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Computer Modelling of Neuronal Interactions in the StriatumHjorth, Johannes January 2009 (has links)
Large parts of the cortex and the thalamus project into the striatum,which serves as the input stage of the basal ganglia. Information isintegrated in the striatal neural network and then passed on, via themedium spiny (MS) projection neurons, to the output stages of thebasal ganglia. In addition to the MS neurons there are also severaltypes of interneurons in the striatum, such as the fast spiking (FS)interneurons. I focused my research on the FS neurons, which formstrong inhibitory synapses onto the MS neurons. These striatal FSneurons are sparsely connected by electrical synapses (gap junctions),which are commonly presumed to synchronise their activity.Computational modelling with the GENESIS simulator was used toinvestigate the effect of gap junctions on a network of synapticallydriven striatal FS neurons. The simulations predicted a reduction infiring frequency dependent on the correlation between synaptic inputsto the neighbouring neurons, but only a slight synchronisation. Thegap junction effects on modelled FS neurons showing sub-thresholdoscillations and stuttering behaviour confirm these results andfurther indicate that hyperpolarising inputs might regulate the onsetof stuttering.The interactions between MS and FS neurons were investigated byincluding a computer model of the MS neuron. The hypothesis was thatdistal GABAergic input would lower the amplitude of back propagatingaction potentials, thereby reducing the calcium influx in thedendrites. The model verified this and further predicted that proximalGABAergic input controls spike timing, but not the amplitude ofdendritic calcium influx after initiation.Connecting models of neurons written in different simulators intonetworks raised technical problems which were resolved by integratingthe simulators within the MUSIC framework. This thesis discusses theissues encountered by using this implementation and gives instructionsfor modifying MOOSE scripts to use MUSIC and provides guidelines forachieving compatibility between MUSIC and other simulators.This work sheds light on the interactions between striatal FS and MSneurons. The quantitative results presented could be used to developa large scale striatal network model in the future, which would beapplicable to both the healthy and pathological striatum. / QC 20100720
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The Ordinal Serial Encoding Model: Serial Memory in Spiking NeuronsChoo, Feng-Xuan January 2010 (has links)
In a world dominated by temporal order, memory capable of processing, encoding and subsequently recalling ordered information is very important. Over the decades this memory, known as serial memory, has been extensively studied, and its effects are well known. Many models have also been developed, and while these models are able to reproduce the behavioural effects observed in human recall studies, they are not always implementable in a biologically plausible manner. This thesis presents the Ordinal Serial Encoding model, a model inspired by biology and designed with a broader view of general cognitive architectures in mind. This model has the advantage of simplicity, and we show how neuro-plausibility can be achieved by employing the principles of the Neural Engineering Framework in the model’s design. Additionally, we demonstrate that not only is the model able to closely mirror human performance in various recall tasks, but the behaviour of the model is itself a consequence of the underlying neural architecture.
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The Attentional Routing Circuit: A Neural Model of Attentional Modulation and Control of Functional ConnectivityBobier, Bruce January 2011 (has links)
Several decades of physiology, imaging and psychophysics research on attention has generated an enormous amount of data describing myriad forms of attentional effects. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail. However, there remains a need for neurally detailed mechanistic models of attention that connect more directly with various kinds of experimental data -- behavioural, psychophysical, neurophysiological, and neuroanatomical -- and that provide experimentally testable predictions.
Research has been conducted that aims to identify neurally consistent principles that underlie selective attentional processing in cortex. The research specifically focuses on describing the functional mechanisms of attentional routing in a large-scale hierarchical model, and demonstrating the biological plausibility of the model by presenting a spiking neuron implementation that can account for a variety of attentional effects.
