Subha Danushika Fernando

Learn More
Unconstrained growth of synaptic connectivity and the lack of references to synaptic depression in Hebb's 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(More)
Malaria being one of the serious health burdens especially in Indian population is conventionally diagnosed by expert pathologists through microscopic observation of stained peripheral blood smears. In order to provide rapid and efficient healthcare support to the common people at rural areas where experts are not (often) available, there is indeed a(More)
Swarm cognition is the field that explores the possibility of implanting human cognitive functions on machines by transplanting the processes in naturally self-organized colonies. These natural colonies, especially ant colony, honey bee colony, etc, have been deeply studied to explore the factors which enable them to simulate high cognitive functions, such(More)
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(More)
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(More)
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(More)
  • 1