88 resources related to Behavioral Neuroscience
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
One of the flagship conferences for the IEEE Robotics and Automation Society (RAS)
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...
Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.
Neural Computation, 2008
The ultimate product of an electrophysiology experiment is often a decision on which biological hypothesis or model best explains the observed data. We outline a paradigm designed for comparison of different models, which we refer to as <italic>spike train prediction</italic>. A key ingredient of this paradigm is a prediction quality valuation that estimates how close a predicted conditional intensity function ...
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
Recently we described an iterative skull conductivity and source location estimation (SCALE) algorithm for simultaneously estimating head tissue conductivities and brain source locations. SCALE uses a realistic FEM forward problem head model and scalp maps of 10 or more near-dipolar sources identified by independent component analysis (ICA) decomposition of sufficient high-density EEG data. In this study, we applied SCALE to ...
2016 28th International Conference on Microelectronics (ICM), 2016
In neuroscience research the development of the brain and the treatment of diseases like certain forms of epilepsy is analysed with genetic mouse disease models. For the special case of the recording from neonatal mice a custom designed integrated circuit is presented. Neonatal mice are only two to three centimetres large and have a weight of only a few gram. ...
First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings., 2003
To address complex issues associated with the practical applications of brain- computer interface systems, we have developed a novel biorobotic system. We combine neurophysiological recordings in trained rats chronically implanted with multi-electrode arrays in sensory cortex with a mobile robot to create what we call the RABOT (rat-robot). The RABOT is a unique system that lends itself well to novel ...
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
This paper presents the optimal design and operation frequency (f) of an inductively-powered homecage for powering biomedical devices with millimeter (mm) dimensions, implanted inside the body of freely-behaving small animal subjects, for longitudinal behavioral neuroscience and electrophysiology experiments. In order to improve the power transmission efficiency (PTE) for powering mm-sized implants, the geometry of the multi-coil inductive links in the ...
Behavioral Signal Processing: Enabling human-centered behavioral informatics
ICASSP 2012 Plenary-Dr. Mitsuo Kawato
Sharing New Breakthroughs in Neuroscience
Laura Specker Sullivan: Neuroscience & Brain Panel - Forecasting the Future by Looking at the Past - TTM 2018
Robotics History: Narratives and Networks Oral Histories:Michael Arbib
Q&A: Neuroscience and Brain Panel - TTM 2018
IEEE Brain: Platypus Neuro : Applied Neuroscience and Optimized Human Performance at Scale
Requirements, Models, and Properties: Their Relationship and Validation
IEEE Brain: Backyard Brains: How We Bootstrapped a Low-Fi, High-Tech Neuroscience Company
Q&A with Jack Gallant: IEEE Brain Podcast, Episode 11
Q&A with Emery Brown: IEEE Brain Podcast, Episode 3
EDOC 2010 - Prof. Dr. David Harel Presentation
Engineering Our Future - Maja Mataric, Ph.D
Q&A with Dr. Al Emondi: IEEE Brain Podcast, Episode 13
A Conversation with Danielle Bassett: IEEE TechEthics Interview
Keynote: Poppy Crum - TTM 2018
Christoph Guger: Neuroscience & Brain Panel - The Future of Non-invasive Brain-computer Interfaces - TTM 2018
The EU Human Brain Project - A Systematic Path from Data to Synthesis
IEEE Brain: Funding Your Neurotech Start-Up with NIH SBIR/STTR Awards
The ultimate product of an electrophysiology experiment is often a decision on which biological hypothesis or model best explains the observed data. We outline a paradigm designed for comparison of different models, which we refer to as <italic>spike train prediction</italic>. A key ingredient of this paradigm is a prediction quality valuation that estimates how close a predicted conditional intensity function is to an actual observed spike train. Although a valuation based on log likelihood (L) is most natural, it has various complications in this context. We propose that a quadratic valuation (Q) can be used as an alternative to L. Q shares some important theoretical properties with L, including consistency, and the two valuations perform similarly on simulated and experimental data. Moreover, Q is more robust than L, and optimization with Q can dramatically improve computational efficiency. We illustrate the utility of Q for comparing models of peer prediction, where it can be computed directly from cross-correlograms. Although Q does not have a straightforward probabilistic interpretation, Q is essentially given by Euclidean distance.
