Neurosciences Research Foundation, Inc.

Neuroscience Research Foundation, Inc.

Jeffrey L Krichmar

Gerald M Edelman

Donald Hutson

James Andrew Snook

Douglas A Nitz

Jeffrey L McKinstry

Douglas Alan Moore

Jason G Fleischer

Botond Szatmáry

Anil K Seth

A brain-based device (BBD) for moving in a real-world environment has sensors that provide data about the environment, actuators to move the BBD, and a hybrid controller which includes a neural controller having a simulated nervous system being a model of selected areas of the human brain and a non-neural controller based on a computational algorithmic network. The neural controller and non-neural controller interact with one another to control movement of the BBD.

Hybrid control device

A brain-based device (BBD) having a physical mobile device NOMAD controlling and under control by a simulated nervous system. The simulated nervous system is based on an intricate anatomy and physiology of the hippocampus and its surrounding neuronal regions including the cortex. The BBD integrates spatial signals from numerous objects in time and provides flexible navigation solutions to aid in the exploration of unknown environments. As NOMAD navigates in its real world environment, the hippocampus of the simulated nervous system organizes multi-modal input information received from sensors on NOMAD over timescales and uses this organization for the development of spatial and episodic memories necessary for navigation.

Mobile brain-based device having a simulated nervous system based on the hippocampus

A mobile brain-based device (BBD) includes a mobile platform with sensors and effectors, which is guided by a simulated nervous system that is an analogue of the cerebellar areas of the brain used for predictive motor control to determine interaction with a real-world environment. The simulated nervous system has neural areas including precerebellum nuclei (PN), Purkinje cells (PC), deep cerebellar nuclei (DCN) and an inferior olive (IO) for predicting turn and velocity control of the BBD during movement in a real-world environment. The BBD undergoes training and testing, and the simulated nervous system learns and performs control functions, based on a delayed eligibility trace learning rule.

Brain-based device having a cerebellar model for predictive motor control

A special purpose processor (SPP) for implementing a synthetic neural model of the biological anatomy of the human brain to control a brain-based device (BBD) that is movable in a real-world environment, including neural processing units (NPUs), each having a programmed processor and a local memory that stores data records of neural elements, a system memory for storing data about all the NPUs, and a finite state machine and a system bus for transferring data between the NPUs and system memory.

Special purpose processor implementing a synthetic neural model of the human brain

A special purpose processor (SPP) can use a Field Programmable Gate Array (FPGA) or similar programmable device to model a large number of neural elements. The FPGAs can have multiple cores doing presynaptic, postsynaptic, and plasticity calculations in parallel. Each core can implement multiple neural elements of the neural model.

Neural modeling and brain-based devices using special purpose processor

A mobile brain-based device BBD includes a mobile base equipped with sensors and effectors (Neurally Organized Mobile Adaptive Device or NOMAD), which is guided by a simulated nervous system that is an analogue of cortical and sub-cortical areas of the brain required for visual processing, decision-making, reward, and motor responses. These simulated cortical and sub-cortical 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. The brain-based device BBD learns to discriminate among multiple objects with shared visual features, and associated 'target' objects with innately preferred auditory cues. Globally distributed neuronal circuits that correspond to distinct objects in the visual field of NOMADare activated. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, result in successful discrimination of objects. The brain-based device BBD is moveable, in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated or autonomous, movement, and shows that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects so as to reorganize object features such as color, shape and motion while distinguishing distinct objects in the environment.

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Cardiff-by-the-Sea, CA, United States of America

Jeffrey L Krichmar