The Experts below are selected from a list of 18498 Experts worldwide ranked by ideXlab platform
Christian Tetzlaff - One of the best experts on this subject based on the ideXlab platform.
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synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ << 1. Synaptic scaling operates in parallel to conventional plasticity and acts simultaneously on different synapses. Here we suggest that synaptic scaling is combined with different types of plasticity mechanisms in the same circuit or even at the same Neuron and regulates synaptic diversity across the circuit. We demonstrate that it robustly yields stable and diverse weight distributions which moreover are independent of the Individual plasticity mechanism. As scaling co-acts with plasticity, such a combined mechanism is mathematically characterized by a weight change dω/dt = μG + γH. Here μ defines the rate of change of conventional synaptic plasticity, γ <<μ << 1, and G and H describe the specific types of plasticity and scaling, respectively [7]. For example, G is different for plain Hebbian plasticity than for STDP. As we show, combining any type of conventional plasticity G with nonlinear weight-dependent scaling H naturally yields global synaptic stabilization across the circuit regardless of the specific form of the plasticity G and also largely independent of the intrinsic Neuron dynamics. Our study demonstrates that synapses are stabilized strictly in an input-determined way thereby capturing characteristic features of the inputs to the network. As an important result, we show that such systems are capable of representing a given input pattern via stably changed weights along several stages of signal propagation. This holds even in circuits containing a substantial number of random recurrent connections but no particular additional architecture.
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Synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ
Florentin Wörgötter - One of the best experts on this subject based on the ideXlab platform.
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synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ << 1. Synaptic scaling operates in parallel to conventional plasticity and acts simultaneously on different synapses. Here we suggest that synaptic scaling is combined with different types of plasticity mechanisms in the same circuit or even at the same Neuron and regulates synaptic diversity across the circuit. We demonstrate that it robustly yields stable and diverse weight distributions which moreover are independent of the Individual plasticity mechanism. As scaling co-acts with plasticity, such a combined mechanism is mathematically characterized by a weight change dω/dt = μG + γH. Here μ defines the rate of change of conventional synaptic plasticity, γ <<μ << 1, and G and H describe the specific types of plasticity and scaling, respectively [7]. For example, G is different for plain Hebbian plasticity than for STDP. As we show, combining any type of conventional plasticity G with nonlinear weight-dependent scaling H naturally yields global synaptic stabilization across the circuit regardless of the specific form of the plasticity G and also largely independent of the intrinsic Neuron dynamics. Our study demonstrates that synapses are stabilized strictly in an input-determined way thereby capturing characteristic features of the inputs to the network. As an important result, we show that such systems are capable of representing a given input pattern via stably changed weights along several stages of signal propagation. This holds even in circuits containing a substantial number of random recurrent connections but no particular additional architecture.
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Synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ
Christoph Kolodziejski - One of the best experts on this subject based on the ideXlab platform.
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synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ << 1. Synaptic scaling operates in parallel to conventional plasticity and acts simultaneously on different synapses. Here we suggest that synaptic scaling is combined with different types of plasticity mechanisms in the same circuit or even at the same Neuron and regulates synaptic diversity across the circuit. We demonstrate that it robustly yields stable and diverse weight distributions which moreover are independent of the Individual plasticity mechanism. As scaling co-acts with plasticity, such a combined mechanism is mathematically characterized by a weight change dω/dt = μG + γH. Here μ defines the rate of change of conventional synaptic plasticity, γ <<μ << 1, and G and H describe the specific types of plasticity and scaling, respectively [7]. For example, G is different for plain Hebbian plasticity than for STDP. As we show, combining any type of conventional plasticity G with nonlinear weight-dependent scaling H naturally yields global synaptic stabilization across the circuit regardless of the specific form of the plasticity G and also largely independent of the intrinsic Neuron dynamics. Our study demonstrates that synapses are stabilized strictly in an input-determined way thereby capturing characteristic features of the inputs to the network. As an important result, we show that such systems are capable of representing a given input pattern via stably changed weights along several stages of signal propagation. This holds even in circuits containing a substantial number of random recurrent connections but no particular additional architecture.
