Correlation-based synaptic plasticity provides a potential cellular mechanism for learning and memory. Studies in the visual and somatosensory systems have shown that behavioral and surgical manipulation of sensory inputs leads to changes in cortical organization that are consistent with the operation of these learning rules. In this study, we examine how the organization of primary auditory cortex (A1) is altered by tones designed to decrease the average input correlation across the frequency map. After one month of separately pairing nucleus basalis stimulation with 2 and 14 kHz tones, a greater proportion of A1 neurons responded to frequencies below 2 kHz and above 14 kHz. Despite the expanded representation of these tones, cortical excitability was specifically reduced in the high and low frequency regions of A1, as evidenced by increased neural thresholds and decreased response strength. In contrast, in the frequency region between the two paired tones, driven rates were unaffected and spontaneous firing rate was increased. Neural response latencies were increased across the frequency map when nucleus basalis stimulation was associated with asynchronous activation of the high and low frequency regions of A1. This set of changes did not occur when pulsed noise bursts were paired with nucleus basalis stimulation. These results are consistent with earlier observations that sensory input statistics can shape cortical map organization and spike timing.
Cortical responses are adjusted and optimized throughout life to meet changing behavioral demands and to compensate for peripheral damage. The cholinergic nucleus basalis (NB) gates cortical plasticity and focuses learning on behaviorally meaningful stimuli. By systematically varying the acoustic parameters of the sound paired with NB activation, we have previously shown that tone frequency and amplitude modulation rate alter the topography and selectivity of frequency tuning in primary auditory cortex. This result suggests that network-level rules operate in the cortex to guide reorganization based on specific features of the sensory input associated with NB activity. This report summarizes recent evidence that temporal response properties of cortical neurons are influenced by the spectral characteristics of sounds associated with cholinergic modulation. For example, repeated pairing of a spectrally complex (ripple) stimulus decreased the minimum response latency for the ripple, but lengthened the minimum latency for tones. Pairing a rapid train of tones with NB activation only increased the maximum following rate of cortical neurons when the carrier frequency of each train was randomly varied. These results suggest that spectral and temporal parameters of acoustic experiences interact to shape spectrotemporal selectivity in the cortex. Additional experiments with more complex stimuli are needed to clarify how the cortex learns natural sounds such as speech.
Three dimensional motion capture facility is a powerful tool for quantitative and qualitative assessment of multijoint external movements. Electro-myograph (EMG) signals give the physiologic information of muscles while doing motions. In this paper, our objective is to integrate these two different bio-medical data together and to extract precise and accurate feature information for classifying the human motions. When both forms of data are integrated and analyzed together, the information achieved will be immensely useful to quantify the complex human motions for medical reasons or sport performances. These biological quantifications of biomechanical data, are useful for gait analysis and several orthopedic applications, such as joint mechanics, prosthetic designs, and sports medicines. The different dimensionality reduction approaches such Integral of Absolute value and Weighted Singular Value Decomposition are used to extract the preliminary features from EMG and motion capture data respectively. On combining these feature vectors, fuzzy clustering such as Fuzzy c-means (FCM) is performed on these vectors that are mapped as the points in multi-dimensional feature space. We get the degree of memberships with every cluster for each mapped point. This extracted information is used as the final feature vectors for classifying the human motions.
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