Prediction and Topological Models in Neuroscience

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone

Hardware Support for Efficient Packet Processing

Scalability is the key ingredient to further increase the performance of today’s supercomputers. As other approaches like frequency scaling reach their limits, parallelization is the only feasible way to further improve the performance. The time required for communication needs to be kept as small as possible to increase the scalability, in order to be able to further parallelize such systems. In the first part of this thesis ways to reduce the inflicted latency in packet based interconnection networks are analyzed and several new architectural solutions are proposed to solve these issues. These solutions have been tested and proven in a field programmable gate array (FPGA) environment. In addition, a hardware (HW) structure is presented that enables low latency packet processing for financial markets. The second part and the main contribution of this thesis is the newly designed crossbar architecture. It introduces a novel way to integrate the ability to multicast in a crossbar design. Furthermore, an efficient implementation of adaptive routing to reduce the congestion vulnerability in packet based interconnection networks is shown. The low latency of the design is demonstrated through simulation and its scalability is proven with synthesis results. The third part concentrates on the improvements and modifications made to EXTOLL, a high performance interconnection network specifically designed for low latency and high throughput applications. Contributions are modules enabling an efficient integration of multiple host interfaces as well as the integration of the on-chip interconnect. Additionally, some of the already existing functionality has been revised and improved to reach better performance and a lower latency. Micro-benchmark results are presented to underline the contribution of the made modifications

Multivariate pattern analysis and the search for neural representations

Multivariate pattern analysis, or MVPA, has become one of the most popular analytic methods in cognitive neuroscience. Since its inception, MVPA has been heralded as offering much more than regular univariate analyses, for—we are told—it not only can tell us which brain regions are engaged while processing particular stimuli, but also which patterns of neural activity represent the categories the stimuli are selected from. We disagree, and in the current paper we offer four conceptual challenges to the use of MVPA to make claims about neural representation. Our view is that the use of MVPA to make claims about neural representation is problematic

A key role for stimulus-specific updating of the sensory cortices in the learning of stimulus-reward associations

Successful adaptive behavior requires the learning of associations between stimulus-specific choices and rewarding outcomes. Most research on the mechanisms underlying such processes has focused on subcortical reward-processing regions, in conjunction with frontal circuits. Given the extensive stimulus-specific coding in the sensory cortices, we hypothesized they would play a key role in the learning of stimulus-specific reward associations. We recorded electrical brain activity (EEG) during a learning-based, decision-making, gambling task where, on each trial, participants chose between a face and a house and then received feedback (gain or loss). Within each 20-trial set, either faces or houses were more likely to predict a gain. Results showed that early feedback processing (~200-1200ms) was independent of the choice made. In contrast, later feedback processing (~1400-1800ms) was stimulus-specific, reflected by decreased alpha power (reflecting increased cortical activity) over face-selective regions. For winning-versus-losing after a face choice, but not after a house choice. Finally, as the reward association was learned in a set, there was increasingly stronger attentional bias towards the more likely winning stimulus, reflected by increasing attentional-orienting-related brain activity and increasing likelihood of choosing that stimulus. These results delineate the processes underlying the updating of stimulus-reward associations during feedback-guided learning, which then guides future attentional allocation and decision making

Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories

† The first two authors contributed equally to this work. Abstract A common approach in memory research is to isolate the function(s) of individual brain regions, such as the hippocampus, without addressing how those regions interact with the larger network. To investigate the properties of the hippocampus embedded within large-scale networks, we used functional magnetic resonance imaging and graph theory to characterize complex hippocampal interactions during the active retrieval of vivid versus dim visual memories. The study yielded 4 main findings. First, the right hippocampus displayed greater communication efficiency with the network (shorter path length) and became a more convergent structure for information integration (higher centrality measures) for vivid than dim memories. Second, vivid minus dim differences in our graph theory measures of interest were greater in magnitude for the right hippocampus than for any other region in the 90-region network. Moreover, the right hippocampus significantly reorganized its set of direct connections from dim to vivid memory retrieval. Finally, beyond the hippocampus, communication throughout the whole-brain network was more efficient (shorter global path length) for vivid than dim memories. In sum, our findings illustrate how multivariate network analyses can be used to investigate the roles of specific regions within the large-scale network, while also accounting for global network changes

