Subha Fernando

Observed Stent's anti-Hebbian postulate on dynamic stochastic computational synapses

Unconstrained growth of synaptic connectivity and the lack of references to synaptic depression in Hebbs postulate has diminished its value as a learning algorithm. While spike timing dependent plasticity and other synaptic scaling mechanisms have been studying the possibility of regulating synaptic activity on neuronal level, we studied the possibility of regulating the synaptic activity of Hebbs neurons on dynamic stochastic computational synapses. The study was conducted on fully connected network with four artificial neurons where each neuron consisted of thousands of artificial stochastic synapses that are modeled with transmitters and receptors. The synapses updated their stochastic states dynamically according to the spike arrival time to that synapses. The activity of these synapses was regulated by a new stability promoting mechanism. Results support the following findings: (i) the synchronous activity between presynaptic (cell A) and postsynaptic (cell B) neuron increases the activity of A. (ii) Asynchronous activation of these two neurons decreases As activity if one of the following conditions are satisfied (a). if activity of the other presynaptic neurons of the postsynaptic neuron B is asynchronous with the As activity or (b) if B is in a depressed state when activity of presynaptic neuron A is increased. (iii) the introduced stability promoting mechanism exhibited similar to the Homeostatic synaptic plasticity process and encouraged the emergence of Hebbs postulate and its anti-Hebbian mechanisms. Further, we demonstrated the metabolic changes that could occur inside Hebbs neurons when such an activity takes place on a dynamic stochastic neural network.

Modeling Function of Nectar Foraging of Honeybees using Operant Conditioning

of the colonies of small unsophisticated agents have been analyzed in the literature with the purpose of developing efficient algorithms to solve complex, dynamic and burden problems in other societies. Among them, only a few research have been conducted in the area of swarm cognition which tries to understand the cognitive behaviors exhibited by human brain by using the cognitive behaviors demonstrated by a colony as a self-organized entity. In this aspect, the role of a neuron and a role of a insect have been equally considered as an unsophisticated agent which adjusts its actions according to the fluctuations of local environment without knowing any global information. The cognitive behavior, such as effective labor division of honeybees at food foraging process, was analyzed in this paper and has been exploited under operant conditioning. The paper has proposed a simple but effective computational model which demonstrates that, the positive reinforcement and the negative reinforcement in operant conditioning are the real factors that affect to the emergent of cognitive behaviors at swarm level when swarm is observed as a self-organized entity.

Spike-timing-dependent plasticity and short-term plasticity jointly control the excitation of hebbian plasticity without weight constraints in neural networks

Hebbian plasticity precisely describes how synapses increase their synaptic strengths according to the correlated activities between two neurons; however, it fails to explain how these activities dilute the strength of the same synapses. Recent literature has proposed spike-timing-dependent plasticity and short-term plasticity on multiple dynamic stochastic synapses that can control synaptic excitation and remove many user-defined constraints. Under this hypothesis, a network model was implemented giving more computational power to receptors, and the behavior at a synapse was defined by the collective dynamic activities of stochastic receptors. An experiment was conducted to analyze can spike-timing-dependent plasticity interplay with short-term plasticity to balance the excitation of the Hebbian neurons without weight constraints? If so what underline mechanisms help neurons to maintain such excitation in computational environment? According to our results both plasticity mechanisms work together to balance the excitation of the neural network as our neurons stabilized its weights for Poisson inputs with mean firing rates from 10 Hz to 40 Hz. The behavior generated by the two neurons was similar to the behavior discussed under synaptic redistribution, so that synaptic weights were stabilized while there was a continuous increase of presynaptic probability of release and higher turnover rate of postsynaptic receptors.

