4 research outputs found
A Supervised STDP-based Training Algorithm for Living Neural Networks
Neural networks have shown great potential in many applications like speech
recognition, drug discovery, image classification, and object detection. Neural
network models are inspired by biological neural networks, but they are
optimized to perform machine learning tasks on digital computers. The proposed
work explores the possibilities of using living neural networks in vitro as
basic computational elements for machine learning applications. A new
supervised STDP-based learning algorithm is proposed in this work, which
considers neuron engineering constrains. A 74.7% accuracy is achieved on the
MNIST benchmark for handwritten digit recognition.Comment: 5 pages, 3 figures, Accepted by ICASSP 201
Design and development of opto-neural processors for simulation of neural networks trained in image detection for potential implementation in hybrid robotics
Neural networks have been employed for a wide range of processing
applications like image processing, motor control, object detection and many
others. Living neural networks offer advantages of lower power consumption,
faster processing, and biological realism. Optogenetics offers high spatial and
temporal control over biological neurons and presents potential in training
live neural networks. This work proposes a simulated living neural network
trained indirectly by backpropagating STDP based algorithms using precision
activation by optogenetics achieving accuracy comparable to traditional neural
network training algorithms
Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells
Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch