Building an organic computing device with multiple interconnected brains. (original) (raw)
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On the Way to Large-Scale and High-Resolution Brain-Chip Interfacing
Cognitive Computation, 2012
Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured / acute brain slices, or implanted "in vivo". This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in-vivo interfacing, either in terms of signal-tonoise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and highresolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs.
Frontiers in neuroscience, 2016
Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. Ho...
A multielectrode implant device for the cerebral cortex
Journal of Neuroscience Methods, 1999
A new class of brain implant technology was developed that allows the simultaneous recording of voltage signals from many individual neurons in the cerebral cortex during cognitive tasks. The device allows recording from 49 independent positions spanning a 2 × 2-mm region of neural tissue. The recording electrodes are positioned in a square grid with 350 mm spacing, and each microelectrode can be precisely independently vertically positioned using a hydraulic microdrive. The device utilizes ultrafine, sharp iridium microelectrodes that minimize mechanical disturbance of the region near the electrode tip and produce low noise neuronal recordings. The total weight of this device is less than 20 g, and the device is reusable. The implant device has been used for transdural recordings in primary somatosensory and auditory cortices of marmosets, owl monkeys, and rats. On a typical day, one-third of the microelectrodes yield well-discriminated single neuron action potential waveforms. Additional array electrodes yield lower amplitude driven multiunit activity. The average signal-to-noise ratio of discriminated action potential waveforms 6 months after implantation was greater than 9. Simple design alternatives are discussed that can increase the number of electrodes in the array and the depths at which dense array recordings can be achieved. : S 0 1 6 5 -0 2 7 0 ( 9 9 ) 0 0 0 8 7 -4
A Brain-to-Brain Interface for Real-Time Sharing of Sensorimotor Information
A brain-to-brain interface (BTBI) enabled a real-time transfer of behaviorally meaningful sensorimotor information between the brains of two rats. In this BTBI, an ''encoder'' rat performed sensorimotor tasks that required it to select from two choices of tactile or visual stimuli. While the encoder rat performed the task, samples of its cortical activity were transmitted to matching cortical areas of a ''decoder'' rat using intracortical microstimulation (ICMS). The decoder rat learned to make similar behavioral selections, guided solely by the information provided by the encoder rat's brain. These results demonstrated that a complex system was formed by coupling the animals' brains, suggesting that BTBIs can enable dyads or networks of animal's brains to exchange, process, and store information and, hence, serve as the basis for studies of novel types of social interaction and for biological computing devices. I n his seminal study on information transfer between biological organisms, Ralph Hartley wrote that ''in any given communication the sender mentally selects a particular symbol and by some bodily motion, as his vocal mechanism, causes the receiver to be directed to that particular symbol'' 1. Brain-machine interfaces (BMIs) have emerged as a new paradigm that allows brain-derived information to control artificial actuators 2 and communicate the subject's motor intention to the outside world without the interference of the subject's body. For the past decade and a half, numerous studies have shown how brain-derived motor signals can be utilized to control the movements of a variety of mechanical, electronic and even virtual external devices 3–6. Recently, intracortical microstimulation (ICMS) has been added to the classical BMI paradigm to allow artificial sensory feedback signals 7,8 , generated by these brain-controlled actuators, to be delivered back to the subject's brain simultaneously with the extraction of cortical motor commands 9,10. In the present study, we took the BMI approach to a new direction altogether and tested whether it could be employed to establish a new artificial communication channel between animals; one capable of transmitting behaviorally relevant sensorimotor information in real-time between two brains that, for all purposes, would from now on act together towards the fulfillment of a particular behavioral task. Previously, we have reported that specific motor 11,12 and sensory parameters 13,14 can be extracted from populations of cortical neurons using linear or nonlinear decoders in real-time. Here, we tested the hypothesis that a similar decoding performed by a ''recipient brain'' was sufficient to guide behavioral responses in sensorimotor tasks, therefore constituting a Brain-to-Brain Interface (BTBI) 15 (Figure 1). To test this hypothesis, we conducted three experiments in which different patterns of cortical sensorimotor signals, coding a particular behavioral response, were recorded in one rat (heretofore named the ''encoder'' rat) and then transmitted directly to the brain of another animal (i.e. the ''decoder'' rat), via intra-cortical microstimulation (ICMS). All BTBI experiments described below were conducted in