The visual and semantic features that predict object memory: Concept property norms for 1,000 object images

Abstract: Humans have a remarkable fidelity for visual long-term memory, and yet the composition of these memories is a longstanding debate in cognitive psychology. While much of the work on long-term memory has focused on processes associated with successful encoding and retrieval, more recent work on visual object recognition has developed a focus on the memorability of specific visual stimuli. Such work is engendering a view of object representation as a hierarchical movement from low-level visual representations to higher level categorical organization of conceptual representations. However, studies on object recognition often fail to account for how these high- and low-level features interact to promote distinct forms of memory. Here, we use both visual and semantic factors to investigate their relative contributions to two different forms of memory of everyday objects. We first collected normative visual and semantic feature information on 1,000 object images. We then conducted a memory study where we presented these same images during encoding (picture target) on Day 1, and then either a Lexical (lexical cue) or Visual (picture cue) memory test on Day 2. Our findings indicate that: (1) higher level visual factors (via DNNs) and semantic factors (via feature-based statistics) make independent contributions to object memory, (2) semantic information contributes to both true and false memory performance, and (3) factors that predict object memory depend on the type of memory being tested. These findings help to provide a more complete picture of what factors influence object memorability. These data are available online upon publication as a public resource

A Multimodal Analysis of Antipsychotic Effects on Brain Structure and Function in First-Episode Schizophrenia

ImportanceRecent data suggest that treatment with antipsychotics is associated with reductions in cortical gray matter in patients with schizophrenia. These findings have led to concerns about the effect of antipsychotic treatment on brain structure and function; however, no studies to date have measured cortical function directly in individuals with schizophrenia and shown antipsychotic-related reductions of gray matter.ObjectiveTo examine the effects of antipsychotics on brain structure and function in patients with first-episode schizophrenia, using cortical thickness measurements and administration of the AX version of the Continuous Performance Task (AX-CPT) during event-related functional magnetic resonance imaging.Design, setting, and participantsThis case-control cross-sectional study was conducted at the Imaging Research Center of the University of California, Davis, from November 2004 through July 2012. Participants were recruited on admission into the Early Diagnosis and Preventive Treatment Clinic, an outpatient clinic specializing in first-episode psychosis. Patients with first-episode schizophrenia who received atypical antipsychotics (medicated patient group) (n = 23) and those who received no antipsychotics (unmedicated patient group) (n = 22) and healthy control participants (n = 37) underwent functional magnetic resonance imaging using a 1.5-T scanner.Main outcomes and measuresBehavioral performance was measured by trial accuracy, reaction time, and d'-context score. Voxelwise statistical parametric maps tested differences in functional activity during the AX-CPT, and vertexwise maps of cortical thickness tested differences in cortical thickness across the whole brain.ResultsSignificant cortical thinning was identified in the medicated patient group relative to the control group in prefrontal (mean reduction [MR], 0.27 mm; P < .001), temporal (MR, 0.34 mm; P = .02), parietal (MR, 0.21 mm; P = .001), and occipital (MR, 0.24 mm; P = .001) cortices. The unmedicated patient group showed no significant cortical thickness differences from the control group after clusterwise correction. The medicated patient group showed thinner cortex compared with the unmedicated patient group in the dorsolateral prefrontal cortex (DLPFC) (MR, 0.26 mm; P = .001) and temporal cortex (MR, 0.33 mm; P = .047). During the AX-CPT, both patient groups showed reduced DLPFC activity compared with the control group (P = .02 compared with the medicated group and P < .001 compared with the unmedicated group). However, the medicated patient group demonstrated higher DLPFC activation (P = .02) and better behavioral performance (P = .02) than the unmedicated patient group.Conclusions and relevanceThese findings highlight the complex relationship between antipsychotic treatment and the structural, functional, and behavioral deficits repeatedly identified in schizophrenia. Although short-term treatment with antipsychotics was associated with prefrontal cortical thinning, treatment was also associated with better cognitive control and increased prefrontal functional activity. This study adds important context to the growing literature on the effects of antipsychotics on the brain and suggests caution in interpreting neuroanatomical changes as being related to a potentially adverse effect on brain function