A Study Of Hippocampus CA3 SWNN Model And Neuronal Ensemble Encoding Stimulus

ObjectiveIn this paper,a hippocampus CA3 neurons spiking small world neural network(SWNN)model is established on the Matlab7.4 platform based on sparse activity ofhippocampus CA3 neurons.The temporal-spatial sequences of the hippocampus CA3area neurons firing activity are simulated under the stimulus of pulse input,Gaussianwhite noise input and the linear superposition input of the two conditions on theSWNN model.The neuronal ensemble rate coding and ISI correlation codingmethods are used to analyze the simulated spiking trains.The hippocampus CA3 areaneurons ensemble coding of the three types stimulus are investigated in this paper.The results may provide neural computation support for hippocampus neuronalensemble coding.Methods1.Construction of hippocampus CA3 area neurons spiking SWNN modelAccording to the anatomical characteristics of hippocampus CA3 area,the ratioof the excitatory to inhibitory neurons is about 5 to 1.The activity of hippocampusCA3 area neurons is sparse firing (less than 10%).A hippocampus CA3 neuronsspiking small world neural network (SWNN)model is established on the Matlab7.4platform.The temporal-spatial sequences of hippocampus CA3 area neurons firingactivity are simulated by the SWNN model.The Hindmarsh-Rose (HR)modelcould describe different discharge property of an excitatory or inhibitory neuron bychanging the parameterγ.In this paper,HR model is used to be the dynamicalequations of the spiking model neurons.The SWNN model is composed of 120neurons,in which 100 neurons are excitatory and 20 are inhibitory.The differentneurons in the SWNN model are connected with WS small-world network.2.The dynamic simulation of hippocampus CA3 area neurons spikingtemporal-spatial sequences by using the SWNN model.Set up three types of stimulus input patterns:pulse input,Gaussian white noiseinput and the linear superposition input of the two conditions.The 40 seconds temporal-spatial sequences of hippocampus CA3 area neurons are achieved under thethree different stimulus patterns imposed on the SWNN model.At the same time theinter-spike interval (ISI)of each neuron is get to make up the ISIs temporal-spatialsequences.3.The hippocampus CA3 neurons ensemble coding of three types of stimulusThe dynamic neuronal ensemble coding is investigated under the differentstimulus input pattern.The neuronal ensemble rate coding and ISI correlation codingmethods are used to analyze the simulated temporal-spatial sequences.Theeffectiveness of the above neuronal ensemble coding methods are compared under thethree different stimulus input pattern.For the neuronal population firing temporal-spatial sequences simulation dataunder the different stimulus pattern,adopt the rate coding and ISI correlation codingmethods to measure the functional neuronal ensembles.(1)The rate coding of neuronal ensemble firing activityThe bin window is set to be 200ms (physiological window)and the step lengthof bin window is set to be 50ms.The spiking numbers are counted in the movingwindow of each neuron temporal-spatial sequence.The dynamic topography maps ofneuronal population spike trains are achieved after normalizing the spiking numbers.(2)The ISI correlation coding of neuronal ensemble firing activity①The inter-spike intervals (ISis)of each neuron firing sequence are computedamong the neuronal population spike trains.②Connect the discrete ISis point of each neuron and get the N continuouscurves of ISI sequence by curve fitting.③The maximum mean firing rate neuronal firing sequence is selected to be thereference sequence.④The bin window length is set to be small scale (less than 50ms)and the ISIcorrelation values are computed in the kth window of N-1 ISI sequence withreference ISI sequence.⑤The step length is set to be 1/3 bin window,and the N-1 ISI correlationvalues are computed in each moving window. ⑥The dynamic topography maps of neuronal population spike trains areachieved after normalizing the ISI correlation value.Results1.A hippocampus CA3 neurons spiking small world neural network (SWNN)model is established on the Matlab7.4 platform in this paper.The SWNN model cansimulate the sparse activity of hippocampus CA3 area neurons successfully.2.The mean population firing rate of hippocampus CA3 area is 7.8% when nostimulus acts on the SWNN model.The result is in accordance with the sparse spikefiring characteristic of hippocampus CA3 area (the mean firing rate is less than 10%).3.The hippocampus CA3 neuronal population firing patterns are different under thethree types of stimulus input(pulse input,Gaussian white noise input and the linearsuperposition input of the two conditions).The neuronal ensemble coding patterns areas followed:①Neuronal ensemble rate coding(a)The firing rate range of neuron is [5.14～21.52] Hz under the pulse inputstimulus acts on the SWNN model.The mean firing rate of neuronal population(120neurons)is 10.43±0.38 Hz.The neuronal ensemble rate coding method canencode the pulse input stimulus pattern.(b)The firing rate range of neuron is [3.02～15.28] Hz under the Gaussian whitenoise input stimulus acts on the SWNN model.The mean firing rate of neuronalpopulation is 7.59±0.62 Hz.The neuronal ensemble rate coding method can encodethe Gaussian white noise input stimulus pattern.(c)The firing rate range of neuron is [2.27～55.21] Hz under the linearsuperposition input of the pulse input and Gaussian white noise input stimulus acts onthe SWNN model.The mean firing rate of neuronal population is 15.52±0.84 Hz.The neuronal ensemble rate coding method can not distinguish between the pulseinput and Gaussian white noise input stimulus pattern.②Neuronal ensemble ISI correlation coding(a)The neuronal ensemble ISI correlation coding method can encode the pulseinput stimulus pattern. (b)The neuronal ensemble ISI correlation coding method can encode theGaussian white noise input stimulus pattern.(c)The encoding effect of the mix stimulus pattern by the neuronal ensemblerate coding method is not obvious.Conclusions1.The hippocampus CA3 neurons spiking small world neural network (SWNN)model can simulate the sparse activity of hippocampus CA3 area neuronssuccessfully.The simulation result is accordance with the sparse firing (mean firingrate is less than 10%)character of hippocampus CA3 area.2.The temporal-spatial sequence patterns of hippocampus CA3 neurons aredifferent under the three different types of stimulus input pattern (pulse input,Gaussian white noise input and the linear superposition input of the above stimulusinput).3.The neuronal ensemble rate coding method can encode the pulse inputstimulus pattern and the Gaussian white noise stimulus pattern.The neuronalensemble rate coding method can not distinguish between the pulse input andGaussian white noise input stimulus pattern.The coding effect of ISI correlation isbetter than the rate coding.The neuronal ensemble ISI correlation coding method canencode the pulse input stimulus pattern and the Gaussian white noise stimulus pattern.But the encoding effect of the mix stimulus pattern by the neuronal ensemble ratecoding method is not obvious.