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Synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ
Marc Timme - One of the best experts on this subject based on the ideXlab platform.
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synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ << 1. Synaptic scaling operates in parallel to conventional plasticity and acts simultaneously on different synapses. Here we suggest that synaptic scaling is combined with different types of plasticity mechanisms in the same circuit or even at the same Neuron and regulates synaptic diversity across the circuit. We demonstrate that it robustly yields stable and diverse weight distributions which moreover are independent of the Individual plasticity mechanism. As scaling co-acts with plasticity, such a combined mechanism is mathematically characterized by a weight change dω/dt = μG + γH. Here μ defines the rate of change of conventional synaptic plasticity, γ <<μ << 1, and G and H describe the specific types of plasticity and scaling, respectively [7]. For example, G is different for plain Hebbian plasticity than for STDP. As we show, combining any type of conventional plasticity G with nonlinear weight-dependent scaling H naturally yields global synaptic stabilization across the circuit regardless of the specific form of the plasticity G and also largely independent of the intrinsic Neuron dynamics. Our study demonstrates that synapses are stabilized strictly in an input-determined way thereby capturing characteristic features of the inputs to the network. As an important result, we show that such systems are capable of representing a given input pattern via stably changed weights along several stages of signal propagation. This holds even in circuits containing a substantial number of random recurrent connections but no particular additional architecture.
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Synaptic scaling generically stabilizes circuit connectivity
BMC Neuroscience, 2011Co-Authors: Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, Florentin WörgötterAbstract:Neural systems regulate synaptic plasticity avoiding overly strong growth or shrinkage of the connections, thereby keeping the circuit architecture operational. Accordingly, several experimental studies have shown that synaptic weights increase only in direct relation to their current value, resulting in reduced growth for stronger synapses [1]. It is, however, difficult to extract from these studies unequivocal evidence about the underlying biophysical mechanisms that control weight growth. The theoretical neurosciences have addressed this problem by exploring mechanisms for synaptic weight change that contain limiting factors to regulate growth [2]. The effectiveness of these mechanisms is difficult to justify from a biophysical perspective, in particular those that require knowledge of global network status (e.g. knowledge of the ‘sum of all weights’) for normalization. Also spike-timing-dependent plasticity [3] cannot guaranty stability because various types of plasticity exist across different Neurons and even at the same Neuron, depending on the location of the synapses [1]. Therefore, it remains an open question how neural circuits simultaneously stabilize their many synapses and ensure diversity in the presence of a variety of distinct plasticity mechanisms. In 1998, a series of studies initiated by Turrigiano augmented this discussion by demonstrating that network activity is homeostatically regulated, suggesting that weights ω are regulated by an activity-dependent difference term [4,5]. Accordingly, synaptic scaling compares output activity v against a desired target activity vT of each Individual Neuron [5]. Most straightforwardly, such a local weight change is defined by dω/dt = γH(νT – ν) [6], where the long characteristic time scale (hours up to days) of synaptic scaling is determined by a small factor γ
Terrence J Sejnowski - One of the best experts on this subject based on the ideXlab platform.
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the population tracking model a simple scalable statistical model for neural population data
Neural Computation, 2017Co-Authors: Cian Odonnell, Tiago J Goncalves, Nick Whiteley, Carlos Porteracailliau, Terrence J SejnowskiAbstract:Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of Neurons recorded . Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of Neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate the number of Neurons synchronously active and the probability that each Individual Neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 Neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65i¾ ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca and voltage imaging tools.
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the population tracking model a simple scalable statistical model for neural population data
bioRxiv, 2016Co-Authors: Cian Odonnell, Tiago J Goncalves, Nick Whiteley, Carlos Porteracailliau, Terrence J SejnowskiAbstract:Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of Neurons recorded (2^Neurons). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of Neurons, so requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of Neurons synchronously active) and the probability that each Individual Neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 Neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex, ~65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and surprisingly found that it first increases, then decreases during development. This statistical model opens new options for interrogating neural population data, and can bolster the use of modern large-scale in vivo Ca2+ and voltage imaging tools.