| ObjectiveThis paper aims to study the coding of neural ensembles relating to memory (hippocampus) with wavelet-clustering method, so as to discriminate specific functional neural ensembles in the detail. The study probes into the key structure of memory hippocampus CA3 area, with wavelet decomposition, expecting to discriminate functional neural ensembles under different wavelet scales, evaluate the coding efficiency, especially with two simultaneous inputs, the priority of certain wavelet scale over other scales.Methods1. Establish pulse coupled neural network (PCNN) model, simulate spatial temporal activity of neural population in hippocampus CA3 area under different stimulus (memory tasks).According to the anatomical characteristics of hippocampus, the PCNN model is composed of 120 neurons, of which 100 is excitatory and 20 inhibitory. In CA3 area, each excitatory neuron is connected to about 75% of other neurons in the same layer. The inhibitory neurons are all-to-all connected and is related to the sparse firings in the area. The synaptic weight among neurons is set Gaussian distribution; only excitatory neurons output to other layers with sparse activity (less than 10%).PCNN model is given different typical patterns of stimuli: (1) random input with Gaussian distribution, sinusoidal input (T=0.2sec), and the linear superimpose of above two signals; (2) Rectified sinusoidal input, rectified cosinusoidal input, and the linear superimpose of above two signals.. The output of PCNN is the time series of spikings of each neuron in the ensemble, from which we acquire inter-spike interval (ISI) series of the neural population.2. Cluster coding the simulation data of neural ensembles: 1) Analyze ISI series of neural population under three different stimuli, discriminate functional neural ensembles with self organizing map;2) According to the FFT spectrum of ISI series, the scale of wavelet is set to 5. Code the ISI series with self organizing map at each scale of wavelet, discriminate different functional ensembles relating to the specific input.Results1. PCNN model can simulate the sparse activity of hippocampus neural population under different stimuli, it provides simulation data for the further study of waveletcluster coding in this paper.2. Cluster coding of neural population with the application of self organizing map:(1) Results for Gaussian input, sSinusoidal input and superimpose of the two inputs:1) Results of cluster coding:â‘ . Under random stimulus with Gaussian distribution, the clusterd neurons are (10, 11, 14, 24, 25, 27, 35, 36, 37, 45, 48, 50, 55, 57, 61, 70, 77, 90, 92,99);â‘¡. Under sinusoidal input, the clusterd neurons are (2, 6, 8, 9, 13, 19, 20, 26, 28, 29, 30, 32, 34, 40, 41, 46, 50, 51, 52, 53, 54, 55, 57, 59, 60, 61, 63, 65, 71, 75, 83, 85, 86, 87)â‘¢. Under the stimulation of both the above inputs, the clustered neurons are ( 1, 3, 4, 5, 6, 8, 10, 11, 12, 14, 15, 17, 18, 19, 20, 21, 22, 24, 25, 26, 29, 31, 33, 34, 41, 42, 44, 45, 46, 47, 49, 50, 52, 55, 56, 57, 59, 60, 61, 64, 67, 71, 73, 75, 76, 77, 78, 79, 80, 81, 84, 85, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99).2) Results of wavelet-cluster coding:For the wavelet-clustering coding, the best results are shown on scale-5, the clustered neurons under three different stimuli are as follows:1) Under random stimulus with Gaussian distribution, the clusterd neurons on scale-5 are (1, 2, 54, 69, 74, 81, 85, 95);2) Under sinusoidal stimulus with Gaussian distribution, the clusterd neurons on scale-5 are (9, 11, 12, 15, 16, 17, 18, 20, 21, 23, 32, 36, 37, 39, 41, 43, 44, 56, 57, 59, 63, 66, 70, 74, 75, 83, 86, 95, 97);3) Under the stimulation of both the above inputs, the clustered neurons are (1, 2, 9, 12, 13, 16, 17, 18, 23, 32, 36, 37, 39, 41, 43, 44, 45, 46, 54, 56, 57, 69, 74, 81, 83, 85, 86, 63, 66, 70, 74, 75, 95).(2) Results for sinusoidal input, cosinusoidal input and superimpose of the two inputs:1) Results of cluster coding:â‘ . Under the stimulation of sinusoidal input, the clustered neurons are (3, 12, 16, 18, 19, 21, 23, 24, 28, 30, 31, 32, 35, 38, 39, 40, 46, 51, 54, 55, 58, 59, 62, 64, 68, 71, 73, 74, 75, 76, 78, 79, 81, 82, 84, 86, 87, 91, 92, 95, 97, 99);â‘¡. Under the stimulation of cosinusoidal input, the clustered neurons are (2, 3, 6, 9, 10, 11, 12, 14, 15, 22, 23, 24, 26, 30, 31, 32, 33, 34, 39, 40, 42, 49, 50, 55, 57, 59, 62, 63, 64, 66, 73, 76, 79, 80, 81, 88, 89, 90, 94, 95, 96);â‘¢. Under the stimulation of both the above inputs, the clustered neurons are (2, 3, 4, 5, 6, 8, 10, 12, 13, 15, 21, 22, 23, 25, 28, 29, 30, 31, 35, 37, 39, 40, 41, 42, 46, 47, 48, 50, 52, 54, 55, 56, 57, 58, 59, 60, 61, 65, 67, 68, 69, 72, 73, 74, 75, 76, 78, 79, 80, 81, 85, 86, 91, 92, 93, 95, 97, 98);2) Results of wavelet-cluster coding..For the wavelet-clustering coding, the best results are shown on scale-2, the clustered neurons under three different stimuli are as follows:â‘ . Under the stimulation of sinusoidal input, the clustered neurons are (1, 7, 8, 10, 22, 23, 33, 41, 43, 51, 57, 65, 77, 87);â‘¡. Under the stimulation of cosinusoidal input, the clustered neurons are (15, 20, 28, 32, 38, 43, 53, 54, 58, 61, 70, 72, 83, 84, 97, 100);â‘¢. Under the stimulation of both the above inputs, the clustered neurons are (7, 8, 12, 15, 17, 20, 21, 22, 23, 24, 25, 26, 28, 30, 31, 32, 33, 34, 36, 38, 39, 40, 41, 43, 46, 47, 52, 56, 59, 61, 64, 65, 67, 70, 72, 73, 75, 77, 78, 79, 80, 83, 84, 86, 87, 90, 91, 93, 95, 97, 100). Conclusions1. The sparse output of PCNN model efficiently simulated firing patterns of hippocampus CA3 area neurons under three different stimuli.2. The cluster coding of neural ensemble under (1) Gaussian distributed random stimulus and sinusoidal stimulus; (2) rectified sinusoidal and rectified cosinusoidal input show little overlap over each other, on the whole , different stimuli can be discriminated with cluster coding neural ensembles. But under the above two stimuli simultaneously, the cluster coding is not very effective.3. The wave-cluster coding can discriminate different input stimulus on most of the scales, this is best shown (1) on scale-5 for Gaussian random input and sinusoidal input; (2) on scale-2 for rectified sinusoidal input and rectified cosinusoidal input. Under the superimpose of both the stimulus, wave-clustering on specific scale show better result than cluster coding. |
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