Spiking neural networks have been used to investigate the mechanisms of processing
in biological neural circuits or to propose hypotheses that can be tested in exper-
iments. Because of their biological plausibility and event-based information trans-
mission, Spiking Neural Networks (SNNs) have suggested as alternatives to Articial
Neural Networks for pattern recognition, classication and function approximation
problems with fewer neurons. In machine learning, SNNs has been shown to be able
to solve pattern and robotic control. For SNNs to be used for such problems, they
must incorporate some mechanism for learning. Current methods to train SNNs use
learning algorithms which adjust the synaptic weights according to an update rule.
In most cases the weights are modied directly. In potential applications such as
driving plasticity in neural culture (in-vitro) and training neuromorphic chips the
directly manipulation of synaptic weights is not possible. Therefore, indirect algo-
rithms, which cause the SNNs to learn based on some biological learning mechanisms
using stimulation of neurons oer signicant advantages over the existing algorithm
for these real world applications.
Indirect algorithms train the neural network by using external stimuli to modulate
the synaptic strengths of a neural network according to synapses intrinsic mechanisms
for plasticity. The training algorithms have been demonstrated in both Integrate and
Fire neurons and more biologically realistic neural networks. In this thesis, four indi-
rect methods to drive the synaptic weights to its desired value in a network through Spike Time Dependent Plasticity (STDP) are developed: Indirect Perturbation, In-
direct Stochastic Gradient, Indirect ReSuMe, and Indirect Training with Supervised
Teaching Signals. These algorithms are used to solve the temporal and spatial input-
output mapping problem using temporal coding. The other type of problem is to
mapping input output ring rates using rate coding.
To test the algorithms, SNNs are used to control both virtual and real world
robots. For the real world robots with SNNs, known and Neurorobots, two types
of robot localization techniques are used: Optitrack, using ceiling mounted cameras
and onboard markers, and embedded cameras. Both small and large SNNs with
biologically realistic neurons are used to drive the neurorobots are modeled with
input coming from Optitrack or the cameras with GPU accelerated SNN simulator.
The results show that the indirect perturbation and indirect stochastic gradient
algorithms can train an SNN to control the robot to nd targets and avoid obstacles
even in the presence of sensor noise. The results also show that indirect training with
supervised training signals algorithm can train a feedforward network with 1000s of
neurons to process and output the correct movement commands to localize a target
using from real time images captured from an embedded camera. Finally. an indirect
version of the Remote Supervised Method (ReSuMe) algorithm was developed using
a more biologically realistic form of Spike-Timing Dependent Plasticity to produce a
specic temporal pattern of spiking from a group of neurons. The indirect algorithms
developed in this thesis may eventually allow the ability to train in vitro and in
vivo biological circuits to perform specic tasks using patterns of electrical or light
stimulation.
","abstract_html":"<p>Spiking neural networks have been used to investigate the mechanisms of processing</p><p>in biological neural circuits or to propose hypotheses that can be tested in exper-</p><p>iments. Because of their biological plausibility and event-based information trans-</p><p>mission, Spiking Neural Networks (SNNs) have suggested as alternatives to Articial</p><p>Neural Networks for pattern recognition, classication and function approximation</p><p>problems with fewer neurons. In machine learning, SNNs has been shown to be able</p><p>to solve pattern and robotic control. For SNNs to be used for such problems, they</p><p>must incorporate some mechanism for learning. Current methods to train SNNs use</p><p>learning algorithms which adjust the synaptic weights according to an update rule.</p><p>In most cases the weights are modied directly. In potential applications such as</p><p>driving plasticity in neural culture (in-vitro) and training neuromorphic chips the</p><p>directly manipulation of synaptic weights is not possible. Therefore, indirect algo-</p><p>rithms, which cause the SNNs to learn based on some biological learning mechanisms</p><p>using stimulation of neurons oer signicant advantages over the existing algorithm</p><p>for these real world applications.</p><p>Indirect algorithms train the neural network by using external stimuli to modulate</p><p>the synaptic strengths of a neural network according to synapses intrinsic mechanisms</p><p>for plasticity. The training algorithms have been demonstrated in both Integrate and</p><p>Fire neurons and more biologically realistic neural networks. In this thesis, four indi-</p><p>rect methods to drive the synaptic weights to its desired value in a network through Spike Time Dependent Plasticity (STDP) are developed: Indirect Perturbation, In-</p><p>direct Stochastic Gradient, Indirect ReSuMe, and Indirect Training with Supervised</p><p>Teaching Signals. These algorithms are used to solve the temporal and spatial input-</p><p>output mapping problem using temporal coding. The other type of problem is to</p><p>mapping input output ring rates using rate coding.