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

Some neural representations gradually 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.

A complementary learning systems model of how sleep moderates retrieval practice effects.

While many theories assume that sleep is critical in stabilizing and strengthening memories, our recent behavioral study (Liu & Ranganath, 2021, Psychonomic Bulletin & Review, 28[6], 2035-2044) suggests that sleep does not simply stabilize memories. Instead, it plays a more complex role, integrating information across two temporally distinct learning episodes. In the current study, we simulated the results of Liu and Ranganath (2021) using our biologically plausible computational model, TEACH, developed based on the complementary learning systems (CLS) framework.

A Neural Network Model of Continual Learning with Cognitive Control.

Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked.

Altered Associations Between Motivated Performance and Frontostriatal Functional Connectivity During Reward Anticipation in Schizophrenia.

Background And Hypothesis: The neuronal mechanisms that underlie deficits in effort cost computation in schizophrenia (SZ) are poorly understood. Given the role of frontostriatal circuits in valence-oriented motivation, we hypothesized that these circuits are either dysfunctional in SZ or do not appropriately predict behavior in SZ when task conditions are difficult and good performance is rewarded.

Study Design: A total of 52 people with recent onset SZ-spectrum disorders and 48 healthy controls (HCs) performed a 3T fMRI task with 2 valence conditions (rewarded vs neutral) and 2 difficulty conditions.

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.

The Structure of Systematicity in the Brain.

A hallmark of human intelligence is the ability to adapt to new situations, by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of inferences and actions (generativity). Here, we propose that the human brain accomplishes these feats through pathways in the parietal cortex that encode the abstract structure of space, events, and tasks, and pathways in the temporal cortex that encode information about specific people, places, and things (content). Recent neural network models show how the separation of structure and content might emerge through a combination of architectural biases and learning, and these networks show dramatic improvements in the ability to capture systematic, generative behavior.

Complementary Structure-Learning Neural Networks for Relational Reasoning.

The neural mechanisms supporting flexible relational inferences, especially in novel situations, are a major focus of current research. In the complementary learning systems framework, pattern separation in the hippocampus allows rapid learning in novel environments, while slower learning in neocortex accumulates small weight changes to extract systematic structure from well-learned environments. In this work, we adapt this framework to a task from a recent fMRI experiment where novel transitive inferences must be made according to implicit relational structure.

Deep Predictive Learning in Neocortex and Pulvinar.

How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism for the widely embraced idea that learning is driven by the differences between predictions and actual outcomes (i.e.

The Neural Correlates of Cued Reward Omission.

Compared to our understanding of positive prediction error signals occurring due to unexpected reward outcomes, less is known about the neural circuitry in humans that drives negative prediction errors during omission of expected rewards. While classical learning theories such as Rescorla-Wagner or temporal difference learning suggest that both types of prediction errors result from a simple subtraction, there has been recent evidence suggesting that different brain regions provide input to dopamine neurons which contributes to specific components of this prediction error computation. Here, we focus on the brain regions responding to negative prediction error signals, which has been well-established in animal studies to involve a distinct pathway through the lateral habenula.

A systems-neuroscience model of phasic dopamine.

We describe a neurobiologically informed computational model of phasic dopamine signaling to account for a wide range of findings, including many considered inconsistent with the simple reward prediction error (RPE) formalism. The central feature of this PVLV framework is a distinction between a primary value (PV) system for anticipating primary rewards (Unconditioned Stimuli [USs]), and a learned value (LV) system for learning about stimuli associated with such rewards (CSs). The LV system represents the amygdala, which drives phasic bursting in midbrain dopamine areas, while the PV system represents the ventral striatum, which drives shunting inhibition of dopamine for expected USs (via direct inhibitory projections) and phasic pausing for expected USs (via the lateral habenula).

Unraveling the Mysteries of Motivation.

Motivation plays a central role in human behavior and cognition but is not well captured by widely used artificial intelligence (AI) and computational modeling frameworks. This Opinion article addresses two central questions regarding the nature of motivation: what are the nature and dynamics of the internal goals that drive our motivational system and how can this system be sufficiently flexible to support our ability to rapidly adapt to novel situations, tasks, etc.? In reviewing existing systems and neuroscience research and theorizing on these questions, a wealth of insights to constrain the development of computational models of motivation can be found.

How Sequential Interactive Processing Within Frontostriatal Loops Supports a Continuum of Habitual to Controlled Processing.

We address the distinction between habitual/automatic vs. goal-directed/controlled behavior, from the perspective of a computational model of the frontostriatal loops. The model exhibits a continuum of behavior between these poles, as a function of the interactive dynamics among different functionally-specialized brain areas, operating iteratively over multiple sequential steps, and having multiple nested loops of similar decision making circuits.

Computational models of motivated frontal function.

Computational models of frontal function have made important contributions to understanding how the frontal lobes support a wide range of important functions, in their interactions with other brain areas including, critically, the basal ganglia (BG). We focus here on the specific case of how different frontal areas support goal-directed, motivated decision-making, by representing three essential types of information: possible plans of action (in more dorsal and lateral frontal areas), affectively significant outcomes of those action plans (in ventral, medial frontal areas including the orbital frontal cortex), and the overall utility of a given plan compared to other possible courses of action (in anterior cingulate cortex). Computational models of goal-directed action selection at multiple different levels of analysis provide insight into the nature of learning and processing in these areas and the relative contributions of the frontal cortex versus the BG.

Prediction error and somatosensory insula activation in women recovered from anorexia nervosa.

Background: Previous research in patients with anorexia nervosa showed heightened brain response during a taste reward conditioning task and heightened sensitivity to rewarding and punishing stimuli. Here we tested the hypothesis that individuals recovered from anorexia nervosa would also experience greater brain activation during this task as well as higher sensitivity to salient stimuli than controls.

Methods: Women recovered from restricting-type anorexia nervosa and healthy control women underwent fMRI during application of a prediction error taste reward learning paradigm.

Regional specialization within the human striatum for diverse psychological functions.

Decades of animal and human neuroimaging research have identified distinct, but overlapping, striatal zones, which are interconnected with separable corticostriatal circuits, and are crucial for the organization of functional systems. Despite continuous efforts to subdivide the human striatum based on anatomical and resting-state functional connectivity, characterizing the different psychological processes related to each zone remains a work in progress. Using an unbiased, data-driven approach, we analyzed large-scale coactivation data from 5,809 human imaging studies.

How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error.

We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error.

Latent structure in random sequences drives neural learning toward a rational bias.

People generally fail to produce random sequences by overusing alternating patterns and avoiding repeating ones-the gambler's fallacy bias. We can explain the neural basis of this bias in terms of a biologically motivated neural model that learns from errors in predicting what will happen next. Through mere exposure to random sequences over time, the model naturally develops a representation that is biased toward alternation, because of its sensitivity to some surprisingly rich statistical structure that emerges in these random sequences.

Proceedings From FDA/A.S.P.E.N. Public Workshop: Clinical Trial Design for Intravenous Fat Emulsion Products, October 29, 2013.

The development of intravenous fat emulsion (IVFE) is the culmination of physiological, biochemical, nutritional, and medical scientific advancements. IVFEs have the ability to deliver critical nutritional substrates to the patient. Recent literature purports that they may also play roles in modulation of immune functionality and pulmonary physiology, but data supporting these potential benefits are limited.

Thalamic pathways underlying prefrontal cortex-medial temporal lobe oscillatory interactions.

As focus shifts to large-scale network interactions involved in memory, it is becoming increasingly clear that oscillatory dynamics are critically involved. A number of studies have shown a negative correlation between memory retrieval in alpha and beta power, and a positive correlation between retrieval and theta power. In this opinion article, we suggest three thalamic sub-regions responsible for the coordination of oscillatory activity and the facilitation of memory processes.

A continuous-time neural model for sequential action.

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g.

Early recurrent feedback facilitates visual object recognition under challenging conditions.

Standard models of the visual object recognition pathway hold that a largely feedforward process from the retina through inferotemporal cortex leads to object identification. A subsequent feedback process originating in frontoparietal areas through reciprocal connections to striate cortex provides attentional support to salient or behaviorally-relevant features. Here, we review mounting evidence that feedback signals also originate within extrastriate regions and begin during the initial feedforward process.

A neural network model of individual differences in task switching abilities.

We use a biologically grounded neural network model to investigate the brain mechanisms underlying individual differences specific to the selection and instantiation of representations that exert cognitive control in task switching. Existing computational models of task switching do not focus on individual differences and so cannot explain why task switching abilities are separable from other executive function (EF) abilities (such as response inhibition). We explore hypotheses regarding neural mechanisms underlying the "Shifting-Specific" and "Common EF" components of EF proposed in the Unity/Diversity model (Miyake & Friedman, 2012) and similar components in related theoretical frameworks.

Indirection and symbol-like processing in the prefrontal cortex and basal ganglia.

The ability to flexibly, rapidly, and accurately perform novel tasks is a hallmark of human behavior. In our everyday lives we are often faced with arbitrary instructions that we must understand and follow, and we are able to do so with remarkable ease. It has frequently been argued that this ability relies on symbol processing, which depends critically on the ability to represent variables and bind them to arbitrary values.

Strategic cognitive sequencing: a computational cognitive neuroscience approach.

We address strategic cognitive sequencing, the "outer loop" of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models.