Giseli de Sousa | Universidade Federal de Santa Catarina - UFSC (Federal University of Santa Catarina) (original) (raw)
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Many theories of cerebellar function assume that long-term depression (LTD) of parallel fiber (PF... more Many theories of cerebellar function assume that long-term depression (LTD) of parallel fiber (PF) synapses ena-bles Purkinje cells (PCs) to learn to recognize PF activity patterns. According to the classic view, a PC can store and learn to distinguish PF activity patterns that have been presented repeatedly together with climbing fibre (CF) input to the cell. The resulting LTD of the PF synapses is often assumed to lead to a decreased rate of PC simple spike firing, a reduction in the inhibition of their target neurons in the deep cerebellar nuclei and thus an increased output from the cerebellum. We have recently shown by combining computer simulations with electro-physiological recordings in slices and in awake behaving mice that the readout of learned patterns in PCs may oper-ate in a fundamentally different way. Our simulations and
Previous studies have shown that the morphology of a neuron can affect its firing pattern [1,2]. ... more Previous studies have shown that the morphology of a neuron can affect its firing pattern [1,2]. Specifically, some neuronal morphologies tend to favour bursting, where short sequences of spikes are interspersed with pauses in firing [1,2]. This type of bursting behaviour has been observed in cerebellar Purkinje cells (PCs), and previous work on associative memory in PCs has shown that the generation of burst-pause sequences can be important for information storage in the cerebellum [3]. These results have implications for the coding of infor-mation in the brain, but they are specific to one particu-lar neuron with a highly specialised morphology. In this study we therefore use a general approach to optimise generic neuronal structures for pattern recognition, while analysing how their morphology influences their
Journal of Computational Neuroscience, 2014
Intelligent devices can be considered biological inspired mechanisms. These devices have the abil... more Intelligent devices can be considered biological inspired mechanisms. These devices have the ability to simulate characteristics and behaviors like those of living beings, by the modeling of their neural systems. To have a higher biological fidelity, the nervous system of these devices must implement neural models as close as possible to the biological neuron. By that reason, we believe that the use of artificial neurons like the spiking neurons which are defined as neurons with action potential output are the recommended solutions to simulate biological neurons. To visualize the nervous system, which in the Artificial Intelligence area is represented by the neural network of the device, we proposed in this work a neural simulator called SPINS (Spiking Neurons Simulator). This simulator was developed with educational purposes, helping to visualize the entire neural network, which shows the activations of every neuron and the states in which it is at the moment, where the state is de...
Lecture Notes in Computer Science, 2016
ABSTRACT In this study, two morphological representations in the genotype, a deterministic and a ... more ABSTRACT In this study, two morphological representations in the genotype, a deterministic and a nondeterministic representation, are compared when evolving a neuronal morphology for a pattern recognition task. The deterministic approach represents the dendritic morphology explicitly as a set of partitions in the genotype which can give rise to a single phenotype. The nondeterministic method used in this study encodes only the branching probability in the genotype which can produce multiple phenotypes. The main result is that the nondeterministic method instigates the selection of more symmetric dendritic morphologies which was not observed in the deterministic method.
Lecture Notes in Computer Science, 2012
ABSTRACT This paper addresses the problem of how dendritic topology and other properties of a neu... more ABSTRACT This paper addresses the problem of how dendritic topology and other properties of a neuron can determine its pattern recognition performance. In this study, dendritic trees were evolved using an evolutionary algorithm, which varied both morphologies and other parameters. Based on these trees, we constructed multi-compartmental conductance-based models of neurons. We found that dendritic morphology did have a considerable effect on pattern recognition performance. The results also revealed that the evolutionary algorithm could find effective morphologies, with a performance that was five times better than that of hand-tuned models.
