• Smart Media Journal
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• v.11 no.2
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• pp.77-83
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• 2022

The neuromorphic architecture uses a spiking neural network (SNN) model to derive more accurate results as more spike values are accumulated through inference experiments. When the inference result converges to a specific value, even if the inference experiment is further performed, the change in the result is smaller and power consumption may increase. In particular, in an AI-based IoT environment, power consumption can be a big problem. Therefore, in this paper, we propose a technique to reduce the power consumption of AI-based IoT by reducing the inference time by adjusting the inference image exposure time in the neuromorphic architecture environment. The proposed technique calculates the next inferred image exposure time by reflecting the change in inference accuracy. In addition, the rate of reflection of the change in inference accuracy can be adjusted with a coefficient value, and an optimal coefficient value is found through a comparison experiment of various coefficient values. In the proposed technique, the inference image exposure time corresponding to the target accuracy is greater than that of the linear technique, but the overall power consumption is less than that of the linear technique. As a result of measuring and evaluating the performance of the proposed method, it is confirmed that the inference experiment applying the proposed method can reduce the final exposure time by about 90% compared to the inference experiment applying the linear method.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.286-291
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• 2020

In this paper, we propose a system that recommends music through tempo-oriented music classification and sensor-based human activity recognition. The proposed method indexes music files using tempo-oriented music classification and recommends suitable music according to the recognized user's activity. For accurate music classification, a dynamic classification based on a modulation spectrum and a sequence classification based on a Mel-spectrogram are used in combination. In addition, simple accelerometer and gyroscope sensor data of the smartphone are applied to deep spiking neural networks to improve activity recognition performance. Finally, music recommendation is performed through a mapping table considering the relationship between the recognized activity and the indexed music file. The experimental results show that the proposed system is suitable for use in any practical mobile device with a music player.

• The Korean Journal of Physiology and Pharmacology
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• v.22 no.1
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• pp.71-80
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• 2018

In patients with epilepsy, depression is a common comorbidity but difficult to be treated because many antidepressants cause pro-convulsive effects. Thus, it is important to identify the risk of seizures associated with antidepressants. To determine whether paroxetine, a very potent selective serotonin reuptake inhibitor (SSRI), interacts with ion channels that modulate neuronal excitability, we examined the effects of paroxetine on Kv3.1 potassium channels, which contribute to high-frequency firing of interneurons, using the whole-cell patch-clamp technique. Kv3.1 channels were cloned from rat neurons and expressed in Chinese hamster ovary cells. Paroxetine reversibly reduced the amplitude of Kv3.1 current, with an $IC_{50}$ value of $9.43{\mu}M$ and a Hill coefficient of 1.43, and also accelerated the decay of Kv3.1 current. The paroxetine-induced inhibition of Kv3.1 channels was voltage-dependent even when the channels were fully open. The binding ($k_{+1}$) and unbinding ($k_{-1}$) rate constants for the paroxetine effect were $4.5{\mu}M^{-1}s^{-1}$ and $35.8s^{-1}$, respectively, yielding a calculated $K_D$ value of $7.9{\mu}M$. The analyses of Kv3.1 tail current indicated that paroxetine did not affect ion selectivity and slowed its deactivation time course, resulting in a tail crossover phenomenon. Paroxetine inhibited Kv3.1 channels in a use-dependent manner. Taken together, these results suggest that paroxetine blocks the open state of Kv3.1 channels. Given the role of Kv3.1 in fast spiking of interneurons, our data imply that the blockade of Kv3.1 by paroxetine might elevate epileptic activity of neural networks by interfering with repetitive firing of inhibitory neurons.

• Journal of IKEEE
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• v.26 no.4
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• pp.633-638
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• 2022

Rencently, neuromorphic systems of spiking neural networks (SNNs) that imitate the human brain have attracted attention. Neuromorphic technology has the advantage of high speed and low power consumption in cognitive applications and processing. Resistive random-access memory (RRAM) for SNNs are the most efficient structure for parallel calculation and perform the gradual switching operation of spike-timing-dependent plasticity (STDP). RRAM as synaptic device operation has low-power processing and expresses various memory states. However, the integration of RRAM device causes high switching voltage and current, resulting in high power consumption. To reduce the operation voltage of the RRAM, it is important to develop new materials of the switching layer and metal electrode. This study suggested a optimized new structure that is the Metal/Al₂O₃/HfO_x/SWCNTs/N+silicon (MOCS) with single-walled carbon nanotubes (SWCNTs), which have excellent electrical and mechanical properties in order to lower the switching voltage. Therefore, we show an improvement in the gradual switching behavior and low-power I/V curve of SWCNTs-based memristors.