F. Folowosele

Johns Hopkins University, Baltimore, MD, United States

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Publications (14)2.95 Total impact

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    ABSTRACT: There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin-Huxley model. The simpler models tend to be easily implemented in silicon but yet not biologically plausible. Conversely, the more complex models tend to occupy a large area although they are more biologically plausible. In this paper, we present the 0.5 μm complementary metal-oxide-semiconductor (CMOS) implementation of the Mihalaş-Niebur neuron model--a generalized model of the leaky integrate-and-fire neuron with adaptive threshold--that is able to produce most of the known spiking and bursting patterns that have been observed in biology. Our implementation modifies the original proposed model, making it more amenable to CMOS implementation and more biologically plausible. All but one of the spiking properties--tonic spiking, class 1 spiking, phasic spiking, hyperpolarized spiking, rebound spiking, spike frequency adaptation, accommodation, threshold variability, integrator and input bistability--are demonstrated in this model.
    IEEE Transactions on Neural Networks 12/2011; 22(12):1915-27. · 2.95 Impact Factor
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    ABSTRACT: Object recognition and categorization are computationally difficult tasks that are performed effortlessly by humans. Attempts have been made to emulate the computations in different parts of the primate cortex to gain a better understanding of the cortex and to design brain–machine interfaces that speak the same language as the brain. The HMAX model proposed by Riesenhuber and Poggio and extended by Serre attempts to truly model the visual cortex. In this paper, we provide a spike-based implementation of the HMAX model, demonstrating its ability to perform biologically-plausible MAX computations as well as classify basic shapes. The spike-based model consists of 2514 neurons and 17$thinspace$305 synapses (S1 Layer: 576 neurons and 7488 synapses, C1 Layer: 720 neurons and 2880 synapses, S2 Layer: 576 neurons and 1152 synapses, C2 Layer: 640 neurons and 5760 synapses, and Classifier: 2 neurons and 25 synapses). Without the limits of the retina model, it will take the system 2 min to recognize rectangles and triangles in 24$,times,$24 pixel images. This can be reduced to 4.8 s by rearranging the lookup table so that neurons which have similar responses to the same input(s) can be placed on the same row and affected in parallel.
    Emerging and Selected Topics in Circuits and Systems, IEEE Journal on. 01/2011; 1(4):516-525.
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    ABSTRACT: Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.
    Frontiers in Neuroscience 01/2011; 5:73.
  • Neural Networks, IEEE Transactions on. 01/2011; 22(12):1915-1927.
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    ABSTRACT: The Mihalas-Niebur neural model is a generalized model of the leaky integrate-and-fire neuron with adaptive threshold. It has been shown in simulation to be capable of producing most of the known spiking and bursting patterns of cortical neurons. We present results from the first circuit implementation of the model with six spiking patterns observed in biological regular-spiking, fast-spiking and low-threshold spiking inhibitory neurons. The circuit was implemented in a 0.5 um CMOS process occupying an area of 277 um by 177 um.
    Biomedical Circuits and Systems Conference, 2009. BioCAS 2009. IEEE; 12/2009
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    ABSTRACT: In this paper we present the circuits and simulation results for a silicon neuron which is based on a modified version of the Mihalas-Niebur neural model. This silicon neuron produces 15 of the 20 known neural spiking and bursting behaviors. It has low complexity and reliable matching and can thus be easily integrated into more complex neuromorphic systems. Implemented in a 0.15 mum 1.5 V CMOS process, each neuron consumes about 7.5 nW of power at 1 kHz and occupies an area of 70 mum by 70 mum.
    International Symposium on Circuits and Systems (ISCAS 2009), 24-17 May 2009, Taipei, Taiwan; 01/2009
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    ABSTRACT: We present a real-time implementation of the V1 stage of the hierarchical model of object recognition in the primate visual cortex with 2400 simple cells and 80 complex cells. This emulation of the visual information processing in the primatepsilas visual cortex has the potential for ultra-fast object recognition that will outperform current, computer vision-based methods.
    Biomedical Circuits and Systems Conference, 2008. BioCAS 2008. IEEE; 12/2008
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    ABSTRACT: In this paper we report a size and position invariant human posture recognition algorithm. The algorithm employs a simplified line segment Hausdorff distance classification and uses projection histograms to achieve size and position invariance. Compared to other existing method utilizing line segment Hausdorff distance, the proposed algorithm reduces the computation complexity by 36000 times, for our test images. Combining bio-inspired event-based image acquisition and hardware friendly feature extraction and classification algorithm will lead to a promising technology for use in wireless sensor network.
    Biomedical Circuits and Systems Conference, 2008. BioCAS 2008. IEEE; 12/2008