</p><p>To test the algorithms, SNNs are used to control both virtual and real world</p><p>robots. For the real world robots with SNNs, known and Neurorobots, two types</p><p>of robot localization techniques are used: Optitrack, using ceiling mounted cameras</p><p>and onboard markers, and embedded cameras. Both small and large SNNs with</p><p>biologically realistic neurons are used to drive the neurorobots are modeled with</p><p>input coming from Optitrack or the cameras with GPU accelerated SNN simulator.</p><p>The results show that the indirect perturbation and indirect stochastic gradient</p><p>algorithms can train an SNN to control the robot to nd targets and avoid obstacles</p><p>even in the presence of sensor noise. The results also show that indirect training with</p><p>supervised training signals algorithm can train a feedforward network with 1000s of</p><p>neurons to process and output the correct movement commands to localize a target</p><p>using from real time images captured from an embedded camera. Finally. an indirect</p><p>version of the Remote Supervised Method (ReSuMe) algorithm was developed using</p><p>a more biologically realistic form of Spike-Timing Dependent Plasticity to produce a</p><p>specic temporal pattern of spiking from a group of neurons. The indirect algorithms</p><p>developed in this thesis may eventually allow the ability to train in vitro and in</p><p>vivo biological circuits to perform specic tasks using patterns of electrical or light</p><p>stimulation.</p>","abstract_has_math":false,"creators":["Zhang, Xu"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Henriquez, Craig S"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-24T02:07:10Z","subjects":["Artificial intelligence","Neurosciences","Robotics","Indirect Training","Learning Mechanism","Neurorobotics","Spiking Neural Networks","STDP"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10161/14362","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Henriquez, Craig S"]},{"key":"dc:creator","label":"Author","values":["Zhang, Xu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-05-16T17:27:08Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-05-16T17:27:08Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial intelligence","Neurosciences","Robotics","Indirect Training","Learning Mechanism","Neurorobotics","Spiking Neural Networks","STDP"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10161/14362"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Spiking neural networks have been used to investigate the mechanisms of processing
in biological neural circuits or to propose hypotheses that can be tested in exper-
iments. Because of their biological plausibility and event-based information trans-
mission, Spiking Neural Networks (SNNs) have suggested as alternatives to Articial
Neural Networks for pattern recognition, classication and function approximation
problems with fewer neurons. In machine learning, SNNs has been shown to be able
to solve pattern and robotic control. For SNNs to be used for such problems, they
must incorporate some mechanism for learning. Current methods to train SNNs use
learning algorithms which adjust the synaptic weights according to an update rule.
In most cases the weights are modied directly. In potential applications such as
driving plasticity in neural culture (in-vitro) and training neuromorphic chips the
directly manipulation of synaptic weights is not possible. Therefore, indirect algo-
rithms, which cause the SNNs to learn based on some biological learning mechanisms
using stimulation of neurons oer signicant advantages over the existing algorithm
for these real world applications.
Indirect algorithms train the neural network by using external stimuli to modulate
the synaptic strengths of a neural network according to synapses intrinsic mechanisms
for plasticity. The training algorithms have been demonstrated in both Integrate and
Fire neurons and more biologically realistic neural networks. In this thesis, four indi-
rect methods to drive the synaptic weights to its desired value in a network through Spike Time Dependent Plasticity (STDP) are developed: Indirect Perturbation, In-
direct Stochastic Gradient, Indirect ReSuMe, and Indirect Training with Supervised
Teaching Signals. These algorithms are used to solve the temporal and spatial input-
output mapping problem using temporal coding. The other type of problem is to
mapping input output ring rates using rate coding.
To test the algorithms, SNNs are used to control both virtual and real world
robots. For the real world robots with SNNs, known and Neurorobots, two types
of robot localization techniques are used: Optitrack, using ceiling mounted cameras
and onboard markers, and embedded cameras. Both small and large SNNs with
biologically realistic neurons are used to drive the neurorobots are modeled with
input coming from Optitrack or the cameras with GPU accelerated SNN simulator.