Recently we described an iterative skull conductivity and source location estimation (SCALE) algorithm for simultaneously estimating head tissue conductivities and brain source locations. SCALE uses a realistic FEM forward problem head model and scalp maps of 10 or more near-dipolar sources identified by independent component analysis (ICA) decomposition of sufficient high-density EEG data. In this study, we applied SCALE to 20 minutes of 64-channel EEG data and magnetic resonance (MR) head images from four twelve- months-of-age infants. For each child, we selected 15-16 near-dipolar independent components from multiple-model adaptive mixture ICA (AMICA) decomposition of their EEG data. SCALE converged to brain-to-skull conductivity ratio (BSCR) estimates in the 10-12 range and mostly compact gyral or sulcal cortical distributions for the IC sources.
In neuroscience research the development of the brain and the treatment of diseases like certain forms of epilepsy is analysed with genetic mouse disease models. For the special case of the recording from neonatal mice a custom designed integrated circuit is presented. Neonatal mice are only two to three centimetres large and have a weight of only a few gram. Thus, the recording circuitry has to be very small and light weight. The integrated circuit implements 16 low-area, low-power analogue differential preamplifiers with a bandpass characteristic (0.5 Hz to 10 kHz). A multiplexed structure of 8:1 multiplexer, post amplifier and 10 bit successive approximation register (SAR) analogue-to-digital converter (ADC) digitizes the signals with high resolution. The digital data is transmitted via a Serial Peripheral Interface (SPI). The integrated circuit has been implemented in a 130 nm CMOS technology and has been successfully applied in in-vivo measurements with an adult mouse.
To address complex issues associated with the practical applications of brain- computer interface systems, we have developed a novel biorobotic system. We combine neurophysiological recordings in trained rats chronically implanted with multi-electrode arrays in sensory cortex with a mobile robot to create what we call the RABOT (rat-robot). The RABOT is a unique system that lends itself well to novel experiments in behavioral neuroscience and neural engineering. 6 rats were implanted with multi-electrode arrays in auditory cortex and placed inside a modified 12" round robot with 2-wheel differential drive locomotion controllable via on-board response levers. In preliminary evaluation, RABOTs were passively moved along a 16 ft track while a 2 Hz, free-field click stimulus was delivered at one end of the track. Response latencies varied almost linearly within the 16 ft track from 18-27 msec. Analysis of peak latency of multichannel PSTH responses was performed offline and used to estimate RABOT position and/or predict stimulus onset. The RABOT platform is ideal for both on and offline experimentation that more closely resembles real-world applications of brain-computer interfaces. In addition, multichannel neural recording in a 'real-world' sensory environment will help to further broaden our understanding of parallel processing in the nervous system.
This paper presents the optimal design and operation frequency (f) of an inductively-powered homecage for powering biomedical devices with millimeter (mm) dimensions, implanted inside the body of freely-behaving small animal subjects, for longitudinal behavioral neuroscience and electrophysiology experiments. In order to improve the power transmission efficiency (PTE) for powering mm-sized implants, the geometry of the multi-coil inductive links in the form of 3- and 4-coil links as well as fp need to be co-optimized. A simplified equation for the PTE of 3-coil inductive links for powering mm- sized implants has been derived, based on which the optimal geometries and fp of a 3-coil link have been found using a commercial field solver (HFSS). In simulations, the optimized 3-coil inductive link achieved a significant PTE of 2.56% at the optimal fp of 40 MHz for powering a 1 mm<sup>3</sup> implant coil at the nominal height of 7 cm, thanks to the link and fp optimization as well as an intermediate coil in the receiver side with 18 mm diameter.
Summary form only given. The resurgence of artificial neural networks in the past decade has affected basic and applied research in neural and cognitive sciences and has generated numerous models that address centuries old questions confronted by neuroscientists and neural engineers. Computational and silicon based models involving dynamics of neural networks play increasingly larger roles in the quantitative formulation of these questions. Topics such as perception, consciousness, memory and will are no longer quantitatively untouchable subjects. They can be formulated with biologically realistic models and tested experimentally. The emerging discipline of neural network dynamics strives to understand the organizational principles and underlying mechanisms of the biology and behavior of neural systems in nature. It coalesces the new emerging fields in engineering and physical sciences including nonlinear dynamics, chaos, wavelets and time-frequency distributions, informatics, silicon and nanotechnologies with the molecular, cellular, systems physiology, cognitive and behavioral neuroscience. To highlight this emerging discipline, we devote this symposium to the neural network dynamics related research.