Synaptic redistribution and variability of signal release probability of Hebbian neurons at low-firing frequencies in a dynamic stochastic neural network

This paper presents the finding of the research we conducted to evaluate the variability of signal release probability at Hebb’s presynaptic neuron under different firing frequencies in a dynamic stochastic neural network. A modeled neuron consisted of thousands of artificial units, called ‘transmitters’ or ‘receptors’ which formed dynamic stochastic synaptic connections between neurons. These artificial units were two-state stochastic computational units that updated their states according to the signal arriving time and their local excitation. An experiment was conducted with three stages by updating the firing frequency of Hebbian neuron at each stage. According to our results, synaptic redistribution has improved the signal transmission for the first few signals in the signal train by continuously increasing and decreasing the number of postsynaptic ‘active-receptors’ and presynaptic ‘active-transmitters’ within a short time period. In long-run, at low-firing frequency, it has increased the steady state efficacy of the synaptic connection between the Hebbian presynaptic and the postsynaptic neuron in terms of the signal release probability of ‘active-transmitters’ in the presynaptic neuron as observed in biology. This ‘low-firing’ frequency of the presynaptic neuron has been identified by the network by comparing it with the ongoing frequency oscillation of the network.

Spike-timing dependent plasticity with release probability supported to eliminate weight boundaries and to balance the excitation of Hebbian neurons

Spike-timing-dependent plasticity is considered as the key underline mechanism which processes the signals in brain. With the introduction of spike-timing dependent plasticity as a long-lasting synaptic modification, neural networks have been driven to era of processing information on the basis of relative timing between presynaptic and postsynaptic action potentials. One of the main drawbacks that impinged the successive progress of the researches in this area is the constraints that have been put on the weight algorithms of these networks. Here, we analyzed the possibility of eliminating these constraints from the neural networks by introducing release probability and dynamic multiple stochastic synaptic connections between neurons. Our results have proven the possibility of balancing the excitation of the neural networks as our modeled network stabilizes its weights distribution for Poisson inputs with frequency less than 40 Hz. Further, the excited synapses have resembled the median of the weight distribution into unimodal Gaussian distribution for input frequencies between 15 Hz to 40 Hz.

Synaptic Plasticity as Agent Communication

Neurons are considered as main computational units of the human brain, are working together with millions of synapses to convey information. The processes of information decoding and neurons’ communication mechanisms are still in a debate. Apart from the numerous researches into those areas, significant attention has given to the synaptic plasticity, which is suspected to have direct relationship with information processing of neurons. As per the biology, synaptic computation can be mainly divided into three plasticity processes, homeostasis, short-term and long-term. The long-term plasticity is considered as the main phenomena related to learning and memory formation; the roles of short-term plasticity and homeostasis plasticity have direct influences to synaptic efficacy and thereby to long-term plasticity. A few researches are being carried out to in cooperate the homeostasis plasticity to Artificial Neural Networks, are still unable to find real integrated mechanism without damaging to learning process. This paper proposes a new model for synaptic computation. In our approach, we understand the neurons as agents consisting of large number of constituent agents those play the roles of synapses, as transmitters or receivers. The statuses of these constituent agents are subjected to homeostasis and short-term plasticity. The number of active transmitters is an in-parameter for the learning processes. With the proposed model, through the active number of transmitters, learning can be explained as integrated process of three plasticity processes.

Decision Making Model Supporting Emergency Medical Care

Accuracy in a pre-hospital trauma triage plays a critical role in reducing trauma mortality in a way that appropriately chooses a patient with severe injuries. Although various triage criteria have been devised and tested, there is no computer-based system developed that helps ambulance teams can make a decision in an appropriate manner. This research proposes to develop Expert Helper, an Expert System which provides a user-friendly environment for the paramedics to decide whether patient to be posted to Critical Care Medical Centers (CCMC).

Modeling towards homeostatic plasticity in neuronal activities

The capacity to re-establish a normal rhythm after an excitation while adapting to external or internal stimuli is a process of great complexity. We propose an agent-based framework to model the homeostatic plasticity in neuronal activity incorporating the concept of selforganization. Our model provides the ability for neuroagents to adapt themselves in a series of activities after the excitements of synaptic inputs in a similar way to the nervous system, hence allowing the creation of diversification and a competitive environment.