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Neuronal activity in the rat motor cortex infers balanced synaptic inputs correlated to movement onsets
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Abstracts / Neuroscience Research 71S (2011) e46–e107
O3-H-1-4 Silicon neuron circuit based on the Izhikevich model Nobuyuki Mizoguchi 1 , Takashi Kohno 2 1
Div. of Electr. Eng., Grad. Sch. of Eng., Tokyo Univ., Tokyo, Japan 2 Institute of Industrial Science, The University of Tokyo, Tokyo, Japan The silicon neuron is an electronic circuit that mimics the electrophysiological functions of a neuron. In the neurophysiological studies, it has been used for the hybrid system, in which silicon neurons and living neurons are connected to each other, to elucidate the neuronal behaviors. The hybrid system makes it easy to analyze the changes in the behavior of the neural network when properties of the specific neurons are altered because behaviors of silicon neurons are configurable. To reproduce behaviors of a neuron precisely, silicon neurons based on the ionic-conductance neuron models are mainly used for the hybrid system. However, their circuit and the number of bias voltages to be applied tend to be quite large, which strictly limits the number of silicon neurons that can be incorporated. The silicon neural network, in which silicon neurons are connected via silicon synapses that mimics the functions of synapses, has been developed to realize various types of neuromorphic hardware, such as silicon cochlea, vision sensor, and pattern recognition system. Simple silicon neurons based on the leaky integrate-and-fire model are mainly used to construct the large scale silicon neural network. However, such silicon neuron can reproduce limited number of neuronal behaviors, which limits the dynamics of the neural network. We propose a silicon neuron circuit based on the Izhikevich model to overcome those limitations. It reproduces the same dynamics as the Izhikevich model, a mathematical neuron model that can reproduce rich dynamics in cortical neurons by a two-variable differential equation, with simple circuits by using a mathematical-structure-based method. The circuitry is composed of MOSFETs that are operated in the subthreshold region, and its power consumption is estimated by HSpice simulation to be under 20 nW. This power consumption is significantly lower than the conductance-based silicon neurons, which is a great advantage for biomedical applications. doi:10.1016/j.neures.2011.07.332
O3-H-2-1 Parameter space analysis of extracellular electrical stimulation having staircase waveform for selective excitation of myelinated axons Ayako Ueno , Norihiro Katayama, Akihiro Karashima, Mitsuyuki Nakao Biomodeling Lab., Graduate School of Information Sciences, Tohoku University, Sendai, Japan Electrical stimulation with an extracellular electrode has been widely used in basic neuroscience as well as clinical medicine. Since the electrical stimulation tends to activate axons near the electrode, it is necessary to move the electrode to change the target of stimulation. So far, it has been reported that extracellular stimulation having staircase waveform is possible to excite selectively axons distant from an electrode without activating axons fiber close to the electrode. However, the response depends on many stimulation parameters; the method of determining stimulation parameters has not been shown clearly. In this study, we conducted parameter space analysis of myelinated axon model in response to the staircase stimulation by the computer simulation. The response to the cathodic staircase stimulation was investigated. When the amplitudes of both the first (pre-pulse) and second pulse (test pulse) were very weak, an action potential was generated at the node of Ranvier closest to the electrode and propagated to both sides (cathodic excitation). When the intensity of the test pulse was slightly increased, propagation of the action potential was blocked at nearest neighbor nodes of the closest one by the strong hyperpolarization caused by the spatial profile of the extracellular electric field (anodal block). If the test pulse intensity was increased much more, action potentials were generated at the neighbor nodes after stopping the stimulation (anodal break excitation). With increasing the amplitude of pre-pulse, the range of parameter of cathodic excitation decreased and disappeared. The axon located more distant from the electrode showed wider parameter region of cathodic excitation. These data would be useful to design and develop dynamic targeting stimulation of myelinated axon. doi:10.1016/j.neures.2011.07.333
O3-H-2-2 A comparison of standardization methods of voltage-imaging data that enable to integrate the spatial information from different samples Yasuhisa Fujiki 1 , Yasumasa Okada 2 , Yoshitaka Oku 3 , Shigefumi Yokota 4 , Yoshiyasu Tamura 1,5 , Makio Ishiguro 1,5 , Fumikazu Miwakeichi 1,5 1