The firing-rate data from 341 cells from two macaques' superion temporal polysensory area (STPa) were subjected to three different analyses to determine the temporal firing-rate patterns in response to visual optic flow patterns. The data were collected while the monkey viewed four types of optic flow and responded to the change in the display. The mean firing rate (MFR) analysis considered the mean change in firing rate for 500 ms after stimulus onset; the discriminant (DIS) analysis and the principal components (PCA+DIS) analysis considered the change in time-binned firing rate over 1000 ms after stimulus presentation, using bin sizes of 30 to 500 ms. The DIS analysis used a step-down discriminant analysis to find temporal windows in which the cell's firing rate could discriminate among the stimuli; the PCA+DIS analysis extracted the principal components of the cell's firing rates without regard for the stimulus type and then applied a step-down discriminant analysis to the PCA scores to determine whether any of the principal components could discriminate among the stimuli. The two temporal analyses found cells sensitive to the optic flows that the MFR analysis missed. A small proportion of cells showed multiple selectivities under the temporal analyses. Thus, the temporal analyses give a more complete representation of the information encoded by the firing properties of STPa neurons. Finally, this approach incorporates temporal approaches with classical statistical techniques in order to select tuned neurons from a population in an unbiased manner.
A low-noise wideband receiver (Rx) is presented for a multichannel wireless implantable neural recording (WINeR) system that utilizes time-division multiplexing of pulse width modulated (PWM) samples. The WINeR-6 Rx consists of four parts: 1) RF front end; 2) signal conditioning; 3) analog output (AO); and 4) field-programmable gate array (FPGA) back end. The RF front end receives RF-modulated neural signals in the 403-490 MHz band with a wide bandwidth of 18 MHz. The frequency-shift keying (FSK) PWM demodulator in the FPGA is a time-to-digital converter with 304 ps resolution, which converts the analog pulse width information to 16-bit digital samples. Automated frequency tracking has been implemented in the Rx to lock onto the free-running voltage- controlled oscillator in the transmitter (Tx). Two antennas and two parallel RF paths are used to increase the wireless coverage area. BCI-2000 graphical user interface has been adopted and modified to acquire, visualize, and record the recovered neural signals in real time. The AO module picks three demultiplexed channels and converts them into analog signals for direct observation on an oscilloscope. One of these signals is further amplified to generate an audio output, offering users the ability to listen to ongoing neural activity. Bench-top testing of the Rx performance with a 32-channel WINeR-6 Tx showed that the input referred noise of the entire system at a Tx- Rx distance of 1.5 m was 4.58 μVrmswith 8-bit resolution at 640 kSps. In an in vivo experiment, location-specific receptive fields of hippocampal place cells were mapped during a behavioral experiment in which a rat completed 40 laps in a large circular track. Results were compared against those acquired from the same animal and the same set of electrodes by a commercial hardwired recording system to validate the wirelessly recorded signals.
An inductively-powered wireless integrated neural recording and stimulation (WINeRS-8) system-on-a-chip (SoC) that is compatible with the EnerCage-HC2 for wireless/battery-less operation has been presented for neuroscience experiments on freely behaving animals. WINeRS-8 includes a 32-ch recording analog front end, a 4-ch current-controlled stimulator, and a 434 MHz on-off keying data link to an external software- defined radio wideband receiver (Rx). The headstage also has a bluetooth low energy link for controlling the SoC. WINeRS-8/EnerCage-HC2 systems form a bidirectional wireless and battery- less neural interface within a standard homecage, which can support longitudinal experiments in an enriched environment. Both systems were verified in vivo on rat animal model, and the recorded signals were compared with hardwired and battery-powered recording results. Realtime stimulation and recording verified the system's potential for bidirectional neural interfacing within the homecage, while continuously delivering 35 mW to the hybrid WINeRS-8 headstage over an unlimited period.
The EnerCage system is designed to enable electrophysiology experiments of any duration in experimental arenas of any size. Optimized overlapping planar spiral coils (PSCs) provide closed-loop wireless power and data to a mobile unit on a small, freely behaving animal subject. The system tracks the location of the mobile unit with an array of 3-axis magnetic sensors and only activates the closest PSC, thereby reducing power dissipation and heat within the cage. A control algorithm collects data from all the sensors, organizes it by physical location, and systematically compares the magnitudes of each to two thresholds to determine the location of a small magnetic tracer embedded in the mobile unit. An in vivo experiment was conducted for one hour using a 1.03 × 0.9 m2EnerCage prototype in a physiology lab, where the system was able to continuously deliver 20 mW to a dummy load and maintain a steady 4V supply on the rat headstage (mobile unit).
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