Kazushi Murakoshi

Firing time of neuron by interference between synaptic inputs

The relation of intensity of one input to firing time monotonically decreases: a strong input generates a spike faster while a weak one is slower. In reality, however, numerous synaptic inputs are gathered into a single neuron at various arrival times. In such a situation it is not easy to predict firing time. In this paper, we examine firing time by interference between two synaptic inputs through the Hodgkin-Huxley (HH) model, which is a well-studied standard model of spike activity. The relations of the intensity to the firing do not always monotonically decrease. We consider why this phenomenon occurs. The analysis indicates that the potassium current controlled by the variable n, which is the delayed rectifier, is crucial for the phenomenon. We also calculated the firing time in the case of the integrate-and-fire (IF) type model, in which such a rectifiable current does not flow, with the same input form as the HH model. All relations of the intensity to the firing time monotonically decrease. This is inconsistent with the case of the HH equations. This result casts doubt on patterns of firing time in the IF type model.

A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses

We propose a neural circuit model forming a semantic network with exceptions using the spike-timing-dependent plasticity (STDP) of inhibitory synapses. To evaluate the proposed model, we conducted nine types of computer simulation by combining the three STDP rules for inhibitory synapses and the three spike pairing rules. The simulation results obtained with the STDP rule for inhibitory synapses by Haas et al. [Haas, J.S., Nowotny, T., Abarbanel, H.D.I., 2006, Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. J. Neurophysiol. 96, 3305-3313] are successful, whereas, the other results are unsuccessful. The results and examinations suggested that the inhibitory connection from the concept linked with an exceptional feature to the general feature is necessary for forming a semantic network with an exception.

Reducing topological defects in self-organizing maps using multiple scale neighborhood functions

In this paper, we propose a method of reducing topological defects in self-organizing maps (SOMs) using multiple scale neighborhood functions. The multiple scale neighborhood functions are inspired by multiple scale channels in the human visual system. To evaluate the proposed method, we applied it to the traveling salesman problem (TSP), and examined two indexes: the tour length of the solution and the number of kinks in the solution. Consequently, the two indexes are lower for the proposed method. These results indicate that our proposed method has the ability to reduce topological defects.

Image correction method for the colour contrast effect using inverse processes of the brain

In the colour contrast effect, the impression of a colour changes according to the situation; cases occur in which the colour appearance is misunderstood. We propose an image signal processing method for preventing such misperception of colour. Many conventional image improving methods emphasize the contrast of images as same as the brain does. However, by their processes, the colour contrast effect is not canceled; we misunderstand the colour. The objective of this study is to perceive original colour. Therefore, we propose an image correction method using inverse processes of the brain in order to cancel the processes of the brain, the colour contrast effect. We verified whether the proposed method corrected the colour contrast effect by conducting a psychological experiment. The results show that the method succeeds in canceling the colour contrast effect.

A parameter control method inspired from neuromodulators in reinforcement learning

The brain gains appropriate behaviors, which get rewards and escapes punishments by trial-and-error. Reinforcement learning models such a system by an engineering approach. Neuromodulators, which project widely in the brain and adjust functions in each brain part, are matched with parameters of reinforcement learning. We propose a reinforcement learning algorithm, which can follow sudden changes in environment by considering how neuromodulators affect behaviors. This algorithm improves actions by controlling the parameters of reinforcement learning after the obtained reward decreased as compared with the past. Computer simulation shows that the robots with the proposed algorithm are able to respond flexibly to sudden environmental changes.

A neural circuit model of emotional learning using two pathways with different granularity and speed of information processing

We propose a neural circuit model of emotional learning using two pathways with different granularity and speed of information processing. In order to derive a precise time process, we utilized a spiking model neuron proposed by Izhikevich and spike-timing-dependent synaptic plasticity (STDP) of both excitatory and inhibitory synapses. We conducted computer simulations to evaluate the proposed model. We demonstrate some aspects of emotional learning from the perspective of the time process. The agreement of the results with the previous behavioral experiments suggests that the structure and learning process of the proposed model are appropriate.

A neural circuit model for changing the amount of information maintained in short-term memory depending on stimuli relationships.

We propose a neural circuit model of changes in amount of information maintained in short-term memory depending on stimuli relationships. The relationships between stimuli are represented by the synchronous firings of overlapping neuronal groups for semantically related stimuli and the excitatory mutual connections for semantically unrelated but simultaneously presented stimuli. We conduct computer simulations to confirm our proposed neural circuit model. The resultant numbers of stored informational input patterns are almost consistent with the maximum numbers in the psychological experiments for both semantically related and unrelated stimuli. This agreement with the psychological experiments suggests that the structure and informational representation of the proposed model are appropriate.

Synchrony and asynchrony of coincident spikes of neural populations depending on line drawings

This research presents a biologically realistic model of the primary visual area to account for how line drawings are identical by synchrony and asynchrony of coincident spikes of neural populations which originate from the neural network with strong circular and weak mutual excitatory connections. This model assumes that in a column a simple cell and an endstopped cell of the same orientation selectivity have mutual excitatory connections, all endstopped cells have mutual excitatory connections, and simple cells of the different orientation selectivities have inhibitory connections via an inhibitory neuron. These assumptions led to the results that within some hundreds of milliseconds time range the cross-correlograms of two line segments in the cross and T junction stimuli showed asynchrony while those in the right angle stimulus showed synchrony. The results of synchrony and asynchrony indicate early local segmentations of line drawings in the psychophysically realistic time scale.