Integrate Different Neural Models Using Double Channels of Pulse Coupled Neural Network

Integrate Different Neural Models Using Double Channels of Pulse Coupled Neural NetworkXiaodong GuDepartment of Electronic EngineeringFudan UniversityShanghai, P.R.China, 200433E-mail: guxiaodong@Abstract—Double-channel modulation multiplied of PCNN (Pulse Coupled Neural Network), based on the experimental observations of synchronous pulse bursts in the cat cortex, combines space-coding with time-coding and can be used to integrate different neural models to improve the performance of the whole system..I. PROBLEM STATEMENTIn the field of artificial neural networks, many models have been introduced to solve different problems. One approach to improving the ability of the whole system is to integrate different models together. However, when a neural network is used to integrate other neural models, a network based on space-coding is not enough to assemble the different specified models because it neglects time-coding. Time plays an important role in information coding. For example, the relative timing between spikes from neurons in one’s ears conveys information to his or her brain about the locations of sound sources in space. The timing spikes has already been well established as a means for conveying information in the visual system in files, in the auditory system of bats, and so on. However, for traditional artificial neural networks called AFR (Average-Firing-Rate) neural networks, each neuron only uses the sum of pulse-average-firing-rates of signals received from other neurons during a short period of time as the input signal, so the information coded by the accurate time, when pulses emitted by other neurons arrive, is lost. In 1995, Hopfield indicated that the sizes of variables are represented by the explicit time at which action potentials occur, rather than by the more usual ‘firing rate’ of neurons [1]. On the same issue, Sejnowski thinks that time may be a new neural code [2]. Therefore, the network used to integrate different models had better not be an AFR neural network, but a space-time coding neural network. Lately, SNNs (Spiking Neural Networks), pulse-emitting and space-time coding models, have attracted the attention of many researches [3-5] because compared with AFR neural networks, they mimic real neurons better and have more powerful computation performance [6]. There are many SNN models, such as oscillatory correlation model [7] and PCNN [8,9] and it is an open question as to which one is better at integrating different neural models?II. CURRENT RESEARCHEckhorn introduced the linking field network [8] based on the phenomena of synchronous pulse bursts in the cat visual cortex in 1990. Synchronous firing was a candidate for blinding different features together in the cortex. After three years, Johnson developed PCNN [9] based on the linking model. PCNN can be applied in many fields, such as image processing, object detection, and optimization [10-15]. PCNN consider the pulse-forming mechanism, the method of transmission and reception, and the interactions based on the phase properties of the pulse trains. It is a minimal model to explain the experimentally observed synchronous feature-dependent activity of neural assemblies over large cortical distances in the cat cortex.PCN (Pulse Coupled Neuron) consists of three parts: the receptive field, the modulation field, and the pulse generator (See Fig.1). A PCN receives signals through the receptive field. There are two channels in a PCN. One channel called F channel is feeding input (F); the other called L channel is linking input (L).There are different synapses with different time constants in each channel. In general, the feeding connection has a slower characteristicresponse time constant than that of the linking connection. In modulation field, the linking input L is a dded a constant positive bias firstly. Then it is multiplied by the feeding input F and the bias is taken to be unity. In Fig.1, β is the linking strength. The total internal activity U is the result of multiplied modulation. In the pulse generator, the total internal activity U is compared with the threshold θto obtain the output and the output feedbacks to adjust the threshold.Double-channel modulation multiplied is the main characteristic of PCNN, and we have used it in image processing efficiently, such as image thinning [14], image shadow removal [15]. We think that it is also useful in integrating different specified models. When integrating different specified models, output signals of a neuron in integrated specified models can be sent to the different synapses with different time constants in one channel, or in both channels of the corresponding PCN. PCNN can integrate specified models by choosing different parameters, such as the time constant of each synapse in double channels.III.KEY AVENUESSNNs consider space-coding and time-coding together, so they have more powerful computation performance than traditional AFR neural networks. Therefore they, rather than AFR networks, are considered better at integrating different neural models. As one of many SNN models, double-channel modulation multiplied of PCNN makes it different from other models and makes it more suitable for integrating different neural networks than other SNN models.Now we plan to use PCNN to integrate specified models to mimic the attention selection of the brain in the developmental robot. In this scheme, on the one hand, using oscillatory correlation network introduced by Wang or PCNN finds each object in the testing scene in sequence; on the other hand, using Kohonen network or PCNN extracts the feature of the interesting object as the prior knowledge. 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