Brain-based Device Research Articles

Synthesis of a hybrid brain for a humanoid robot

This article comprehends the design of a Brain-Based Robot (BBR) using hybrid techniques that incorporate both Brain-Based Device (BBD) and computational algorithms. BBDs are biologically inspired machines which have its behavior guided by a simulated nervous system. This nervous system follows detailed neuroanatomy of different brain areas. BBDs tend to have a nervous system with a large number of neurons and synapses. Thus, a huge computational power is required to simulate the nervous system of a BBD. Nevertheless, some of the tasks carried out by the simulated nervous system can be accomplished using computational algorithms which can help reducing the required computational power greatly. In this article, a BBR is built which combines some subsystems from BBD with computer vision algorithms. Computer vision algorithms are applied using OpenCV to extract some features from images, while neuronal-areas are connected together based on a detailed neuroanatomical structure to mimic the human learning process. Nengo python package is used for simulating neuronal areas in the system and monitoring activities of neuronal units. Moreover, the successful integration of the BBD’s subsystems with computer vision leads to the perceptual categorization based on invariant object-recognition of various visual cues. To make a fair comparison with BBD, the nervous system of a BBD is built on the same computer used to build the hybrid brain for the proposed BBR. The proposed hybrid brain is then applied to a Nao humanoid robot in V-REP simulation environment to test it. The results obtained through this article prove that the proposed hybrid brain possesses the same intelligence of the BBD and requires much less computational power that it can run on an on-board computer of a robot, which makes it plausible for engineering applications.

A Simplified Cerebellar Model with Priority-based Delayed Eligibility Trace Learning for Motor Control

The study of cerebellum has resulted in a common agreement that it is implicated in motor learning for movement coordination. Learning governed by error signal through synaptic eligibility traces has been proposed to be a learning mechanism in cerebellum. In this paper, we extend this idea and suggest a simplified and improved cerebellar model with priority-based delayed eligibility trace learning rule (S-CDE) that enables a mobile robot to freely and smoothly navigate in an environment. S-CDE is constructed in a brain-based device which mimics the anatomy, physiology, and dynamics of cerebellum. The input signal in terms of depth information generated from a simulated laser sensor is encoded as neuronal region activity for velocity and turn rate control. A priority-based delayed eligibility trace learning rule is proposed to maximize the usage of input signals for learning in synapses on Purkinje cell and cells in the deep cerebellar nuclei of cerebellum. Error signal generation and input signal conversion algorithms for turn rate and velocity are designed to facilitate training in an environment containing turns of varying curvatures. S-CDE is tested on a simulated mobile robot which had to randomly navigate maps of Singapore and Hong Kong expressways.

Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

Embodied models of delayed neural responses: Spatiotemporal categorization and predictive motor control in brain based devices

In order to respond appropriately to environmental stimuli, organisms must integrate over time spatiotemporal signals that reflect object motion and self-movement. One possible mechanism to achieve this spatiotemporal transformation is to delay or lag neural responses. This paper reviews our recent modeling work testing the sufficiency of delayed responses in the nervous system in two different behavioral tasks: (1) Categorizing spatiotemporal tactile cues with thalamic "lag" cells and downstream coincidence detectors, and (2) Predictive motor control was achieved by the cerebellum through a delayed eligibility trace rule at cerebellar synapses. Since the timing of these neural signals must closely match real-world dynamics, we tested these ideas using the brain based device (BBD) approach in which a simulated nervous system is embodied in a robotic device. In both tasks, biologically inspired neural simulations with delayed neural responses were critical for successful behavior by the device.

Distinguishing Causal Interactions in Neural Populations

We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.