ABSTRACT Many theories of cerebellar learning assume that long-term depression (LTD) of synapses ... more ABSTRACT Many theories of cerebellar learning assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells is the basis for pattern recognition in the cerebellum. Here we describe a series of computer simulations that use a morphologically realistic conductance-based model of a cerebellar Purkinje cell to study pattern recognition based on PF LTD. Our simulation results, which are supported by electrophysiological recordings in vitro and in vivo, suggest that Purkinje cells can use a novel neural code that is based on the duration of silent periods in their activity. The simulations of the biologically detailed Purkinje cell model are compared with simulations of a corresponding artificial neural network (ANN) model. We find that the predictions of the two models differ to a large extent. The Purkinje cell model is very sensitive to the amount of LTD induced, whereas the ANN is not. Moreover, the pattern recognition performance of the ANN increases as the patterns become sparser, while the Purkinje cell model is unable to recognise very sparse patterns. These results highlight that it is important to choose a model at a level of biological detail that fits the research question that is being addressed.
Journal of Computational Neuroscience, 2014
In this paper we examine how a neuron's den-dritic morphology can affect its pattern recognition ... more In this paper we examine how a neuron's den-dritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that generates all possible dendritic trees with 22 terminal points, and one that creates representative samples of trees with 128 terminal points. Based on these trees, we construct multi-compartmental models. To assess the performance of the resulting neuronal models, we quantify their ability to discriminate learnt and novel input patterns. We find that the dendritic morphology does have a considerable effect on pattern recognition performance and that the neuronal performance is inversely correlated with the mean depth of the dendritic tree. The results also reveal that the asymmetry index of the dendritic tree does not correlate with the performance for the full range of tree morphologies. The performance of neurons with dendritic tapering is best predicted by the mean and variance of the electrotonic distance of their synapses to the soma. All relationships found for passive neuron models also hold, even in more accentuated form, for neurons with active membranes.
BMC Neuroscience, 2008
Many theories of cerebellar function assume that longterm depression (LTD) of parallel fiber (PF)... more Many theories of cerebellar function assume that longterm depression (LTD) of parallel fiber (PF) synapses enables Purkinje cells (PCs) to learn to recognize PF activity patterns. According to the classic view, a PC can store and learn to distinguish PF activity patterns that have been presented repeatedly together with climbing fibre (CF) input to the cell. The resulting LTD of the PF synapses is often assumed to lead to a decreased rate of PC simple spike firing, a reduction in the inhibition of their target neurons in the deep cerebellar nuclei and thus an increased output from the cerebellum. We have recently shown by combining computer simulations with electrophysiological recordings in slices and in awake behaving mice that the readout of learned patterns in PCs may operate in a fundamentally different way. Our simulations and experiments predict that the best criterion to distinguish between learned and novel patterns is the duration of a pause in firing that occurs after presentation of a pattern, with shorter pauses in response to learned patterns [1].
BMC Neuroscience, 2010
Previous studies have shown that the morphology of a neuron can affect its firing pattern . Speci... more Previous studies have shown that the morphology of a neuron can affect its firing pattern . Specifically, some neuronal morphologies tend to favour bursting, where short sequences of spikes are interspersed with pauses in firing . This type of bursting behaviour has been observed in cerebellar Purkinje cells (PCs), and previous work on associative memory in PCs has shown that the generation of burst-pause sequences can be important for information storage in the cerebellum [3]. These results have implications for the coding of information in the brain, but they are specific to one particular neuron with a highly specialised morphology. In this study we therefore use a general approach to optimise generic neuronal structures for pattern recognition, while analysing how their morphology influences their firing pattern.
Many cerebellar learning theories assume that long-term depression (LTD) of synapses between para... more Many cerebellar learning theories assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells (PCs) provides the basis for pattern recognition in the cerebellum. Previous work has suggested that PCs can use a novel neural code based on the duration of silent periods. These simulations have used a simplified learning rule, where the synaptic conductance was halved each time a pattern was learned. However, experimental studies in cerebellar slices show that the synaptic conductance saturates and is rarely reduced to less than 50% of its baseline value. Moreover, the previous simulations did not include plasticity of the synapses between inhibitory interneurons and PCs. Here we study the effect of LTD saturation and inhibitory synaptic plasticity on pattern recognition in a complex PC model. We find that the PC model is very sensitive to the value at which LTD saturates, but is unaffected by inhibitory synaptic plasticity.