awake, behaving rats chronically implanted with cortical microelectrode arrays capable of both neur-onal ensemble recordings and intracortical microstimulation 16. We demonstrated that pairs of rats could cooperate through a BTBI to achieve a common behavioral goal. Results In our training paradigm, animals learned basic elements of the tasks prior to participating in any BTBI experiments. First, prospective encoder rats were trained to respond to either tactile or visual stimuli until they reached 95% correct trials accuracy. Meanwhile, decoder rats were trained to become proficient while receiving ICMS as a stimulus. A train of ICMS pulses instructed the animal to select one of the levers/nose pokes, whereas a single ICMS pulse instructed a response to the other option. Decoder rats reached a 78.77% 6 2.1 correct trials
A Platform Technology For Brain Emulation Updated 9-05-2013
A computer is a great tool for statistical analysis, simulation and number crunching, but its usefulness is limited in Artificial Intelligence applications and in the simulation of biologically accurate neural networks. This is due to the sequential nature of these machines, whereby all data has to pass through a central processor in chunks of 16, 32 or 64 bits, depending on the width of the data bus. In contrast, the brain's network is massively parallel and processes the equivalent of millions of data bits simultaneously. Simulation of a sizable network of biologically accurate neurons requires the resources of a huge supercomputer consisting of tens of thousands of processors. Even so, attempts to emulate the entire human brain are far removed from their goal. The published results of IBM's efforts to emulate the brain on a Blue Gene Supercomputer shows that these machines run the emulation software at 1/640th of the brain's real speed and 1/10th of its capacity. There are also doubts about the accuracy of the neuron model (ref. "The cat is out of the bag") So called "Artificial Neural Networks (ANNs) have little to do with how the brain actually works. ANNs are based on a 1950's concept of a neuron. The learning time of Multi-layer ANNs with back propagation networks increases exponentially due to the method of learning, even when simplified training sets are applied i . The method of learning is limited to the information that can be passed over the narrow data bus. The CPU is a bottleneck and all data has to pass through it.
Brain-Implantable Biomimetic Electronics As Neural Prosthetics
Proceedings of the 25th …, 2003
An interdisciplinary multilaboratory effort to develop an implantable neural prosthetic that can coexist and bidirectionally communicate with living brain tissue is described. Although the final achievement of such a goal is many years in the future, it is proposed that the path to an implantable prosthetic is now definable, allowing the problem to be solved in a rational, incremental manner. Outlined in this report is our collective progress in developing the underlying science and technology that will enable the functions of specific brain damaged regions to be replaced by multichip modules consisting of novel hybrid analog/digital microchips. The component microchips are "neurocomputational" incorporating experimentally based mathematical models of the nonlinear dynamic and adaptive properties of biological neurons and neural networks. The hardware developed to date, although limited in capacity, can perform computations supporting cognitive functions such as pattern recognition, but more generally will support any brain function for which there is sufficient experimental information. To allow the "neurocomputational" multichip module to communicate with existing brain tissue, another novel microcircuitry element
The neuronal network controlling feeding behavior in the CNS of the mollusc Lymnaea stagnalis has been extensively investigated using intracellular microelectrodes. Using microelectrodes however it has not been possible to record from large numbers of neurons simultaneously and therefore little is known about the population coding properties of the feeding network. Neither can the relationships between feeding and neuronal networks controlling other behaviors be easily analyzed with microelectrodes. Here we describe a multielectrode array (MEA) technique for recording action potentials simultaneously from up to 60 electrodes on the intact CNS. The preparation consists of the whole CNS connected by sensory nerves to the chemosensory epithelia of the lip and esophagus. From the buccal ganglia, the region of the CNS containing the feeding central pattern generator (CPG), a rhythmic pattern of activity characteristic of feeding was readily induced either by depolarizing an identified feeding-command neuron (the CV1a) or by perfusing the chemosensory epithelia with sucrose, a gustatory stimulus known to activate feeding. Activity induced by sucrose is not restricted to the buccal ganglia but is distributed widely throughout the CNS, notably in ganglia controlling locomotion, a behavior that must be coordinated with feeding. The MEA also enabled us to record electrophysiological consequences of the associative conditioning of feeding behavior. The results suggest that MEA recording from an intact CNS enables distributed, multiple-source neural activity to be analyzed in the context of biologically relevant behavior, behavioral coordination and behavioral plasticity.
