Jason G. Fleischer

Spatial navigation and causal analysis in a brain-based device modeling cortical-hippocampal interactions.

We describe Darwin X, a physical device that interacts with a real environment, whose behavior is guided by a simulated nervous system incorporating aspects of the detailed anatomy and physiology of the hippocampus and its surrounding regions. This brain-based device integrates cues from its environment and solves a spatial memory task. The responses of simulated neuronal units in the hippocampal areas during its exploratory behavior are comparable to place cells in the rodent hippocampus and emerged by associating sensory cues during exploration. To identify different functional hippocampal pathways and their influence on behavior, we employed a time series analysis that distinguishes causal interactions within and between simulated hippocampal and neocortical regions while the device is engaged in a spatial memory task. Our analysis identified different functional pathways within the neural simulation and prompts novel predictions about the influence of the perforant path, the trisynaptic loop and hippocampal-cortical interactions on place cell activity and behavior during navigation. Moreover, this causal time series analysis may be useful in analyzing networks in general.

Retrospective and prospective responses arising in a modeled hippocampus during maze navigation by a brain-based device

Recent recordings of place field activity in rodent hippocampus have revealed correlates of current, recent past, and imminent future events in spatial memory tasks. To analyze these properties, we used a brain-based device, Darwin XI, that incorporated a detailed model of medial temporal structures shaped by experience-dependent synaptic activity. Darwin XI was tested on a plus maze in which it approached a goal arm from different start arms. In the task, a journey corresponded to the route from a particular starting point to a particular goal. During maze navigation, the device developed place-dependent responses in its simulated hippocampus. Journey-dependent place fields, whose activity differed in different journeys through the same maze arm, were found in the recordings of simulated CA1 neuronal units. We also found an approximately equal number of journey-independent place fields. The journey-dependent responses were either retrospective, where activity was present in the goal arm, or prospective, where activity was present in the start arm. Detailed analysis of network dynamics of the neural simulation during behavior revealed that many different neural pathways could stimulate any single CA1 unit. That analysis also revealed that place activity was driven more by hippocampal and entorhinal cortical influences than by sensory cortical input. Moreover, journey-dependent activity was driven more strongly by hippocampal influence than journey-independent activity.

Sensory integration and remapping in a model of the medial temporal lobe during maze navigation by a brain-based device.

Information from many different sensory modalities converges on the medial temporal lobe in the mammalian brain, an area that is known to be involved in the formation of episodic memories. Neurons in this region, called place cells, display location-correlated activity. Because it is not feasible to record all neurons using current electrophysiological techniques, it is difficult to address the mechanisms by which different sensory modalities are combined to form place field activity. To address this limitation, this paper presents an embodied neural simulation of the medial temporal lobe and other cortical structures, in which all aspects of the model can be examined during a maze navigation task. The neural simulation has realistic neuroanatomical connectivity. It uses a rate code model where a single neuronal unit represents the local field potential of a pool of neurons. The dynamics of these neuronal units are based on measured neurophysiological parameters. The model is embodied in a mobile device with multiple sensory modalities. Neural activity and behavior are analyzed both in the normal condition and after sensory lesions. Place field activity arose in the model through plasticity, and it continued even when one or more sensory modalities were lesioned. An analysis that traced through all neural circuits in the model revealed that many different pathways led to the same place activity, i.e., these pathways were degenerate. After sensory lesions, the pathways leading to place activity had even greater degeneracy, but more of this variance occurred in entorhinal cortex and sensory areas than in hippocampus. This model predicts that when examining neurons causing place activity in rodents, hippocampal neurons are more likely than entorhinal or sensory neurons to maintain involvement in the circuit after sensory deprivation.

Neural Correlates of Anticipation in Cerebellum, Basal Ganglia, and Hippocampus

Animals anticipate the future in a variety of ways. For instance: (a) they make motor actions that are timed to a reference stimulus and motor actions that anticipate future movement dynamics; (b) they learn to make choices that will maximize reward they receive in the future; and (c) they form memories of behavioral episodes such that the animals future actions can be predicted by current neural activity associated with those memories. Although these effects are clearly observable at the behavioral level, research into the mechanisms of such anticipatory learning are still largely in the early stages. This review, intended for those who have a computational background and are less familiar with neuroscience, addresses neural mechanisms found in the mammalian cerebellum, basal ganglia, and the hippocampus that give rise to such adaptive anticipatory behavior.