The results show that the indirect perturbation and indirect stochastic gradient
algorithms can train an SNN to control the robot to nd targets and avoid obstacles
even in the presence of sensor noise. The results also show that indirect training with
supervised training signals algorithm can train a feedforward network with 1000s of
neurons to process and output the correct movement commands to localize a target
using from real time images captured from an embedded camera. Finally. an indirect
version of the Remote Supervised Method (ReSuMe) algorithm was developed using
a more biologically realistic form of Spike-Timing Dependent Plasticity to produce a
specic temporal pattern of spiking from a group of neurons. The indirect algorithms
developed in this thesis may eventually allow the ability to train in vitro and in
vivo biological circuits to perform specic tasks using patterns of electrical or light
stimulation.
"]},{"key":"dc:title","label":"Title","values":["Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications"]}]}],"canonical_facts":{"dc:contributor.advisor":["Henriquez, Craig S"],"dc:creator":["Zhang, Xu"],"dc:date.accessioned":["2017-05-16T17:27:08Z"],"dc:date.available":["2017-05-16T17:27:08Z"],"dc:date.issued":["2017"],"dc:description.abstract":["Spiking neural networks have been used to investigate the mechanisms of processing
in biological neural circuits or to propose hypotheses that can be tested in exper-
iments. Because of their biological plausibility and event-based information trans-
mission, Spiking Neural Networks (SNNs) have suggested as alternatives to Articial
Neural Networks for pattern recognition, classication and function approximation
problems with fewer neurons. In machine learning, SNNs has been shown to be able
to solve pattern and robotic control. For SNNs to be used for such problems, they
must incorporate some mechanism for learning. Current methods to train SNNs use
learning algorithms which adjust the synaptic weights according to an update rule.
In most cases the weights are modied directly. In potential applications such as
driving plasticity in neural culture (in-vitro) and training neuromorphic chips the
directly manipulation of synaptic weights is not possible. Therefore, indirect algo-
rithms, which cause the SNNs to learn based on some biological learning mechanisms
using stimulation of neurons oer signicant advantages over the existing algorithm
for these real world applications.
Indirect algorithms train the neural network by using external stimuli to modulate
the synaptic strengths of a neural network according to synapses intrinsic mechanisms
for plasticity. The training algorithms have been demonstrated in both Integrate and
Fire neurons and more biologically realistic neural networks. In this thesis, four indi-
rect methods to drive the synaptic weights to its desired value in a network through Spike Time Dependent Plasticity (STDP) are developed: Indirect Perturbation, In-
direct Stochastic Gradient, Indirect ReSuMe, and Indirect Training with Supervised
Teaching Signals. These algorithms are used to solve the temporal and spatial input-
output mapping problem using temporal coding. The other type of problem is to
mapping input output ring rates using rate coding.
To test the algorithms, SNNs are used to control both virtual and real world
robots. For the real world robots with SNNs, known and Neurorobots, two types
of robot localization techniques are used: Optitrack, using ceiling mounted cameras
and onboard markers, and embedded cameras. Both small and large SNNs with
biologically realistic neurons are used to drive the neurorobots are modeled with
input coming from Optitrack or the cameras with GPU accelerated SNN simulator.
The results show that the indirect perturbation and indirect stochastic gradient
algorithms can train an SNN to control the robot to nd targets and avoid obstacles
even in the presence of sensor noise. The results also show that indirect training with
supervised training signals algorithm can train a feedforward network with 1000s of
neurons to process and output the correct movement commands to localize a target
using from real time images captured from an embedded camera. Finally. an indirect
version of the Remote Supervised Method (ReSuMe) algorithm was developed using
a more biologically realistic form of Spike-Timing Dependent Plasticity to produce a
specic temporal pattern of spiking from a group of neurons. The indirect algorithms
developed in this thesis may eventually allow the ability to train in vitro and in
vivo biological circuits to perform specic tasks using patterns of electrical or light
stimulation.
"],"dc:identifier.uri":["https://hdl.handle.net/10161/14362"],"dc:subject":["Artificial intelligence","Neurosciences","Robotics","Indirect Training","Learning Mechanism","Neurorobotics","Spiking Neural Networks","STDP"],"dc:title":["Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications"],"dc:type":["Dissertation"]},"updated_at":"2026-07-24T02:07:10Z"}