Christopher I. Connolly

Bio: Christopher I. Connolly is an academic researcher from SRI International. The author has contributed to research in topics: Anomaly detection & Cellular network. The author has an hindex of 7, co-authored 18 publications receiving 988 citations.

Building neural representations of habits.

Abstract: Memories for habits and skills ("implicit or procedural memory") and memories for facts ("explicit or episodic memory") are built up in different brain systems and are vulnerable to different neurodegenerative disorders in humans. So that the striatum-based mechanisms underlying habit formation could be studied, chronic recordings from ensembles of striatal neurons were made with multiple tetrodes as rats learned a T-maze procedural task. Large and widely distributed changes in the neuronal activity patterns occurred in the sensorimotor striatum during behavioral acquisition, culminating in task-related activity emphasizing the beginning and end of the automatized procedure. The new ensemble patterns remained stable during weeks of subsequent performance of the same task. These results suggest that the encoding of action in the sensorimotor striatum undergoes dynamic reorganization as habit learning proceeds.

Anomaly Detection and Diagnosis for Automatic Radio Network Verification

Abstract: The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.

An Integrated Feasibility Demonstration for Automatic Population of Geospatial Databases

Abstract: In this Automatic Population of GeospatialDatabases (APGD) overview paper, we describeour progress in developing and evaluating a genericcontext-based architecture (which we call the BOSfor the Battlespace Observer System) and algorith-mic techniques to radically reduce the human effortrequired to extract cartographic features, especiallyroads and buildings, from aerial and satelliteimagery. We also describe progress in supportingthe DARPA APGD community by constructingverified and well-documented datasets and evalu-ation procedures to allow interested researchers toexperimentally evaluate their extraction techniquesin a common framework. Our APGD Communityoriented activities are accessible at SRI’s APGDweb site, http://www.ai.sri.com/~apgd,and the APGD “Virtual Laboratory,”http://www.ai.sri.com/~apgd/vl.In a formal April1998presentation,we successfullydemonstrated the “end-to-end” extraction of all the This work was sponsored by the Defense Advanced Re-search Projects Agency under contract NMA100-97-C-1004monitored by the National Imagery and Mapping Agency, Re-ston, VA. The views and conclusions contained in this docu-ment are those of the author and should not be interpreted asrepresenting the official policies, either expressed or implied,of the DefenseAdvancedResearchProjects Agency,the UnitedStates Government, or SRI International.

Managing scope changes for cellular network-level anomaly detection

Abstract: The Self-Organizing Networks (SON) concept is increasingly being used as an approach for managing complex, dynamic mobile radio networks. In this paper we focus on the verification component of SON, which is the ability to automatically detect problems such as performance degradation or network instability stemming from configuration management changes. In previous work, we have shown how Key Performance Indicators (KPIs) that are continuously collected from network cells can be used in an anomaly detection framework to characterize the state of the network. In this study, we introduce new methods designed to handle scope changes. Such changes can include the addition of new KPIs or cells in the network, or even re-scoping the analysis from the level of a cell or group of cells to the network level. Our results, generated using real cellular network data, suggest that the proposed network-level anomaly detection can adapt to such changes in scope and accurately identify different network states based on all types of available KPIs.

The Neural Basis of Addiction: A Pathology of Motivation and Choice

Abstract: Objective: A primary behavioral pathology in drug addiction is the overpowering motivational strength and decreased ability to control the desire to obtain drugs. In this review the authors explore how advances in neurobiology are approaching an understanding of the cellular and circuitry underpinnings of addiction, and they describe the novel pharmacotherapeutic targets emerging from this understanding. Method: Findings from neuroimaging of addicts are integrated with cellular studies in animal models of drug seeking.

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

Abstract: A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.

Large-scale recording of neuronal ensembles

Abstract: How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron–electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

Place Cells, Grid Cells, and the Brain's Spatial Representation System

Abstract: More than three decades of research have demonstrated a role for hippocampal place cells in representation of the spatial environment in the brain. New studies have shown that place cells are part of a broader circuit for dynamic representation of self-location. A key component of this network is the entorhinal grid cells, which, by virtue of their tessellating firing fields, may provide the elements of a path integration-based neural map. Here we review how place cells and grid cells may form the basis for quantitative spatiotemporal representation of places, routes, and associated experiences during behavior and in memory. Because these cell types have some of the most conspicuous behavioral correlates among neurons in nonsensory cortical systems, and because their spatial firing structure reflects computations internally in the system, studies of entorhinal-hippocampal representations may offer considerable insight into general principles of cortical network dynamics.

Learning and memory functions of the Basal Ganglia.

Abstract: Although the mammalian basal ganglia have long been implicated in motor behavior, it is generally recognized that the behavioral functions of this subcortical group of structures are not exclusively motoric in nature. Extensive evidence now indicates a role for the basal ganglia, in particular the dorsal striatum, in learning and memory. One prominent hypothesis is that this brain region mediates a form of learning in which stimulus-response (S-R) associations or habits are incrementally acquired. Support for this hypothesis is provided by numerous neurobehavioral studies in different mammalian species, including rats, monkeys, and humans. In rats and monkeys, localized brain lesion and pharmacological approaches have been used to examine the role of the basal ganglia in S-R learning. In humans, study of patients with neurodegenerative diseases that compromise the basal ganglia, as well as research using brain neuroimaging techniques, also provide evidence of a role for the basal ganglia in habit learning. Several of these studies have dissociated the role of the basal ganglia in S-R learning from those of a cognitive or declarative medial temporal lobe memory system that includes the hippocampus as a primary component. Evidence suggests that during learning, basal ganglia and medial temporal lobe memory systems are activated simultaneously and that in some learning situations competitive interference exists between these two systems.