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A Connectionist Model Based on Physiological Properties of the Neuron Proceedings of the International Joint Conference IBERAMIA/SBIA/SBRN 2006 - 1st Workshop on Computational Intelligence (WCI’2006), Ribeir˜aoPreto, Brazil, October 23–28, 2006. CD-ROM. ISBN 85-87837-11-7 A Connectionist Model based on Physiological Properties of the Neuron Alberione Braz da Silva Joao˜ Lu´ıs Garcia Rosa Ceatec, PUC-Campinas Ceatec, PUC-Campinas Campinas, SP, Brazil Campinas, SP, Brazil [email protected] [email protected] Abstract 2 Intraneuron Signaling Recent research on artificial neural network models re- 2.1 The biological neuron and the intra- gards as relevant a new characteristic called biological neuron signaling plausibility or realism. Nevertheless, there is no agreement about this new feature, so some researchers develop their The intraneuron signaling is based on the principle of own visions. Two of these are highlighted here: the first is dynamic polarization, proposed by Ramon´ y Cajal, which related directly to the cerebral cortex biological structure, establishes that “electric signals inside a nervous cell flow and the second focuses the neural features and the signaling only in a direction: from neuron reception (often the den- between neurons. The proposed model departs from the pre- drites and cell body) to the axon trigger zone” [5]. vious existing system models, adopting the standpoint that a Based on physiological evidences of principle of dy- biologically plausible artificial neural network aims to cre- namic polarization the signaling inside the neuron is per- ate a more faithful model concerning the biological struc- formed by four basic elements: Receptive, responsible for ture, properties, and functionalities of the cerebral cortex, input signals; Trigger, responsible for neuron activation not disregarding its computational efficiency. threshold; Signaling, responsible for conducting and keep- ing the signal; and Secretor, responsible for signal releasing to another related neuron. 1 Introduction These elements are related to the dendritic region, cell body, axon, and presynaptic terminals of the biological neu- ron, respectively [5]. Classical connectionist models are based on the pioneer- The cell body is the neuron portion where cell genes are ing McCulloch-Pitts neuron proposal [9] although their op- and is the place where protein syntheses occur. Consid- erations are far from the way human brain works. Artificial ered the neural cell metabolic center, the cell body is the Neural Networks (ANN) nowadays are designed to mimic, main control place of ordinary neural electric signals. From with limited realism, some brain functionalities including the cell body - or soma - originate two signal conducting pattern recognition, new information acquisition, and mo- branches, the axon and the dendrites. tor performance [7, 3]. The main function of a dendrite is the reception of sig- More recently, some researchers of this area have ap- nals originated from other cells. The axon, in general, proached the proposition of new models based on cerebral comes from a cell body area called axon hillock [5], located structure, that is, on cerebral cortex biological properties. on the opposite side of the tree trunk formed by dendrites, This new direction on research makes noticeable a new and ended in a micro-ramification form called axonal tree characteristic to be incorporated to ANNs: the biological or axonal branch. These branches have in their terminals plausibility. synaptic buttons, responsible for connectivity between neu- However there is no agreement about the definition of rons. this new concept. For this reason, biological plausibility The axon is considered the main neuron conductivity is still being analyzed under several aspects. One of these unit [5], responsible for the transmission of electric signals main visions concerns the cerebral cortex structure. (action potentials or spikes) from the axonal cone - area sit- Taken the ideas presented previously into consideration, uated at the end of cell body - to the presynaptic terminals, this paper proposes a new artificial neuron model based on keeping the same amplitude and duration, varying only in biological properties of the physiological neuron. intensity or frequency. 1 The transmission rules of neuron output signaling are none [3, 5], originating in a specific trigger zone - the be- limited, but very efficient. This efficiency is verified ginning of the axon. The fact that the action potential, the through fast propagation and signal properties maintenance neuron conducting signal, is an “all-or-none” signal or bi- between initial and final points of involved axons [5, 6]. nary is motivated by the fact that a stimulus smaller than There are other elements, co-responsible for propagation threshold does not produce signals and a stimulus greater speed and amplitude maintenance of action potentials, for than threshold produces the same signal independently of example, the glial cell and the Ranvier nodules. The glial amplitude variation and stimulus duration. Consequently, cell involves the neural cell bodies and their axons in a pro- there are only two pulse characteristics for the informa- cess called myelination, which provides as an insulating fi- tion transmission process to be taken into consideration: the nal product, the myelin sheaths, white fatty extensions of amount of action potentials and the time intervals between neuroglial cells (or Schwann cells), involving the axon in them [5, 12, 4]. several layers. These sheaths are interrupted at regular in- The action potential is generated only if the input signal, tervals by the nodes of Ranvier, which are electrically non- or the weighted sum of the input signals by their synaptic insulated and are responsible for the action potential regen- weights, is greater than a certain activation threshold, that eration [5, 6]. is, once the threshold is reached, the additional increase in There is also the ionic mechanism, which can act as a amplitude of the input signal implies in an increase of the signaling structure in an excitable cell, independently of action potential frequency and not in an increase of the po- synapse localization. This mechanism refers to the cur- tential amplitude [5]. Since action potentials are of the kind rent flow generated by the ionic movement through protein “all-or-none” and conducted without alterations through the membranes - channels - generating the signals (membrane whole axon, the information contained in the signal is rep- potentials) in neural cells. These alterations in resting po- resented only by the frequency and pulse amount, without tentials of the cell membrane provoked by the ionic current taking into account their amplitude. So, the larger the stim- flowing through open channels produce input signals that ulus duration, the longer the action potential sequence lasts, develop the intraneuron signaling process. and consequently, the larger the amount of pulses [5]. The intraneuron signaling process begins after neuron Once the synaptic terminals are reached, action poten- binding, either directly or by means of modulation (see item tials stimulate chemical neurotransmitter releasing by the about interneuron signaling), altering the membrane poten- cell, considered as analogical output signals. In this graded tial and transforming chemical potential energy in an elec- transmitter releasing, there is, repeatedly, the transforma- tric signal called synaptic potential. The synaptic potential tion of digital signal, represented by action potential, in is graded and its amplitude is directly related to volume and analogical signal. Also in this process, the exact amount releasing time of chemical neurotransmitters in interneuron of neurotransmitters to be released by the cell is defined by signaling process. Once generated, the input signal - synap- the total number of action potentials in a given time inter- tic potential - often flows through dendrites and cell body val [5]. After an action potential releasing, a very short re- where it is accumulated until reaching a certain threshold, fractoriness time period happens, that is, a period of lesser responsible for pulse or action potential generation. This in- excitability. This time period - called refractory period - can put signal can be excitatory or inhibitory, so the latter tries be divided in: (1) absolute, when happens immediately after to prevent action potential from releasing. the action potential; and (2) relative, which is subsequent to The over stimulus of the target cell makes the inputs the absolute refractory period. compete. In the “struggle,” the weaker synapses are elimi- In the absolute refractory period, it is impossible to ex- nated and the stronger synapses keep the inputs to the post- cite a cell; the same does not happen in the relative period, synaptic cell [5]. These competitive inputs are combined when a pulse can be released [5, 11]. into the postsynaptic neuron through a process called neu- ronal integration, which sum up all the signals received by 2.2 The artificial neuron and the intraneu- a neuron and takes the decision of firing or not an action ron signaling potential, which reflects the brain fundamental ability of decision-making in the neuron [5, 11]. In neuronal inte- The neuron is considered the fundamental operational gration process, the brain ability in opting for one of the unit of a neural network - natural or artificial -, due to its ca- competitive alternatives (fire or not fire), selecting one and pacity of processing and transmitting
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