The thesis begins by discussing several significant physiological effects of attention, and prominent brain areas involved in selective attention, which provide strong constraints for developing a model of attentional processing in cortex. Several prominent models of attention are then discussed, from which a set of common limitations in existing models is assembled that need to be addressed by the proposed model. One central limitation is that, for many existing models, it remains to be demonstrated that their computations can be plausibly performed in spiking neurons. Further, few models address attentional effects for more than a single neuron or single cortical area. And finally, few are able to account for different forms of attentional modulation in a single detailed model. These and other limitations are addressed by the Attentional Routing Circuit (ARC) proposed in this thesis.
The presentation of the ARC begins with the proposal of a high-level mathematical model for selective routing in the visual hierarchy. The mathematical model is used to demonstrate that the suggested mechanisms allow for scale- and position-invariant representations of attended stimuli to be formed, and provides a functional context for interpreting detailed physiological effects.
To evaluate the model's biological plausibility, the Neural Engineering Framework (NEF) is used to implement the ARC as a detailed spiking neuron model. Simulation results are then presented which demonstrate that selective routing can be performed efficiently in spiking neurons in a way that is consistent with the mathematical model. The neural circuitry for computing and applying attentional control signals in the ARC is then mapped on to neural populations in specific cortical laminae using known anatomical interlaminar and interareal connections to support the plausibility of its cortical implementation.
The model is then tested for its ability to account for several forms of attentional modulation that have been reported in neurophysiological experiments. Three experiments of attention in macaque are simulated using the ARC, and for each of these experiments, the model is shown to be quantitatively consistent with measured data. Specifically, a study by Womelsdorf et al. (2008) demonstrates that spatial shifts of attention result in a shifting and shrinking of receptive fields depending on the target's position. An experiment by Treue and Martinez-Trujllo (1999) reports that attentional shifts between receptive field stimuli produce a multiplicative scaling of responses, but do not affect the neural tuning sensitivity. Finally, a study by Lee and Maunsell (2010) demonstrates that attentional shifts result in a multiplicative scaling of neural contrast-response functions that is consistent with a response-gain effect. The model accounts for each of these experimentally observed attentional effects using a single mechanism for selectively processing attended stimuli.
In conclusion, it is suggested that the ARC is distinguished from previous models by providing a unifying interpretation of attentional effects at the level of single cells, neural populations, cortical areas, and over the bulk of the visual hierarchy. As well, there are several advantages of the ARC over previous models, including: (1) scalability to larger implementations without affecting the model's principles; (2) a significant increase in biological plausibility; (3) the ability to account for experimental results at multiple levels of analysis; (4) a detailed description of the model's anatomical substrate; (5) the ability to perform selective routing while preserving biological detail; and (6) generating a variety of experimentally testable predictions.
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Information processing in the Striatum : a computational studyHjorth, Johannes January 2006 (has links)
<p>The basal ganglia form an important structure centrally placed in the brain. They receive input from motor, associative and limbic areas, and produce output mainly to the thalamus and the brain stem. The basal ganglia have been implied in cognitive and motor functions. One way to understand the basal ganglia is to take a look at the diseases that affect them. Both Parkinson's disease and Huntington's disease with their motor problems are results of malfunctioning basal ganglia. There are also indications that these diseases affect cognitive functions. Drug addiction is another example that involves this structure, which is also important for motivation and selection of behaviour.