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Activity of Spiking Neurons Stimulated by External Signals of Different Wave Natalie Sanghvi, Natacha Gueorguieva [email protected] Computer Science Department Spiking neuron systems gained increasing interest in recent years because they represent spatio-temporal relations within simulated systems, unlike the spatial simple neuron models found in artificial neural systems. They are also closer to biophysical models of neurons, synapses, and related elements and their synchronized firing of neuronal assemblies could serve the brain as a code for feature binding and pattern segmentation. The human brain consists of a large number of neurons that are interconnected with each other. On average, each neuron is connected to other neurons through about 10 000 synapses. The brain network of neurons forms a massively parallel information processing system. This contrasts with conventional computers, in which a single processor executes a sequential series of instructions. A typical neuron consists of dendrites, soma and axon. Dendrites receive and deliver signals and act like an “input device”. Soma is the “central processing unit” that generates a signal if the total input exceeds a certain threshold (about -30 mV) and the axon transmits the signals to other neurons. Synapses are the contact points for transferring information between neurons and facilitate the connection between axons and dendrites. The pulses or spikes (also called action potentials) last about 1-2 ms in amplitude of 100 mV. The neuron sends out spikes of electrical activity through the axon (the output and conducting structure), which can split into thousands of branches. At the end of each branch, a synapse converts the activity from the axon into electrical effects that inhibit or excite activity on the contacted (target) neuron. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity (an action potential) down its axon. This spiking event is also called depolarization, and it is followed by a refractory period, during which the neuron is unable to fire. In this research we investigate the integrate-and-fire model (I&F) which is based on the idea that the neuron adds and subtracts excitatory and inhibitory inputs until it reaches a threshold, at which point it fires a single impulse or action potential. The goal is to perform spiking-neuron simulations with external input signals of different wave: sinusoidal, two combined absolute sinusoidal signals, square and step wave signals and to analyze the timing and number of spikes of membrane potential.