A neurally controlled robot competes and cooperates with humans in Segway soccer

A new RoboCup soccer league is being developed, focusing on human-robot interaction. In this league each team consists of both a human player, mounted on a Segway HT scooter, and a robotic version of the Segway; both human and robot players must cooperate to score goals. This paper details the design of our robotic Segway soccer brain-based device (SS-BBD). The SS-BBD control system is based on a large scale neural simulation, whose design is dictated by details from the published literature on vertebrate neuroanatomy, neurophysiology, and psychophysics. The physical device is completely autonomous, and possesses special manipulators for kicking and capturing a full-sized soccer ball. The SS-BBD uses visual and laser rangefinder information to recognize a variety of game related objects, which enables it to perform actions such as capturing the ball, kicking the ball to another player, shooting a goal, and maneuvering safely across the field. The SS-BBD can act autonomously or obey voice commands from the human player. This is an unprecedented level of human-robot teamwork on a soccer field, in that our players are not merely acting autonomously, but also communicate with each other and support each other on the field

Brain-based devices

This paper presents an embodied approach to linking nervous system structure and function to behavior.

Dopamine signaling and the distal reward problem

Actions and their associated consequences, such as reward attainment, are often temporally distant. Animals nevertheless learn such associations thereby solving the ‘distal reward’ problem. We sought to determine whether dopamine signaling plays a role in such learning. Wild-type and dopamine type I receptor knockout mice executed three left/right choices leading to one of eight differentially rewarded goal sites. Compared with wild-type mice, knockouts exhibited selective impairments in decision making at choice points distal, but not proximal, to goal sites. We conclude that dopamines role in reinforcement learning depends on the temporal relationship of actions to reward and that dopamine signaling through D1 receptors constitutes a component of those brain mechanisms responsible for solving the distal reward problem.

Neuromorphic and Brain-Based Robots: The case for using brain-based devices to study consciousness

Introduction Within the past few decades, the nature of consciousness has become a central issue in neuroscience, and it is increasingly the focus of both theoretical and empirical work. Studying consciousness is vital to developing an understanding of human perception and behavior, of our relationships with one another, and of our relationships with other potentially conscious animals. Although the study of consciousness through the construction of artificial models is a recent innovation, the advantages of such an approach are clear. First, models allow us to investigate consciousness in ways that are currently not feasible using human subjects or other animals. Second, an artifact that exhibits the necessary and sufficient properties of consciousness may conceivably be the forerunner of a new and very useful class of neuromorphic robots. A model of consciousness must take into account current theories of its biological bases. Although the field of artificial consciousness is a new one, it is striking how little attention has been given to modeling mechanisms. Instead, great – and perhaps undue – emphasis has been placed on purely phenomenological models. Many of these models are strongly reductionist in aim and fail to specify neural mechanisms.

Imagery May Arise from Associations Formed through Sensory Experience: A Network of Spiking Neurons Controlling a Robot Learns Visual Sequences in Order to Perform a Mental Rotation Task

Mental imagery occurs “when a representation of the type created during the initial phases of perception is present but the stimulus is not actually being perceived.” How does the capability to perform mental imagery arise? Extending the idea that imagery arises from learned associations, we propose that mental rotation, a specific form of imagery, could arise through the mechanism of sequence learning–that is, by learning to regenerate the sequence of mental images perceived while passively observing a rotating object. To demonstrate the feasibility of this proposal, we constructed a simulated nervous system and embedded it within a behaving humanoid robot. By observing a rotating object, the system learns the sequence of neural activity patterns generated by the visual system in response to the object. After learning, it can internally regenerate a similar sequence of neural activations upon briefly viewing the static object. This system learns to perform a mental rotation task in which the subject must determine whether two objects are identical despite differences in orientation. As with human subjects, the time taken to respond is proportional to the angular difference between the two stimuli. Moreover, as reported in humans, the system fills in intermediate angles during the task, and this putative mental rotation activates the same pathways that are activated when the system views physical rotation. This work supports the proposal that mental rotation arises through sequence learning and the idea that mental imagery aids perception through learned associations, and suggests testable predictions for biological experiments.