Kenneth A. Norman - US grants

DESCRIPTION (provided by applicant): The goal of this research is to develop and test a mechanistically explicit theory of how the brain gives rise to episodic memory: our ability to recall previously experienced events, and to recognize events as having been experienced previously. Researchers have known for years that the hippocampus is the key neural substrate of recall; more recently, several studies have found that -- after focal hippocampal damage -- medial temporal lobe cortex (MTLC) can support some degree of spared recognition performance on its own. To explore how the contributions of hippocampus and MTLC differ, the research proposed here uses neural network models of these structures to simulate patterns of memory performance from normal and brain-damaged subjects. The first specific aim is to test the model's predictions regarding when item and associative recognition performance will be affected by knocking out the hippocampal contribution; "knockout" will be operationalized by using patients with focal hippocampal damage, and also normal subjects who are forced to respond quickly. Other experiments will test model predictions regarding how context change and interference manipulations affect recognition in the two systems. The second specific aim is to run simulations using a combined cortico-hippocampal model to address how the two processes conjointly determine recognition performance (e.g., in paradigms that place the two processes in opposition), and to run experiments in amnesic patients and controls to test these predictions. Extant formal models of recognition memory do not make contact with the underlying neurobiology; as such, this research constitutes a major step forward in recognition memory modeling. This link to neurobiology provides extra constraints that can be leveraged to gain new insights into fundamental puzzles in the memory literature. Regarding health benefits: The model's ability to address lesion data will bolster our understanding of what kinds of learning are spared after different kinds of brain damage, which (in turn) will help doctors develop more effective rehabilitation regimes. Furthermore, as researchers start to develop therapies that change the underlying parameters of learning in the brain, we will need some way of assessing how these changes scale up and affect behavioral memory performance; the research proposed here is ideally positioned to meet this growing need.

DESCRIPTION (provided by applicant): The overarching goal of this research is to understand neural learning rules: What are the circumstances that give rise to strengthening and weakening of memories? Our specific goal is to evaluate the hypothesis that learning is competition-dependent. According to this hypothesis, whenever neural representations compete to be active, the winning representations are strengthened, the losing representations are weakened, and learning is modulated by the margin of victory (i.e., closer competition yields more learning). The present research builds on previously funded work where biologically-based computational models were used to explore the neural mechanisms of competition- dependent learning. These simulations demonstrated that competition-dependent learning could be implemented in the brain by leveraging oscillations in the strength of neural inhibition; they also demonstrated that competition- dependent learning could account for detailed patterns of human memory data. The first specific aim of this research is to refine our computational model of competition-dependent learning. In particular, the simulations will focus on paradigms where there is close competition between the sought-after memory and other memories. In situations of this sort, the competition-dependent learning hypothesis predicts that learning effects will be highly volatile - small differences in memory excitation can affect the outcome of the competition, which (in turn) will affect which memories are strengthened and which are weakened. The computational model will be extended to include a more realistic form of inhibition, a more biophysically detailed hippocampal model, and a cognitive control system that allows the model to recall the sought-after memory even when it is weaker than competitors. These changes should improve our ability to predict behavioral forgetting effects and neural activity patterns in these high-volatility situations. The second specific aim of this research is to develop and utilize new experimental methods for testing hypotheses about competition-dependent learning. Testing the competition-dependent learning hypothesis is difficult because (according to the model) learning effects depend on the precise level of excitation of the competing memories. To address this problem, the proposed studies will use highly sensitive pattern classifier algorithms, applied to brain imaging data, to track the extent to which memories compete on a trial-by-trial basis. This neural readout of the competing memories can be used to test the model's predictions about how competition drives learning. The proposed studies will test the model's predictions about memory weakening in a perceptual priming paradigm, a short-term memory paradigm, and a long-term paired-associate learning paradigm. With regard to mental health: This research will improve our understanding of the circumstances that trigger strengthening and weakening of memories, and it will improve our ability to detect (based on neural activity) when strengthening or weakening will occur. These developments will help us to devise better methods for strengthening desirable memory associations and also methods for weakening maladaptive memories (e.g., when treating phobias or post-traumatic stress disorder).

The PIs and Co-PIs of grants supported through the NSF-NIH-BMBF Collaborative Research in Computational Neuroscience (CRCNS) program meet annually. This will be the seventh meeting of CRCNS investigators, and the first involving US-German projects supported by NSF/NIH and BMBF. The meeting brings together a broad spectrum of computational neuroscience researchers supported by the program, and includes poster presentations, talks and plenary lectures. The meeting is scheduled for October 9-11, 2011 and will be held at Princeton University.

This proposal is for the renewal of a predoctoral and postdoctoral Quantitative Neuroscience Training Program (QNTP) at Princeton University. Neuroscience research is becoming increasingly quantitative. Formal theoretical techniques are essential for understanding how complex, large-scale interactions between neurons give rise to thought and behavior, and advanced quantitative methods of data analysis are necessary for addressing the increasingly large, multidimensional data sets generated by modern brain imaging techniques (e.g., multiunit recording, fMRI). These methods are also necessary for future progress to be made in understanding, diagnosing, treating and, ultimately, curing brain disturbances that give rise to psychiatric disorders. Unfortunately, the mathematical and computational skills required to address these needs are not a focus of standard neuroscience curricula. Princeton's QNTP is designed to address this need, by providing the next generation of neuroscientists with the necessary mathematical and computational skills for measuring, analyzing, and modeling brain function. The establishment of the QNTP sparked several developments at Princeton, that (in turn) have accelerated the pace at which the goals of the QNTP are being met. By bringing Princeton's neuroscientists together with faculty in Physics, Mathematics, Computer Science and Engineering, the QNTP helped to spur the formation of the Princeton Neuroscience Institute (PNI) in 2005. The QNTP also helped to inspire the formation (in 2008) of PNI's free-standing PhD Program in Neuroscience, which strongly emphasizes classroom and laboratory training in basic quantitative and computational methods during its first two years. These developments have made it possible for us to refocus the QNTP from its original purpose (providing a foundation in quantitative neuroscience for trainees who are starting out in this area) to providing advanced training in quantitative neuroscience. Specifically, we will take the most quantitatively-focused subset of our predoctoral and postdoctoral trainees and provide them with the additional tools and training that they need to excel in computational neuroscience research. This training will be accomplished via advanced quantitative and computational neuroscience elective courses that were developed for the QNTP and are taught by leaders in the field, as well as participation in research seminars, journal clubs, and career development activities that are designed to deepen the trainees' knowledge and bolster community among the trainees. PNI faculty have made seminal contributions to quantitative neuroscience, ranging from information- theoretic analyses of neuronal spiking and dynamical systems analysis of decision-making to multivariate decoding of human neuroimaging data. The QNTP has been specifically formulated to bring predoctoral and postdoctoral trainees into contact with this expertise and, through this, to catalyze their transformation into full- fledged computational neuroscientists. As with the prior funding period, we are requesting support for four predoctoral trainees and four postdoctoral trainees (with 2 year appointments for each trainee).

PROJECT SUMMARY Our overarching goal is to understanding how stored memories change as a function of experience. The pro- posed work builds on prior research showing a U-shaped relationship between memory activation and learn- ing, whereby strong activation leads to synaptic strengthening, moderate activation leads to synaptic weaken- ing, and no activation leads to no change in synaptic strength. The present grant focuses on the implications of this U-shaped relationship for representational change: Learning is not just about making memories stronger or weaker?it can also decrease neural overlap between memories (differentiation) or increase neural overlap (integration). These neural changes can have profound effects on memory retrieval: Decreased overlap can reduce interference, at the cost of preventing generalization. Our specific goal is to construct and test a com- putational model of representational change and how it is shaped by competitive neural dynamics. When im- plemented in neural networks that are capable of self-organizing internal representations, our theory makes clear, novel predictions about when differentiation and integration will occur: Differentiation of memories A and B will occur when (i) B is moderately activated while processing A, causing weakening of connections between B and A, and (ii) B is reactivated later, allowing it to acquire new features that do not overlap with A; by con- trast, integration will occur if B is strongly activated during A, causing strengthening of connections between B and A. Aim 1 will use neural network simulations to address vexing puzzles in the literature and to generate novel empirical predictions. Aim 2 will test these predictions using behavioral and fMRI experiments focused on learning of new associations in the hippocampus, with a particular emphasis on testing the model's predic- tions about how competitive dynamics relate to representational change. Aim 3 will test the model's predictions regarding cortical plasticity, using a novel sketching task that induces competition between representations of familiar objects. Representational change will be assessed behaviorally in terms of how sketches and object recognition change over learning and neurally using fMRI of visual cortex; a deep neural network model of the ventral stream will be used to measure changes in the features of sketches. In summary: The proposed studies use multiple innovative approaches (fMRI pattern analysis, neural network modeling, free-form object sketching, and computer vision) to address the fundamental question of when experience causes neural repre- sentations to differentiate or integrate, thereby advancing our basic understanding of neuroplasticity. Improving our understanding of neural differentiation could have transformative implications for treating cognitive deficits in a wide range of clinical conditions, including stroke, dyslexia, and dementia. In all of these conditions, cogni- tive deficits can arise from insufficient separation of representations. This research may lead to better ways of re-differentiating these representations and?through this?ameliorating the associated cognitive deficits.

This project will break new ground in the study of memory by partnering with competitors in the USA Memory Championship. These competitors are not savants, but instead are well-practiced in the use of mnemonic techniques and, as a result, exhibit enhanced powers of memory on a range of real-world tasks, such as memorizing the items on a shopping list. All of these techniques rely on the practitioner structuring prior knowledge in very specific ways that facilitate the incorporation of new information. By scanning the brains of these trained memorists with functional magnetic resonance imaging (fMRI) and comparing their brain activity to participants who are learning these mnemonic systems for the first time, the researchers will identify principles for optimal scaffolding: How can prior knowledge be structured and used to most effectively support new learning? Identifying these principles will improve our fundamental understanding of real world-memory and will also lay the foundation for future educational interventions based on these principles. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

The goal of the project is to extend theories of memory to address how people can optimally use cognitive maps (structured prior knowledge) to support new learning. Reinforcement learning algorithms will be applied to computational models of memory to make predictions about which strategies will result in the best performance, factoring in biological constraints on the human memory system. Model predictions about optimal memory strategies will be tested using fMRI data from memory experts who have spent years optimizing their ability to bind arbitrary information to an internal cognitive map (a ?memory palace?), and who therefore serve as a unique comparison group for optimized memory models; these subjects will be compared to a sample of young adult subjects who are being trained to use these memorization techniques. New neuroimaging approaches developed by the researchers will allow them to map the brain patterns corresponding to each room of the memory palace and the patterns corresponding to each individual memory, and then track the activation of these patterns as subjects recall memories using mental walks through their palace. Results of these analyses will be used to test detailed model predictions about how memory training will alter the structure and use of subjects? cognitive maps, and how these changes relate to memory performance. As a final test of the models, the researchers will use neural measurements of individual subjects? cognitive maps to predict which specific items they will recall. By examining how prior knowledge is deployed to support learning in experts and novices at a much finer resolution than was previously possible, this work will provide the foundation for understanding why wide variations in memory performance exist across individuals and how memory can be improved, paving the way for targeted interventions to improve memory performance.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.