</p><p>In this licentiate thesis I am laying the groundwork for a detailed model of the striatum, which is the input stage of the basal ganglia. The striatum receives glutamatergic input from the cortex and thalamus, as well as dopaminergic input from substantia nigra. The majority of the neurons in the striatum are medium spiny (MS) projection neurons that project mainly to globus pallidus but also to other neurons in the striatum and to both dopamine producing and GABAergic neurons in substantia nigra. In addition to the MS neurons there are fast spiking (FS) interneurons that are in a position to regulate the firing of the MS neurons. These FS neurons are few, but connected into large networks through electrical synapses that could synchronise their effect. By forming strong inhibitory synapses on the MS neurons the FS neurons have a powerful influence on the striatal output. The inhibitory output of the basal ganglia on the thalamus is believed to keep prepared motor commands on hold, but once one of them is disinhibited, then the selected motor command is executed. This disinhibition is initiated in the striatum by the MS neurons.</p><p>Both MS and FS neurons are active during so called up-states, which are periods of elevated cortical input to striatum. Here I have studied the FS neurons and their ability to detect such up-states. This is important because FS neurons can delay spikes in MS neurons and the time between up-state onset and the first spike in the MS neurons is correlated with the amount of calcium entering the MS neuron, which in turn might have implications for plasticity and learning of new behaviours. The effect of different combinations of electrical couplings between two FS neurons has been tested, where the location, number and strength of these gap junctions have been varied. I studied both the ability of the FS neurons to fire action potentials during the up-state, and the synchronisation between neighbouring FS neurons due to electrical coupling. I found that both proximal and distal gap junctions synchronised the firing, but the distal gap junctions did not have the same temporal precision. The ability of the FS neurons to detect an up-state was affected by whether the neighbouring FS neuron also received up-state input or not. This effect was more pronounced for distal gap junctions than proximal ones, due to a stronger shunting effect of distal gap junctions when the dendrites were synaptically activated.</p><p>We have also performed initial stochastic simulations of the Ca<sup>2+</sup>-calmodulin-dependent protein kinase II (CaMKII). The purpose here is to build the knowledge as well as the tools necessary for biochemical simulations of intracellular processes that are important for plasticity in the MS neurons. The simulated biochemical pathways will then be integrated into an existing model of a full MS neuron. Another venue to explore is to build striatal network models consisting of MS and FS neurons and using experimental data of the striatal microcircuitry. With these different approaches we will improve our understanding of striatal information processing.</p>
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Μελέτη της λειτουργικής υπόθεσης της επιλογής δράσεως από τα βασικά γάγγλια / Study of the functional hypothesis of action selection in the basal gangliaΠεράκης, Δημήτρης 29 June 2007 (has links)
Μελέτη της υπόθεσης ότι τα βασικά γάγγλια, μία κεντρική δομή του εγκεφάλου, συμμετέχει ενεργά στην επιλογή της κατάλληλης δράσης. Προσπάθεια μοντελοποίησης της λειτουργίας των βασικών γαγγλίων κια προσομοίωση παθολογικών καταστάσεων του ανθρώπινου εγκεφάλου. / Stydy of the hypothesis that the basal ganglia, a major structure in the mamalian brain, plays a crusial role in the selection of the appropriate action. In this direction we try to simulate the functionality of the basal ganglia as well the pathophysiological conditions (Parkinson, Huntington disease) related to them.
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The Ordinal Serial Encoding Model: Serial Memory in Spiking NeuronsChoo, Feng-Xuan January 2010 (has links)
In a world dominated by temporal order, memory capable of processing, encoding and subsequently recalling ordered information is very important. Over the decades this memory, known as serial memory, has been extensively studied, and its effects are well known. Many models have also been developed, and while these models are able to reproduce the behavioural effects observed in human recall studies, they are not always implementable in a biologically plausible manner. This thesis presents the Ordinal Serial Encoding model, a model inspired by biology and designed with a broader view of general cognitive architectures in mind. This model has the advantage of simplicity, and we show how neuro-plausibility can be achieved by employing the principles of the Neural Engineering Framework in the model’s design. Additionally, we demonstrate that not only is the model able to closely mirror human performance in various recall tasks, but the behaviour of the model is itself a consequence of the underlying neural architecture.
