11,309 research outputs found
Scalable parallel architecture for biological neural simulation on hardware platforms
Difficulties and dangers in doing experiments on living systems and providing a
testbed for theorists make the biologically detailed neural simulation an essential part of
neurobiology. Due to the complexity of the neural systems and dynamic properties of the
neurons simulation of biologically realistic models is very challenging area. Currently all
general purpose simulator are software based. Limitation on the available processing
power provides a huge gap between the maximum practical simulation size and human
brain simulation as the most complex neural system. This thesis aimed at providing a
hardware friendly parallel architecture in order to accelerate the simulation process.
This thesis presents a scalable hierarchical architecture for accelerating simulations of
large-scale biological neural systems on field-programmable gate arrays (FPGAs). The
architecture provides a high degree of flexibility to optimize the parallelization ratio
based on available hardware resources and model specifications such as complexity of
dendritic trees. The whole design is based on three types of customized processors and a
switching module. An addressing scheme is developed which allows flexible integration
of various combination of processors. The proposed addressing scheme, design
modularity and data process localization allow the whole system to extend over multiple
FPGA platforms to simulate a very large biological neural system.
In this research Hodgkin-Huxley model is adopted for cell excitability. Passive
compartmental approach is used to model dendritic tree with any level of complexity.
The whole architecture is verified in MATLAB and all processor modules and the
switching unit implemented in Verilog HDL and Schematic Capture. A prototype
simulator is integrated and synthesized for Xilinx V5-330t-1 as the target FPGA. While
not dependent on particular IP (Intellectual Property) cores, the whole implementation is
based on Xilinx IP cores including IEEE-754 64-bit floating-point adder and multiplier
cores. The synthesize results and performance analyses are provided
Neuromorphic Learning towards Nano Second Precision
Temporal coding is one approach to representing information in spiking neural
networks. An example of its application is the location of sounds by barn owls
that requires especially precise temporal coding. Dependent upon the azimuthal
angle, the arrival times of sound signals are shifted between both ears. In
order to deter- mine these interaural time differences, the phase difference of
the signals is measured. We implemented this biologically inspired network on a
neuromorphic hardware system and demonstrate spike-timing dependent plasticity
on an analog, highly accelerated hardware substrate. Our neuromorphic
implementation enables the resolution of time differences of less than 50 ns.
On-chip Hebbian learning mechanisms select inputs from a pool of neurons which
code for the same sound frequency. Hence, noise caused by different synaptic
delays across these inputs is reduced. Furthermore, learning compensates for
variations on neuronal and synaptic parameters caused by device mismatch
intrinsic to the neuromorphic substrate.Comment: 7 pages, 7 figures, presented at IJCNN 2013 in Dallas, TX, USA. IJCNN
2013. Corrected version with updated STDP curves IJCNN 201
An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation
We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499
Function-Theoretic Explanation and the Search for Neural Mechanisms
A common kind of explanation in cognitive neuroscience might be called functiontheoretic:
with some target cognitive capacity in view, the theorist hypothesizes that
the system computes a well-defined function (in the mathematical sense) and explains
how computing this function constitutes (in the systemâs normal environment) the
exercise of the cognitive capacity. Recently, proponents of the so-called ânew mechanistâ
approach in philosophy of science have argued that a model of a cognitive capacity is
explanatory only to the extent that it reveals the causal structure of the mechanism
underlying the capacity. If they are right, then a cognitive model that resists a transparent
mapping to known neural mechanisms fails to be explanatory. I argue that a functiontheoretic
characterization of a cognitive capacity can be genuinely explanatory even
absent an account of how the capacity is realized in neural hardware
Analog VLSI-Based Modeling of the Primate Oculomotor System
One way to understand a neurobiological system is by building a simulacrum that replicates its behavior in real time using similar constraints. Analog very large-scale integrated (VLSI) electronic circuit technology provides such an enabling technology. We here describe a neuromorphic system that is part of a long-term effort to understand the primate oculomotor system. It requires both fast sensory processing and fast motor control to interact with the world. A one-dimensional hardware model of the primate eye has been built that simulates the physical dynamics of the biological system. It is driven by two different analog VLSI chips, one mimicking cortical visual processing for target selection and tracking and another modeling brain stem circuits that drive the eye muscles. Our oculomotor plant demonstrates both smooth pursuit movements, driven by a retinal velocity error signal, and saccadic eye movements, controlled by retinal position error, and can reproduce several behavioral, stimulation, lesion, and adaptation experiments performed on primates
Neuromorphic analogue VLSI
Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
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