The Graduate University for Advanced Studies 2 Keio University 3 Hyogo College of Medicine 4 Shimane University 5 The Institute of Statistical Mathematics Fluorescence imaging has been widely applied to analyze spatio-temporal patterns of cellular and molecular dynamics in the brain and spinal cord. However, there is a difficulty in integrating spatial information of specific regions of interest in multiple samples, because of inter-sample variability in the shape and size of target structures. Specifically, we attempted to standardize transversely sectioned spinal cord images and identify laminar layers. We employed two standardization methods, affine transformation (AT) and angle-dependent transformation (ADT). An affine transformation maps a raw image into a new image by applying a linear combination of translation, rotation, scaling, and shearing operations. We adopted the polar coordination (r: radius, : angular) to pixels on the outline and defined an outline function with these two variables; r and . ADT is a novel nonlinear transformation that combines rotation and angle-dependent scaling, in which the scaling parameter is estimated as a function of . Therefore, each raw image was rotated and scaled linearly along each . We next evaluated the accuracy of three standardization methods, AT, ADT, and a combined method AT and ADT (AT + ADT). We evaluated three indices, the ratio that pixels are correctly classified to the target layer, the spatial distribution that pixels are not categorized to any layer and the error ratio by using the leave-one-out cross validation method. We used a neuron-specific marker (NeuN)-stained histological images of transversely sectioned spinal cord slices (n = 21) as a set of bench mark data because the spatial resolution of fluorescence images, such as those imaged with voltage-sensitive dyes, is not high enough to distinguish internal structures. From these indices, we conclude that the accuracy of each method varies depending on layers and AT + ADT outperforms other two methods. Research fund: KAKENHI(19200021,20590218). doi:10.1016/j.neures.2011.07.334
O3-H-2-3 Neuronal activity in the rat motor cortex infers balanced synaptic inputs correlated to movement onsets Yasuhiro Tsubo 1 , Yoshikazu Isomura 2 , Tomoki Fukai 1 1
RIKEN BSI, Wako, Saitama Machida, Tokyo
2
Brain Science Institute, Tamagawa Univ.,
Cortical neurons in an awake animal exhibit highly irregular spike sequences. It is crucial for elucidating the cognitive function in the brain to understand how the irregular spike sequences recorded from in vivo cortical neurons are caused by intrinsic properties of the neurons and/or input properties from surrounding neurons. Previously, we found that the inter-spike intervals recorded from in vivo neurons in rat motor cortex during a self-paced forelimb movement task obeyed long-tailed power-law distributions. However, such power-law spike sequences are not able to be explained by neuronal models receiving simple Poisson synaptic inputs from surrounding neurons. Indeed, our previous in vitro experiments showed that the inter-spike intervals generated by cortical pyramidal neurons receiving simple Poisson synaptic inputs obeyed a gamma distribution, instead of a power-law distribution. This problem motivated us to seek the input conditions that enable a neuron to generate the power-law spike sequences. We found that model neurons receiving balanced excitatory and inhibitory input can robustly generate power-law spike sequences when the input rate changes are correlated to the timings of movement onset, but not directly correlated to the arm movement or movement velocity. Our results imply that neuronal population in the rat motor cortex may encode both the onset and passage times of each movement event. Research fund: KAKENHI 22700323 (Y.T.) doi:10.1016/j.neures.2011.07.335
Abstracts / Neuroscience Research 71S (2011) e46–e107
O3-H-1-4 Silicon neuron circuit based on the Izhikevich model Nobuyuki Mizoguchi 1 , Takashi Kohno 2 1
Div. of Electr. Eng., Grad. Sch. of Eng., Tokyo Univ., Tokyo, Japan 2 Institute of Industrial Science, The University of Tokyo, Tokyo, Japan The silicon neuron is an electronic circuit that mimics the electrophysiological functions of a neuron. In the neurophysiological studies, it has been used for the hybrid system, in which silicon neurons and living neurons are connected to each other, to elucidate the neuronal behaviors. The hybrid system makes it easy to analyze the changes in the behavior of the neural network when properties of the specific neurons are altered because behaviors of silicon neurons are configurable. To reproduce behaviors of a neuron precisely, silicon neurons based on the ionic-conductance neuron models are mainly used for the hybrid system. However, their circuit and the number of bias voltages to be applied tend to be quite large, which strictly limits the number of silicon neurons that can be incorporated. The silicon neural network, in which silicon neurons are connected via silicon synapses that mimics the functions of synapses, has been developed to realize various types of neuromorphic hardware, such as silicon cochlea, vision sensor, and pattern recognition system. Simple silicon neurons based on the leaky integrate-and-fire model are mainly used to construct the large scale silicon neural network. However, such silicon neuron can reproduce limited number of neuronal behaviors, which limits the dynamics of the neural network. We propose a silicon neuron circuit based on the Izhikevich model to overcome those limitations. It reproduces the same dynamics as the Izhikevich model, a mathematical neuron model that can reproduce rich dynamics in cortical neurons by a two-variable differential equation, with simple circuits by using a mathematical-structure-based method. The circuitry is composed of MOSFETs that are operated in the subthreshold region, and its power consumption is estimated by HSpice simulation to be under 20 nW. This power consumption is significantly lower than the conductance-based silicon neurons, which is a great advantage for biomedical applications. doi:10.1016/j.neures.2011.07.332
