8 research outputs found
Review of medical data analysis based on spiking neural networks
Medical data mainly includes various types of biomedical signals and medical
images, which can be used by professional doctors to make judgments on
patients' health conditions. However, the interpretation of medical data
requires a lot of human cost and there may be misjudgments, so many scholars
use neural networks and deep learning to classify and study medical data, which
can improve the efficiency and accuracy of doctors and detect diseases early
for early diagnosis, etc. Therefore, it has a wide range of application
prospects. However, traditional neural networks have disadvantages such as high
energy consumption and high latency (slow computation speed). This paper
presents recent research on signal classification and disease diagnosis based
on a third-generation neural network, the spiking neuron network, using medical
data including EEG signals, ECG signals, EMG signals and MRI images. The
advantages and disadvantages of pulsed neural networks compared with
traditional networks are summarized and its development orientation in the
future is prospected
Plasticity and Adaptation in Neuromorphic Biohybrid Systems
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel \u201cbiohybrid\u201d experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering. \ua9 2020 The Author(s
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
Harnessing Neural Dynamics as a Computational Resource
Researchers study nervous systems at levels of scale spanning several orders of magnitude, both in terms of time and space. While some parts of the brain are well understood at specific levels of description, there are few overarching theories that systematically bridge low-level mechanism and high-level function. The Neural Engineering Framework (NEF) is an attempt at providing such a theory. The NEF enables researchers to systematically map dynamical systemsâcorresponding to some hypothesised brain functionâonto biologically constrained spiking neural networks. In this thesis, we present several extensions to the NEF that broaden both the range of neural resources that can be harnessed for spatiotemporal computation and the range of available biological constraints. Specifically, we suggest a method for harnessing the dynamics inherent in passive dendritic trees for computation, allowing us to construct single-layer spiking neural networks that, for some functions, achieve substantially lower errors than larger multi-layer networks. Furthermore, we suggest âtemporal tuningâ as a unifying approach to harnessing temporal resources for computation through time. This allows modellers to directly constrain networks to temporal tuning observed in nature, in ways not previously well-supported by the NEF.
We then explore specific examples of neurally plausible dynamics using these techniques. In particular, we propose a new âinformation erasureâ technique for constructing LTI systems generating temporal bases. Such LTI systems can be used to establish an optimal basis for spatiotemporal computation. We demonstrate how this captures âtime cellsâ that have been observed throughout the brain. As well, we demonstrate the viability of our extensions by constructing an adaptive filter model of the cerebellum that successfully reproduces key features of eyeblink conditioning observed in neurobiological experiments.
Outside the cognitive sciences, our work can help exploit resources available on existing neuromorphic computers, and inform future neuromorphic hardware design. In machine learning, our spatiotemporal NEF populations map cleanly onto the Legendre Memory Unit (LMU), a promising artificial neural network architecture for stream-to-stream processing that outperforms competing approaches. We find that one of our LTI systems derived through âinformation erasureâ may serve as a computationally less expensive alternative to the LTI system commonly used in the LMU
A cortical model of object perception based on Bayesian networks and belief propagation.
Evidence suggests that high-level feedback plays an important role in visual perception by shaping
the response in lower cortical levels (Sillito et al. 2006, Angelucci and Bullier 2003, Bullier
2001, Harrison et al. 2007). A notable example of this is reflected by the retinotopic activation
of V1 and V2 neurons in response to illusory contours, such as Kanizsa figures, which has been
reported in numerous studies (Maertens et al. 2008, Seghier and Vuilleumier 2006, Halgren et al.
2003, Lee 2003, Lee and Nguyen 2001). The illusory contour activity emerges first in lateral
occipital cortex (LOC), then in V2 and finally in V1, strongly suggesting that the response is
driven by feedback connections. Generative models and Bayesian belief propagation have been
suggested to provide a theoretical framework that can account for feedback connectivity, explain
psychophysical and physiological results, and map well onto the hierarchical distributed
cortical connectivity (Friston and Kiebel 2009, Dayan et al. 1995, Knill and Richards 1996,
Geisler and Kersten 2002, Yuille and Kersten 2006, Deneve 2008a, George and Hawkins 2009,
Lee and Mumford 2003, Rao 2006, Litvak and Ullman 2009, Steimer et al. 2009).
The present study explores the role of feedback in object perception, taking as a starting point
the HMAX model, a biologically inspired hierarchical model of object recognition (Riesenhuber
and Poggio 1999, Serre et al. 2007b), and extending it to include feedback connectivity.
A Bayesian network that captures the structure and properties of the HMAX model is
developed, replacing the classical deterministic view with a probabilistic interpretation. The
proposed model approximates the selectivity and invariance operations of the HMAX model
using the belief propagation algorithm. Hence, the model not only achieves successful feedforward
recognition invariant to position and size, but is also able to reproduce modulatory effects
of higher-level feedback, such as illusory contour completion, attention and mental imagery.
Overall, the model provides a biophysiologically plausible interpretation, based on state-of-theart
probabilistic approaches and supported by current experimental evidence, of the interaction
between top-down global feedback and bottom-up local evidence in the context of hierarchical
object perception
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