Many theories of cerebellar function assume that long-term depression (LTD) of parallel fiber (PF... more Many theories of cerebellar function assume that long-term depression (LTD) of parallel fiber (PF) synapses ena-bles Purkinje cells (PCs) to learn to recognize PF activity patterns. According to the classic view, a PC can store and learn to distinguish PF activity patterns that have been presented repeatedly together with climbing fibre (CF) input to the cell. The resulting LTD of the PF synapses is often assumed to lead to a decreased rate of PC simple spike firing, a reduction in the inhibition of their target neurons in the deep cerebellar nuclei and thus an increased output from the cerebellum. We have recently shown by combining computer simulations with electro-physiological recordings in slices and in awake behaving mice that the readout of learned patterns in PCs may oper-ate in a fundamentally different way. Our simulations and
Previous studies have shown that the morphology of a neuron can affect its firing pattern [1,2]. ... more Previous studies have shown that the morphology of a neuron can affect its firing pattern [1,2]. Specifically, some neuronal morphologies tend to favour bursting, where short sequences of spikes are interspersed with pauses in firing [1,2]. This type of bursting behaviour has been observed in cerebellar Purkinje cells (PCs), and previous work on associative memory in PCs has shown that the generation of burst-pause sequences can be important for information storage in the cerebellum [3]. These results have implications for the coding of infor-mation in the brain, but they are specific to one particu-lar neuron with a highly specialised morphology. In this study we therefore use a general approach to optimise generic neuronal structures for pattern recognition, while analysing how their morphology influences their
Journal of Computational Neuroscience, 2014
Intelligent devices can be considered biological inspired mechanisms. These devices have the abil... more Intelligent devices can be considered biological inspired mechanisms. These devices have the ability to simulate characteristics and behaviors like those of living beings, by the modeling of their neural systems. To have a higher biological fidelity, the nervous system of these devices must implement neural models as close as possible to the biological neuron. By that reason, we believe that the use of artificial neurons like the spiking neurons which are defined as neurons with action potential output are the recommended solutions to simulate biological neurons. To visualize the nervous system, which in the Artificial Intelligence area is represented by the neural network of the device, we proposed in this work a neural simulator called SPINS (Spiking Neurons Simulator). This simulator was developed with educational purposes, helping to visualize the entire neural network, which shows the activations of every neuron and the states in which it is at the moment, where the state is de...
Lecture Notes in Computer Science, 2016
ABSTRACT In this study, two morphological representations in the genotype, a deterministic and a ... more ABSTRACT In this study, two morphological representations in the genotype, a deterministic and a nondeterministic representation, are compared when evolving a neuronal morphology for a pattern recognition task. The deterministic approach represents the dendritic morphology explicitly as a set of partitions in the genotype which can give rise to a single phenotype. The nondeterministic method used in this study encodes only the branching probability in the genotype which can produce multiple phenotypes. The main result is that the nondeterministic method instigates the selection of more symmetric dendritic morphologies which was not observed in the deterministic method.
Lecture Notes in Computer Science, 2012
ABSTRACT This paper addresses the problem of how dendritic topology and other properties of a neu... more ABSTRACT This paper addresses the problem of how dendritic topology and other properties of a neuron can determine its pattern recognition performance. In this study, dendritic trees were evolved using an evolutionary algorithm, which varied both morphologies and other parameters. Based on these trees, we constructed multi-compartmental conductance-based models of neurons. We found that dendritic morphology did have a considerable effect on pattern recognition performance. The results also revealed that the evolutionary algorithm could find effective morphologies, with a performance that was five times better than that of hand-tuned models.