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Modeling prediction and pattern recognition in the early visual and olfactory systemsKaplan, Bernhard January 2015 (has links)
Our senses are our mind's window to the outside world and determine how we perceive our environment.Sensory systems are complex multi-level systems that have to solve a multitude of tasks that allow us to understand our surroundings.However, questions on various levels and scales remain to be answered ranging from low-level neural responses to behavioral functions on the highest level.Modeling can connect different scales and contribute towards tackling these questions by giving insights into perceptual processes and interactions between processing stages.In this thesis, numerical simulations of spiking neural networks are used to deal with two essential functions that sensory systems have to solve: pattern recognition and prediction.The focus of this thesis lies on the question as to how neural network connectivity can be used in order to achieve these crucial functions.The guiding ideas of the models presented here are grounded in the probabilistic interpretation of neural signals, Hebbian learning principles and connectionist ideas.The main results are divided into four parts.The first part deals with the problem of pattern recognition in a multi-layer network inspired by the early mammalian olfactory system with biophysically detailed neural components.Learning based on Hebbian-Bayesian principles is used to organize the connectivity between and within areas and is demonstrated in behaviorally relevant tasks.Besides recognition of artificial odor patterns, phenomena like concentration invariance, noise robustness, pattern completion and pattern rivalry are investigated.It is demonstrated that learned recurrent cortical connections play a crucial role in achieving pattern recognition and completion.The second part is concerned with the prediction of moving stimuli in the visual system.The problem of motion-extrapolation is studied using different recurrent connectivity patterns.The main result shows that connectivity patterns taking the tuning properties of cells into account can be advantageous for solving the motion-extrapolation problem.The third part focuses on the predictive or anticipatory response to an approaching stimulus.Inspired by experimental observations, particle filtering and spiking neural network frameworks are used to address the question as to how stimulus information is transported within a motion sensitive network.In particular, the question if speed information is required to build up a trajectory dependent anticipatory response is studied by comparing different network connectivities.Our results suggest that in order to achieve a dependency of the anticipatory response to the trajectory length, a connectivity that uses both position and speed information seems necessary.The fourth part combines the self-organization ideas from the first part with motion perception as studied in the second and third parts.There, the learning principles used in the olfactory system model are applied to the problem of motion anticipation in visual perception.Similarly to the third part, different connectivities are studied with respect to their contribution to anticipate an approaching stimulus.The contribution of this thesis lies in the development and simulation of large-scale computational models of spiking neural networks solving prediction and pattern recognition tasks in biophysically plausible frameworks. / <p>QC 20150504</p>
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The Attentional Routing Circuit: A Neural Model of Attentional Modulation and Control of Functional ConnectivityBobier, Bruce January 2011 (has links)
Several decades of physiology, imaging and psychophysics research on attention has generated an enormous amount of data describing myriad forms of attentional effects. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail. However, there remains a need for neurally detailed mechanistic models of attention that connect more directly with various kinds of experimental data -- behavioural, psychophysical, neurophysiological, and neuroanatomical -- and that provide experimentally testable predictions.
Research has been conducted that aims to identify neurally consistent principles that underlie selective attentional processing in cortex. The research specifically focuses on describing the functional mechanisms of attentional routing in a large-scale hierarchical model, and demonstrating the biological plausibility of the model by presenting a spiking neuron implementation that can account for a variety of attentional effects.
The thesis begins by discussing several significant physiological effects of attention, and prominent brain areas involved in selective attention, which provide strong constraints for developing a model of attentional processing in cortex. Several prominent models of attention are then discussed, from which a set of common limitations in existing models is assembled that need to be addressed by the proposed model. One central limitation is that, for many existing models, it remains to be demonstrated that their computations can be plausibly performed in spiking neurons. Further, few models address attentional effects for more than a single neuron or single cortical area. And finally, few are able to account for different forms of attentional modulation in a single detailed model. These and other limitations are addressed by the Attentional Routing Circuit (ARC) proposed in this thesis.
The presentation of the ARC begins with the proposal of a high-level mathematical model for selective routing in the visual hierarchy. The mathematical model is used to demonstrate that the suggested mechanisms allow for scale- and position-invariant representations of attended stimuli to be formed, and provides a functional context for interpreting detailed physiological effects.
