old memories can account for the observed diversity of retrieval states in the cortex, and in particular, the strong temporal fluctuations of cortical activity.1 MoreReceived 3 December 2021Revised 31 August 2022Accepted 6 December 2022DOI:https://doi.org/10.1103/PhysRevX.13.011009Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasLearningMemoryNeuronal networksPhysical SystemsArtificial neural networksTechniquesDynamical mean field theoryInterdisciplinary PhysicsNetworksStatistical PhysicsNonlinear DynamicsBiological Physics

Memory retrieval dynamics and storage capacity of a modular network model of association cortex with featural decomposition

The primate heteromodal cortex presents an evident functional modularity at a mesoscopic level, with physiological and anatomical evidence pointing to it as likely substrate of long-term memory. In order to investigate some of its properties, a model of multimodular autoassociator is studied. Each of the many modules represents a neocortical functional ensemble of recurrently connected neurons and operates as a Hebbian autoassociator, storing a number of local features which it can recall upon cue. The global memory patterns are made of combinations of features sparsely distributed across the modules. Intermodular connections are modelled as a finite-connectivity random graph. Any pair of features in any respective pair of modules is allowed to be involved in several memory patterns; the coarse-grained modular network dynamics is defined in such a way as to overcome the consequent ambiguity of associations. Effects of long-range homeostatic synaptic scaling on network performance are also assessed.The dynamical process of cued retrieval almost saturates a natural upper bound while producing negligible spurious activation. The extent of finite-size effects on storage capacity is quantitatively evaluated. In the limit of infinite size, the functional relationship between storage capacity and number of features per module reduces to that which other authors found by methods from equilibrium statistical mechanics, which suggests that the origin of the functional form is of a combinatorial nature. In contrast with its apparent inevitability at intramodular level, long-range synaptic scaling results to be of minor relevance to both retrieval and storage capacity, casting doubt on its existence in the neocortex. A conjecture is also posited about how statistical fluctuation of connectivity across the network may underpin spontaneous emergence of semantic hierarchies through learning.

Towards a Cognitive Theory of Cyber Deception.

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present-day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision-making when paired against the defense algorithm. We developed an instance-based learning (IBL) model of an attacker using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture to investigate how humans make decisions under deception in cyber-attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality.

The dynamics of memory retrieval in hierarchical networks.

Memory retrieval is of central importance to a wide variety of brain functions. To understand the dynamic nature of memory retrieval and its underlying neurophysiological mechanisms, we develop a biologically plausible spiking neural circuit model, and demonstrate that free memory retrieval of sequences of events naturally arises from the model under the condition of excitation-inhibition (E/I) balance. Using the mean-field model of the spiking circuit, we gain further theoretical insights into how such memory retrieval emerges. We show that the spiking neural circuit model quantitatively reproduces several salient features of free memory retrieval, including its semantic proximity effect and log-normal distributions of inter-retrieval intervals. In addition, we demonstrate that our model can serve as a platform to examine memory retrieval deficits observed in neuropsychiatric diseases such as Parkinson's and Alzheimer's diseases. Furthermore, our model allows us to make novel and experimentally testable predictions, such as the prediction that there are long-range correlations in the sequences of retrieved items.

Impact of aging on the dynamics of memory retrieval: A time-course analysis

The response-signal speed–accuracy trade-off (SAT) procedure was used to provide an in-depth investigation of the impact of aging on the dynamics of short-term memory retrieval. Young and older adults studied sequentially presented 3-item lists, immediately followed by a recognition probe. Analyses of composite list and serial position SAT functions found no differences in overall accuracy, but indicated slower retrieval speed for older adults. Analysis of false alarms to recent negatives (lures from the previous study list) revealed no differences in the timing or magnitude of early false alarms that are thought to reflect familiarity-based judgments. However, onset and accrual of recollective processing required for resolving interference was slower for older adults. These findings suggest that older adults have a selective impairment on controlled and recollective retrieval operations, and further specify this impairment to arise primarily from delayed onset of cognitive control potentially coupled with reduced availability of recollective information.

The dynamics of memory retrieval in older adulthood.

One of the most robust findings in cognitive aging is that of a significant decline in self-initiated recall from episodic memory. In laboratory studies this deficit can be seen in significant age differences in word-list free recall. In this article, we focus on free recall of categorized word lists where one observes "response bursting" in the form of a rapid output of within-category items with longer delays between categories. Age differences appear primarily in between-category latencies, results that are consistent with a relative sparing of semantic memory combined with an age-deficit in episodic retrieval. When adjusted for differences in overall mnemonic ability, we demonstrate that the relationship between organization and learning remains invariant with normal aging. We argue that the locus of the age deficit in free recall lies at the level of temporal coding of items and the use of temporal associations to guide recall.

Retrieval dynamics and brain mechanisms

Experiments on memory-retrieval dynamics support the hypothesis that different mechanisms mediate processes of familiarity and recall (recollection). The minimal retrieval time for accurate recognition and frequency judgments is about 100 msec earlier than the minimal time to judge details such as modality or location. The difference is consistent with neurophysiological evidence. Retrieval dynamics data are also relevant to other aspects of Aggleton & Brown's target article.