ABSTRACT Many theories of cerebellar learning assume that long-term depression (LTD) of synapses ... more ABSTRACT Many theories of cerebellar learning assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells is the basis for pattern recognition in the cerebellum. Here we describe a series of computer simulations that use a morphologically realistic conductance-based model of a cerebellar Purkinje cell to study pattern recognition based on PF LTD. Our simulation results, which are supported by electrophysiological recordings in vitro and in vivo, suggest that Purkinje cells can use a novel neural code that is based on the duration of silent periods in their activity. The simulations of the biologically detailed Purkinje cell model are compared with simulations of a corresponding artificial neural network (ANN) model. We find that the predictions of the two models differ to a large extent. The Purkinje cell model is very sensitive to the amount of LTD induced, whereas the ANN is not. Moreover, the pattern recognition performance of the ANN increases as the patterns become sparser, while the Purkinje cell model is unable to recognise very sparse patterns. These results highlight that it is important to choose a model at a level of biological detail that fits the research question that is being addressed.
Journal of Computational Neuroscience, 2014
In this paper we examine how a neuron's den-dritic morphology can affect its pattern recognition ... more In this paper we examine how a neuron's den-dritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that generates all possible dendritic trees with 22 terminal points, and one that creates representative samples of trees with 128 terminal points. Based on these trees, we construct multi-compartmental models. To assess the performance of the resulting neuronal models, we quantify their ability to discriminate learnt and novel input patterns. We find that the dendritic morphology does have a considerable effect on pattern recognition performance and that the neuronal performance is inversely correlated with the mean depth of the dendritic tree. The results also reveal that the asymmetry index of the dendritic tree does not correlate with the performance for the full range of tree morphologies. The performance of neurons with dendritic tapering is best predicted by the mean and variance of the electrotonic distance of their synapses to the soma. All relationships found for passive neuron models also hold, even in more accentuated form, for neurons with active membranes.
BMC Neuroscience, 2008
Many theories of cerebellar function assume that longterm depression (LTD) of parallel fiber (PF)... more Many theories of cerebellar function assume that longterm depression (LTD) of parallel fiber (PF) synapses enables Purkinje cells (PCs) to learn to recognize PF activity patterns. According to the classic view, a PC can store and learn to distinguish PF activity patterns that have been presented repeatedly together with climbing fibre (CF) input to the cell. The resulting LTD of the PF synapses is often assumed to lead to a decreased rate of PC simple spike firing, a reduction in the inhibition of their target neurons in the deep cerebellar nuclei and thus an increased output from the cerebellum. We have recently shown by combining computer simulations with electrophysiological recordings in slices and in awake behaving mice that the readout of learned patterns in PCs may operate in a fundamentally different way. Our simulations and experiments predict that the best criterion to distinguish between learned and novel patterns is the duration of a pause in firing that occurs after presentation of a pattern, with shorter pauses in response to learned patterns [1].
BMC Neuroscience, 2010
Previous studies have shown that the morphology of a neuron can affect its firing pattern . Speci... more Previous studies have shown that the morphology of a neuron can affect its firing pattern . Specifically, some neuronal morphologies tend to favour bursting, where short sequences of spikes are interspersed with pauses in firing . This type of bursting behaviour has been observed in cerebellar Purkinje cells (PCs), and previous work on associative memory in PCs has shown that the generation of burst-pause sequences can be important for information storage in the cerebellum [3]. These results have implications for the coding of information in the brain, but they are specific to one particular neuron with a highly specialised morphology. In this study we therefore use a general approach to optimise generic neuronal structures for pattern recognition, while analysing how their morphology influences their firing pattern.
Many cerebellar learning theories assume that long-term depression (LTD) of synapses between para... more Many cerebellar learning theories assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells (PCs) provides the basis for pattern recognition in the cerebellum. Previous work has suggested that PCs can use a novel neural code based on the duration of silent periods. These simulations have used a simplified learning rule, where the synaptic conductance was halved each time a pattern was learned. However, experimental studies in cerebellar slices show that the synaptic conductance saturates and is rarely reduced to less than 50% of its baseline value. Moreover, the previous simulations did not include plasticity of the synapses between inhibitory interneurons and PCs. Here we study the effect of LTD saturation and inhibitory synaptic plasticity on pattern recognition in a complex PC model. We find that the PC model is very sensitive to the value at which LTD saturates, but is unaffected by inhibitory synaptic plasticity.