To evaluate the model's biological plausibility, the Neural Engineering Framework (NEF) is used to implement the ARC as a detailed spiking neuron model. Simulation results are then presented which demonstrate that selective routing can be performed efficiently in spiking neurons in a way that is consistent with the mathematical model. The neural circuitry for computing and applying attentional control signals in the ARC is then mapped on to neural populations in specific cortical laminae using known anatomical interlaminar and interareal connections to support the plausibility of its cortical implementation.
The model is then tested for its ability to account for several forms of attentional modulation that have been reported in neurophysiological experiments. Three experiments of attention in macaque are simulated using the ARC, and for each of these experiments, the model is shown to be quantitatively consistent with measured data. Specifically, a study by Womelsdorf et al. (2008) demonstrates that spatial shifts of attention result in a shifting and shrinking of receptive fields depending on the target's position. An experiment by Treue and Martinez-Trujllo (1999) reports that attentional shifts between receptive field stimuli produce a multiplicative scaling of responses, but do not affect the neural tuning sensitivity. Finally, a study by Lee and Maunsell (2010) demonstrates that attentional shifts result in a multiplicative scaling of neural contrast-response functions that is consistent with a response-gain effect. The model accounts for each of these experimentally observed attentional effects using a single mechanism for selectively processing attended stimuli.
In conclusion, it is suggested that the ARC is distinguished from previous models by providing a unifying interpretation of attentional effects at the level of single cells, neural populations, cortical areas, and over the bulk of the visual hierarchy. As well, there are several advantages of the ARC over previous models, including: (1) scalability to larger implementations without affecting the model's principles; (2) a significant increase in biological plausibility; (3) the ability to account for experimental results at multiple levels of analysis; (4) a detailed description of the model's anatomical substrate; (5) the ability to perform selective routing while preserving biological detail; and (6) generating a variety of experimentally testable predictions.
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Towards a Brain-inspired Information Processing System: Modelling and Analysis of Synaptic DynamicsEl-Laithy, Karim 12 January 2012 (has links) (PDF)
Biological neural systems (BNS) in general and the central nervous system (CNS) specifically
exhibit a strikingly efficient computational power along with an extreme flexible and adaptive basis
for acquiring and integrating new knowledge. Acquiring more insights into the actual mechanisms
of information processing within the BNS and their computational capabilities is a core objective
of modern computer science, computational sciences and neuroscience. Among the main reasons
of this tendency to understand the brain is to help in improving the quality of life of people suffer
from loss (either partial or complete) of brain or spinal cord functions. Brain-computer-interfaces
(BCI), neural prostheses and other similar approaches are potential solutions either to help these
patients through therapy or to push the progress in rehabilitation. There is however a significant
lack of knowledge regarding the basic information processing within the CNS. Without a better
understanding of the fundamental operations or sequences leading to cognitive abilities, applications
like BCI or neural prostheses will keep struggling to find a proper and systematic way to
help patients in this regard. In order to have more insights into these basic information processing
methods, this thesis presents an approach that makes a formal distinction between the essence
of being intelligent (as for the brain) and the classical class of artificial intelligence, e.g. with
expert systems. This approach investigates the underlying mechanisms allowing the CNS to be
capable of performing a massive amount of computational tasks with a sustainable efficiency and
flexibility. This is the essence of being intelligent, i.e. being able to learn, adapt and to invent.
The approach used in the thesis at hands is based on the hypothesis that the brain or specifically a
biological neural circuitry in the CNS is a dynamic system (network) that features emergent capabilities.
These capabilities can be imported into spiking neural networks (SNN) by emulating the
dynamic neural system. Emulating the dynamic system requires simulating both the inner workings
of the system and the framework of performing the information processing tasks. Thus, this
work comprises two main parts. The first part is concerned with introducing a proper and a novel
dynamic synaptic model as a vital constitute of the inner workings of the dynamic neural system.