O3-H-2-1 Parameter space analysis of extracellular electrical stimulation having staircase waveform for selective excitation of myelinated axons Ayako Ueno , Norihiro Katayama, Akihiro Karashima, Mitsuyuki Nakao Biomodeling Lab., Graduate School of Information Sciences, Tohoku University, Sendai, Japan Electrical stimulation with an extracellular electrode has been widely used in basic neuroscience as well as clinical medicine. Since the electrical stimulation tends to activate axons near the electrode, it is necessary to move the electrode to change the target of stimulation. So far, it has been reported that extracellular stimulation having staircase waveform is possible to excite selectively axons distant from an electrode without activating axons fiber close to the electrode. However, the response depends on many stimulation parameters; the method of determining stimulation parameters has not been shown clearly. In this study, we conducted parameter space analysis of myelinated axon model in response to the staircase stimulation by the computer simulation. The response to the cathodic staircase stimulation was investigated. When the amplitudes of both the first (pre-pulse) and second pulse (test pulse) were very weak, an action potential was generated at the node of Ranvier closest to the electrode and propagated to both sides (cathodic excitation). When the intensity of the test pulse was slightly increased, propagation of the action potential was blocked at nearest neighbor nodes of the closest one by the strong hyperpolarization caused by the spatial profile of the extracellular electric field (anodal block). If the test pulse intensity was increased much more, action potentials were generated at the neighbor nodes after stopping the stimulation (anodal break excitation). With increasing the amplitude of pre-pulse, the range of parameter of cathodic excitation decreased and disappeared. The axon located more distant from the electrode showed wider parameter region of cathodic excitation. These data would be useful to design and develop dynamic targeting stimulation of myelinated axon. doi:10.1016/j.neures.2011.07.333
O3-H-2-2 A comparison of standardization methods of voltage-imaging data that enable to integrate the spatial information from different samples Yasuhisa Fujiki 1 , Yasumasa Okada 2 , Yoshitaka Oku 3 , Shigefumi Yokota 4 , Yoshiyasu Tamura 1,5 , Makio Ishiguro 1,5 , Fumikazu Miwakeichi 1,5 1
The Graduate University for Advanced Studies 2 Keio University 3 Hyogo College of Medicine 4 Shimane University 5 The Institute of Statistical Mathematics Fluorescence imaging has been widely applied to analyze spatio-temporal patterns of cellular and molecular dynamics in the brain and spinal cord. However, there is a difficulty in integrating spatial information of specific regions of interest in multiple samples, because of inter-sample variability in the shape and size of target structures. Specifically, we attempted to standardize transversely sectioned spinal cord images and identify laminar layers. We employed two standardization methods, affine transformation (AT) and angle-dependent transformation (ADT). An affine transformation maps a raw image into a new image by applying a linear combination of translation, rotation, scaling, and shearing operations. We adopted the polar coordination (r: radius, : angular) to pixels on the outline and defined an outline function with these two variables; r and . ADT is a novel nonlinear transformation that combines rotation and angle-dependent scaling, in which the scaling parameter is estimated as a function of . Therefore, each raw image was rotated and scaled linearly along each . We next evaluated the accuracy of three standardization methods, AT, ADT, and a combined method AT and ADT (AT + ADT). We evaluated three indices, the ratio that pixels are correctly classified to the target layer, the spatial distribution that pixels are not categorized to any layer and the error ratio by using the leave-one-out cross validation method. We used a neuron-specific marker (NeuN)-stained histological images of transversely sectioned spinal cord slices (n = 21) as a set of bench mark data because the spatial resolution of fluorescence images, such as those imaged with voltage-sensitive dyes, is not high enough to distinguish internal structures. From these indices, we conclude that the accuracy of each method varies depending on layers and AT + ADT outperforms other two methods. Research fund: KAKENHI(19200021,20590218). doi:10.1016/j.neures.2011.07.334
O3-H-2-3 Neuronal activity in the rat motor cortex infers balanced synaptic inputs correlated to movement onsets Yasuhiro Tsubo 1 , Yoshikazu Isomura 2 , Tomoki Fukai 1 1
RIKEN BSI, Wako, Saitama Machida, Tokyo
2
Brain Science Institute, Tamagawa Univ.,
Cortical neurons in an awake animal exhibit highly irregular spike sequences. It is crucial for elucidating the cognitive function in the brain to understand how the irregular spike sequences recorded from in vivo cortical neurons are caused by intrinsic properties of the neurons and/or input properties from surrounding neurons. Previously, we found that the inter-spike intervals recorded from in vivo neurons in rat motor cortex during a self-paced forelimb movement task obeyed long-tailed power-law distributions. However, such power-law spike sequences are not able to be explained by neuronal models receiving simple Poisson synaptic inputs from surrounding neurons. Indeed, our previous in vitro experiments showed that the inter-spike intervals generated by cortical pyramidal neurons receiving simple Poisson synaptic inputs obeyed a gamma distribution, instead of a power-law distribution. This problem motivated us to seek the input conditions that enable a neuron to generate the power-law spike sequences. We found that model neurons receiving balanced excitatory and inhibitory input can robustly generate power-law spike sequences when the input rate changes are correlated to the timings of movement onset, but not directly correlated to the arm movement or movement velocity. Our results imply that neuronal population in the rat motor cortex may encode both the onset and passage times of each movement event. Research fund: KAKENHI 22700323 (Y.T.) doi:10.1016/j.neures.2011.07.335