A cerebellar model for predictive motor control tested in a brain-based device

The cerebellum is known to be critical for accurate adaptive control and motor learning. We propose here a mechanism by which the cerebellum may replace reflex control with predictive control. This mechanism is embedded in a learning rule (the delayed eligibility trace rule) in which synapses onto a Purkinje cell or onto a cell in the deep cerebellar nuclei become eligible for plasticity only after a fixed delay from the onset of suprathreshold presynaptic activity. To investigate the proposal that the cerebellum is a general-purpose predictive controller guided by a delayed eligibility trace rule, a computer model based on the anatomy and dynamics of the cerebellum was constructed. It contained components simulating cerebellar cortex and deep cerebellar nuclei, and it received input from a middle temporal visual area and the inferior olive. The model was incorporated in a real-world brain-based device (BBD) built on a Segway robotic platform that learned to traverse curved paths. The BBD learned which visual motion cues predicted impending collisions and used this experience to avoid path boundaries. During learning, the BBD adapted its velocity and turning rate to successfully traverse various curved paths. By examining neuronal activity and synaptic changes during this behavior, we found that the cerebellar circuit selectively responded to motion cues in specific receptive fields of simulated middle temporal visual areas. The system described here prompts several hypotheses about the relationship between perception and motor control and may be useful in the development of general-purpose motor learning systems for machines.

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task.

Analyzing neural dynamics underlying complex behavior is a major challenge in systems neurobiology. To meet this challenge through computational neuroscience, we have constructed a brain-based device (Darwin X) that interacts with a real environment, and whose behavior is guided by a simulated nervous system incorporating detailed aspects of the anatomy and physiology of the hippocampus and its surrounding regions. Darwin X integrates cues from its environment to solve a spatial memory task. Place-specific units, similar to place cells in the rodent, emerged by integrating visual and self-movement cues during exploration without prior assumptions in the model about environmental inputs. Because synthetic neural modeling using brain-based devices allows recording from all elements of the simulated nervous system during behavior, we were able to identify different functional hippocampal pathways. We did this by tracing back from reference neuronal units in the CA1 region of the simulated hippocampus to all of the synaptically connected units that were coactive during a particular exploratory behavior. Our analysis identified a number of different functional pathways within the simulated hippocampus that incorporate either the perforant path or the trisynaptic loop. Place fields, which were activated by the trisynaptic circuit, tended to be more selective and informative. However, place units that were activated by the perforant path were prevalent in the model and were crucial for generating appropriate exploratory behavior. Thus, in the model, different functional pathways influence place field activity and, hence, behavior during navigation.

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.

Visual binding through reentrant connectivity and dynamic synchronization in a brain-based device.

Effective visual object recognition requires mechanisms to bind object features (e.g. color, shape and motion) while distinguishing distinct objects. Synchronously active neuronal circuits among reentrantly connected cortical areas may provide a basis for visual binding. To assess the potential of this mechanism, we have constructed a mobile brain-based device, Darwin VIII, which is guided by simulated analogues of cortical and sub-cortical areas required for visual processing, decision-making, reward and motor responses. These simulated areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. Darwin VIII learns to discriminate among multiple objects with shared visual features and associates 'target' objects with innately preferred auditory cues. We observed the co-activation of globally distributed neuronal circuits that corresponded to distinct objects in Darwin VIII's visual field. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, are necessary for successful discrimination. By situating Darwin VIII in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated movement, and by recording its behavioral and neuronal responses in detail, we were able to show that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects.

Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device.

In studying brain activity during the behavior of living animals, it is not possible simultaneously to analyze all levels of control from molecular events to motor responses. To provide insights into how levels of control interact, we have carried out synthetic neural modeling using a brain-based real-world device. We describe here the design and performance of such a device, designated Darwin VII, which is guided by computer-simulated analogues of cortical and subcortical structures. All levels of Darwin VII's neural architecture can be examined simultaneously as the device behaves in a real environment. Analysis of its neural activity during perceptual categorization and conditioned behavior suggests neural mechanisms for invariant object recognition, experience-dependent perceptual categorization, first-order and second-order conditioning, and the effects of different learning rates on responses to appetitive and aversive events. While individual Darwin VII exemplars developed similar categorical responses that depended on exploration of the environment and sensorimotor adaptation, each showed highly individual patterns of changes in synaptic strengths. By allowing exhaustive analysis and manipulation of neuroanatomy and large-scale neural dynamics, such brain-based devices provide valuable heuristics for understanding cortical interactions. These devices also provide the groundwork for the development of intelligent machines that follow neurobiological rather than computational principles in their construction.