2003
Abstruct-A chief concern in the pursuit of controlling a neuroprosthetic device using direct brain signals is the question of how many bits of information are achievable through a direct brain-machine interface (BMI) via implantable microelectrode devices. This experiment begins to address this issue with implementation of a simple, software based decoding algorithm that allows the brain to adapt to the rules imposed upon it, To test this algorithm, two chronic Idchannel Michigan silicon microelectrode arrays were implanted into the primary motor cortex of two rats to record simultaneous unit spike activity. The animals were trained to perform an auditory detection task by modulating the recorded cortical spike activity in a prescribed manner. Both non-adaptive and adaptive neural decoding algorithms were evaluated. With the implementation of a non-adaptive decoding algorithm, the rats' behavioral (cortical) responses plateaued at approximately 75% correct; however, with the implementation of an adaptive algorithm, the rats' behavioral responses relatively quickly increased to 91% correct. The neural recordings suggest that the brain is able to modulate detailed cortical responses in accordance with the prescribed operant conditioning rules.
Neurotech for Neuroscience: Unifying Concepts, Organizing Principles, and Emerging Tools
Journal of Neuroscience, 2007
The ability to tackle analysis of the brain at multiple levels simultaneously is emerging from rapid methodological developments. The classical research strategies of "measure," "model," and "make" are being applied to the exploration of nervous system function. These include novel conceptual and theoretical approaches, creative use of mathematical modeling, and attempts to build brain-like devices and systems, as well as other developments including instrumentation and statistical modeling (not covered here). Increasingly, these efforts require teams of scientists from a variety of traditional scientific disciplines to work together. The potential of such efforts for understanding directed motor movement, emergence of cognitive function from neuronal activity, and development of neuromimetic computers are described by a team that includes individuals experienced in behavior and neuroscience, mathematics, and engineering. Funding agencies, including the National Science Foundation, explore the potential of these changing frontiers of research for developing research policies and long-term planning.
Computing Arm Movements with a Monkey Brainet OPEN
Traditionally, brain-machine interfaces (BMIs) extract motor commands from a single brain to control the movements of artificial devices. Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour. A B2 generated 2D movements of an avatar arm where each monkey contributed equally to X and Y coordinates; or one monkey fully controlled the X-coordinate and the other controlled the Y-coordinate. A B3 produced arm movements in 3D space, while each monkey generated movements in 2D subspaces (X-Y, Y-Z, or X-Z). With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan. Overall, performance of the Brainet improved owing to collective monkey behaviour. These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal. BMIs are computational systems that link biological circuits to external devices, such as computer cursors , robotic prostheses and communication aids 1. Heretofore, BMIs have been utilized either: (i) to extract motor signals from neural activity and convert them to the movements of external devices 2–5 , (ii) to deliver sensory signals from the environment to the brain 6–8 , or (iii) to combine both operations and enable bidirectional communications between the brain and machine 9. In each of these implementations, a BMI serves as an accessory to a single brain. Recently an entirely new direction was proposed for BMI research – a brain to brain interface (BtBI) 10. BtBI allows animal brains to exchange and share sensory and motor information to achieve a behavioural goal 11,12–17. BtBI is a hybrid computational system since it incorporates both biological components (the primate brains) and digital parts (the BMI system). In the present study, we have designed and tested a more elaborate computational architecture which we refer to as a Brainet 10. Our Brainets involved groups formed by 2-3 monkeys in a shared BMI that enacted conjoint motor behaviours. Previously, human psychophysics studies have shown that two or more individuals who are performing movements simultaneously often entrain to each other's behavior, even if they are not explicitly instructed to do so 18–22. However, the neurophysiological mechanisms of such joint actions are not well understood. In particular, we were interested in investigating the possibility that neuronal ensemble could directly control conjoint behaviors enabled by multiple interconnected BMIs. Our study adds to previous attempts to overcome limitations of one individual confronted with a high processing load by mixing contributions of multiple individuals 23–29. Particularly relevant to our present work, several EEG studies 13,30–34 have combined brain derived signals from multiple subjects to enhance visual discrimination, motor performance, and decision making. A recent EEG study 30 has implemented shared control that involved dynamic collaboration of multiple individuals in real time to achieve a common goal. However, in none of these EEG experiments participants interacted with each other over a long term. Moreover, no large-scale intracranial cortical recordings were obtained in order to investigate