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    ABSTRACT: The cross-correlation function is an important yet computationally intensive processing step in many engineering applications such as wireless communication and object recognition. A neuromorphic approach to this function has been shown to facilitate implementation using a neural-based architecture. Using a custom designed array of silicon neurons on a compact, low-power chip, we demonstrate a cross-correlation system based on two half center oscillators. These preliminary results show the validity of this approach and could provide an elegant solution to wireless communication systems in the next generation of neuroprosthetic devices.
    Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on; 06/2008
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    ABSTRACT: We describe a circuit of two spiking neurons which extracts mathematically accurate cross-correlations from the signal inputs. It differs from prior circuits such as coincidence detectors or enhanced motion detectors in that it does not require an a priori fixed delay between input signals to be selected. The output, in the form of a differential spike histogram, displays a mathematical cross-correlation in the conventional correlation vs. time form.
    Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on; 06/2008
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    ABSTRACT: Complex cells in the visual cortex utilize a maximum (MAX) operation to pool the outputs of simple cells to achieve feature specificity and invariance. We demonstrate a biologically-plausible MAX network for nonlinear pooling in hardware, using a reconfigurable multichip address event representation based VLSI system. With this implementation we have shown that we can implement simple and advanced stages of visual processing on the same chip and are one step closer to constructing an autonomous, continuous-time, biologically- plausible hierarchical model of visual information processing using large-scale arrays of identical silicon neurons.
    Biomedical Circuits and Systems Conference, 2007. BIOCAS 2007. IEEE; 12/2007
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    ABSTRACT: For most prosthetic users, the break in sensation between their prosthesis and residual limb greatly limits their interaction with the environment. The sensation of temperature provides useful information for activities of daily living such as material discrimination, extreme temperature avoidance, and psychological comfort. This paper expands upon prior temperature displays in deploying a cosmetic covering that serves as a platform for embedded sensors. In order to utilize the sensors distributed throughout the cosmesis, a wireless sensing system is employed for communication between the sensors and the prosthetic hand. The authors show that users can identify the temperature ranges of the objects they grasp using the prosthetic hand with the sensing cosmesis on. The significance of relative temperatures is also shown as users report lower than actual temperature values due to prior exposure to higher temperature trials. As such, temperature is a valuable component of daily life, and further work towards temperature feedback for prosthetic users is warranted. An example would be the integration of multiple temperature points from sensor arrays embedded within prosthetic coverings so as to map the temperature of objects with greater resolution.
    Rehabilitation Robotics, 2007. ICORR 2007. IEEE 10th International Conference on; 07/2007
  • Fopefolu Folowosele, Jonathan Tapson, Ralph Etienne-Cummings
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    ABSTRACT: We describe wireless networking systems for close proximity biological sensors, as would be encountered in artificial skin. The sensors communicate to a "base station" that interprets the data and decodes its origin. Using a large bundle of ultra thin metal wires from the sensors to the "base station" introduces significant technological hurdles for both the construction and maintenance of the system. Fortunately, the Address Event Representation (AER) protocol provides an elegant and biomorphic method for transmitting many impulses (i.e. neural spikes) down a single wire/channel. However, AER does not communicate any sensory information within each spike, other that the address of the origination of the spike. Therefore, each sensor must provide a number of spikes to communicate its data, typically in the form of the inter-spike intervals or spike rate. Furthermore, complex circuitry is required to arbitrate access to the channel when multiple sensors communicate simultaneously, which results in spike delay. This error is exacerbated as the number of sensors per channel increases, mandating more channels and more wires. We contend that despite the effectiveness of the wire-based AER protocol, its natural evolution will be the wireless AER protocol. A wireless AER system: (1) does not require arbitration to handle multiple simultaneous access of the channel, (2) uses cross-correlation delay to encode sensor data in every spike (eliminating the error due to arbitration delay), and (3) can be reorganized and expanded with little consequence to the network. The system uses spread spectrum communications principles, implemented with a low-power integrate-and-fire neurons. This paper discusses the design, operation and capabilities of such a system. We show that integrate-and-fire neurons can be used to both decode the origination of each spike and extract the data contained within in. We also show that there are many technical obstacles to overcome before this version of wireless AER can be practical.
    Proc SPIE 06/2007;
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    Spie Newsroom. 01/2007;

Publication Stats

76 Citations
2.95 Total Impact Points

Institutions

  • 2008–2011
    • Johns Hopkins University
      • Department of Electrical and Computer Engineering
      Baltimore, MD, United States
    • University of Cape Town
      • Department of Electrical Engineering
      Kaapstad, Western Cape, South Africa