This model represents a balanced integration between the needed biophysical details and being
computationally inexpensive. Being a biophysical model is important to allow for the abilities of
the target dynamic system to be inherited, and being simple is needed to allow for further implementation
in large scale simulations and for hardware implementation in the future. Besides, the
energy related aspects of synaptic dynamics are studied and linked to the behaviour of the networks
seeking for stable states of activities. The second part of the thesis is consequently concerned with
importing the processing framework of the dynamic system into the environment of SNN. This
part of the study investigates the well established concept of binding by synchrony to solve the information binding problem and to proposes the concept of synchrony states within SNN. The
concepts of computing with states are extended to investigate a computational model that is based
on the finite-state machines and reservoir computing. Biological plausible validations of the introduced
model and frameworks are performed. Results and discussions of these validations indicate
that this study presents a significant advance on the way of empowering the knowledge about the
mechanisms underpinning the computational power of CNS. Furthermore it shows a roadmap on
how to adopt the biological computational capabilities in computation science in general and in
biologically-inspired spiking neural networks in specific. Large scale simulations and the development
of neuromorphic hardware are work-in-progress and future work. Among the applications
of the introduced work are neural prostheses and bionic automation systems.
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Computer Simulation of the Neural Control of Locomotion in the Cat and the SalamanderHarischandra, Nalin January 2011 (has links)
Locomotion is an integral part of a whole range of animal behaviours. The basic rhythm for locomotion in vertebrates has been shown to arise from local networks residing in the spinal cord and these networks are known as central pattern generators (CPG). However, during the locomotion, these centres are constantly interacting with the sensory feedback signals coming from muscles, joints and peripheral skin receptors in order to adapt the stepping or swimming to varying environmental conditions. Conceptual models of vertebrate locomotion have been constructed using mathematical models of locomotor subsystems based on the neurophysiological evidence obtained primarily in the cat and the salamander, an amphibian with a sprawling posture. Such models provide opportunity for studying the key elements in the transition from aquatic to terrestrial locomotion. Several aspects of locomotor control using the cat or the salamander as an animal model have been investigated employing computer simulations and here we use the same approach to address a number of questions or/and hypotheses related to rhythmic locomotion in quadrupeds. Some of the involved questions are, the role of mechanical linkage during deafferented walking, finding inherent stabilities/instabilities of muscle-joint interactions during normal walking and estimating phase dependent controlability of muscle action over joints. Also we investigate limb and body coordination for different gaits, use of side-stepping in front limbs for turning and the role of sensory feedback in gait generation and transitions in salamanders. This thesis presents the basics of the biologically realistic models of cat and salamander locomotion and summarizes computational methods in modeling quadruped locomotor subsystems such as CPG, limb muscles and sensory pathways. In the case of cat hind limb, we conclude that the mechanical linkages between the legs play a major role in producing the alternating gait. In another experiment we use the model to identify open-loop linear transfer functions between muscle activations and joint angles while ongoing locomotion. We hypothesize that the musculo-skeletal system for locomotion in animals, at least in cats, operates under critically damped condition. The 3D model of the salamander is successfully used to mimic locomotion on level ground and in water. We compare the walking gait with the trotting gait in simulations. We also found that for turning, the use of side-stepping alone or in combination with trunk bending is more effective than the use of trunk bending alone. The same model together with a more realistic CPG composed of spiking neurons was used to investigate the role of sensory feedback in gait generation and transition. We found that the proprioceptive sensory inputs are essential in obtaining the walking gait, whereas the trotting gait is more under central (CPG) influence compared to that of the peripheral or sensory feedback. This thesis work sheds light on understanding the neural control mechanisms behind vertebrate locomotion. Additionally, both neuro-mechanical models can be used for further investigations in finding new control algorithms which give robust, adaptive, efficient and realistic stepping in each leg, which would be advantageous since it can be implemented on a controller of a quadruped-robotic device. / This work is Funded by Swedish International Development cooperation Agency (SIDA). QC 20111110
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