Gradual Translocation of Spatial Correlates of Neuronal Firing in the Hippocampus toward Prospective Reward Locations

SummaryIn a continuous T-maze alternation task, CA1 complex-spike neurons in the hippocampus differentially fire as the rat traverses overlapping segments of the maze (i.e., the stem) repeatedly via alternate routes. The temporal dynamics of this phenomenon were further investigated in the current study. Rats learned the alternation task from the first day of acquisition and the differential firing pattern in the stem was observed accordingly. More importantly, we report a phenomenon in which spatial correlates of CA1 neuronal ensembles gradually changed from their original firing locations, shifting toward prospective goal locations in the continuous T-maze alternation task. The relative locations of simultaneously recorded firing fields, however, were preserved within the ensemble spatial representation during this shifting. The within-session shifts in preferred firing locations in the absence of any changes in the environment suggest that certain cognitive factors can significantly alter the location-bound coding scheme of hippocampal neurons

Integrating Spatial Working Memory and Remote Memory: Interactions between the Medial Prefrontal Cortex and Hippocampus

In recent years, two separate research streams have focused on information sharing between the medial prefrontal cortex (mPFC) and hippocampus (HC). Research into spatial working memory has shown that successful execution of many types of behaviors requires synchronous activity in the theta range between the mPFC and HC, whereas studies of memory consolidation have shown that shifts in area dependency may be temporally modulated. While the nature of information that is being communicated is still unclear, spatial working memory and remote memory recall is reliant on interactions between these two areas. This review will present recent evidence that shows that these two processes are not as separate as they first appeared. We will also present a novel conceptualization of the nature of the medial prefrontal representation and how this might help explain this area’s role in spatial working memory and remote memory recall

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of spatial navigation, examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL. Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location, revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics. Rather than localization, the key problem in learning is generalization between past and present situations, which may not match perfectly. Accordingly, although neural populations collectively offer a precise representation of position, our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons, which allows learning about rewards to generalize over longer distances given fewer training experiences. However, work on generalization in RL suggests the underlying representation should respect the environment's layout. In particular, although it is often assumed that neurons track location in Euclidean coordinates (that a place cell's activity declines “as the crow flies” away from its peak), the relevant metric for value is geodesic: the distance along a path, around any obstacles. We formalize this intuition and present simulations showing how Euclidean, but not geodesic, representations can interfere with RL by generalizing inappropriately across barriers. Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields, which provides a new viewpoint on data concerning both spatial codes

Where Does TAM Reside in the Brain? The Neural Mechanisms Underlying Technology Adoption

Toward materializing the recently identified potential of cognitive neuroscience for IS research (Dimoka, Pavlou and Davis 2007), this paper demonstrates how functional neuroimaging tools can enhance our understanding of IS theories. Specifically, this study aims to uncover the neural mechanisms that underlie technology adoption by identifying the brain areas activated when users interact with websites that differ on their level of usefulness and ease of use. Besides localizing the neural correlates of the TAM constructs, this study helps understand their nature and dimensionality, as well as uncover hidden processes associated with intentions to use a system. The study also identifies certain technological antecedents of the TAM constructs, and shows that the brain activations associated with perceived usefulness and perceived ease of use predict selfreported intentions to use a system. The paper concludes by discussing the study’s implications for underscoring the potential of functional neuroimaging for IS research and the TAM literature

A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics

Neuro IS: The Potential of Cognitive Neuroscience for Information Systems Research

This paper proposes the idea of applying cognitive neuroscience theories, methods, and tools in Information Systems (IS) research (termed “Neuro-IS”), and introduces a research agenda for exploring the potential of cognitive neuroscience for IS research. Recent cognitive neuroscience discoveries have clarified the neural bases of human psychological processes and behavior and provided insights that may advance progress on core IS research questions on designing and deploying IT tools, technology adoption and use, e-commerce, virtual teams, human-computer interaction, decision making, and information sharing in organizational and market environments, among others. Functional neuroimaging techniques (fMRI, PET, EEG, MEG) have led to a better understanding of the brain areas and structures involved when people make decisions, deal with risk, uncertainty, and ambiguity, respond to rewards and social influences, trust and distrust, cooperate and compete, and acquire and process information. Similar to economics, marketing, and psychology, IS research may also benefit from integrating some of these new discoveries in cognitive neuroscience into IS theories about how IT supports human processes. Moreover, the use of neuroimaging techniques in IS research could complement traditional methods and data, such as self-report surveys, interviews, lab and field experiments, and archival data to integrate new objective sources of brain data for IS theory development and testing. This paper provides several examples of potentially fertile intersections of cognitive neuroscience and IS research on such areas as technology adoption and use, e-commerce, and group support systems. The paper also overviews today’s functional neuroimaging tools and gives guidelines on how IS researchers can utilize these tools to obtain additional new insights into IS phenomena. Finally, it discusses the implications of incorporating cognitive neuroscience theories and functional neuroimaging tools in neuro-IS research, aiming to enhance the diversity of theories, methods, tools, and data in the portfolio of IS researchers

Impact of Acute Ethanol Injections on Medial Prefrontal Cortex Neural Activity

Indiana University-Purdue University Indianapolis (IUPUI)The medial prefrontal cortex (mPFC) is a cortical brain region involved in the evaluation and selection of motivationally relevant outcomes. mPFC-mediated cognitive functions are impaired following acute alcohol exposure. In rodent models, ethanol (EtOH) doses as low as 0.75 g/kg yield deficits in cognitive functions. These deficits following acute EtOH are thought to be mediated, at least in part, by decreases in mPFC firing rates. However, these data have been generated exclusively in anesthetized rodents. To eliminate the potentially confounding role of anesthesia on EtOH modulated mPFC activity, the present study investigated the effects of acute EtOH injections on mPFC neural activity in awake-behaving rodents. We utilized three groups: the first group received 2 saline injections during the recording. The second group received a saline injection followed 30 minutes later by a 1.0 g/kg EtOH injection. The last group received a saline injection followed 30 minutes later by a 2.0 g/kg EtOH injection. One week following the awake-behaving recording, an anesthetized recording was performed using one dose of saline followed 30 minutes later by one dose of 1.0 g/kg EtOH in order to replicate previous studies. Firing rates were normalized to a baseline period that occurred 5 minutes prior to each injection. A 5-minute time period 30 minutes following the injection was used to compare across groups. There were no significant differences across the awake-behaving saline-saline group, indicating no major effect on mPFC neural activity as a result of repeated injections. There was a significant main effect across treatment & behavioral groups in the saline-EtOH 1.0 g/kg group with reductions in the EtOH & Sleep condition. In the saline-EtOH 2.0 g/kg, mPFC neural activity was only reduced in lowered states of vigilance. This suggests that EtOH only causes gross changes on neural activity when the animal is not active and behaving. Ultimately this means that EtOH’s impact on decision making is not due to gross changes in mPFC neural activity and future work should investigate its mechanism

Goal-Directed Decision Making with Spiking Neurons.

UNLABELLED: Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT: Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level.This research was supported by the Swiss National Science Foundation (J.F., Grant PBBEP3 146112) and the Wellcome Trust (J.F. and M.L.).This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Society for Neuroscience

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates