5 research outputs found
Multi-electrode analysis of pattern generation and its adaptation to reward
Much behaviour is controlled by neural circuits known as central pattern generators
(CPGs). The aim of the work presented in this thesis was to uncover general
mechanisms that modify the behavioural output of CPGs in ways that maximise
adaptive fitness. To achieve this aim it was necessary to monitor populations of
neurons associated with a CPG that responds to changes in sensory reward. I used
multi-electrode arrays (MEAs) to monitor neuronal populations in semi-intact
preparations of the snail Lymnaea stagnalis. Spike patterns associated with cycles of
the feeding CPG were readily recorded in the buccal, cerebral and pedal ganglia. A
sensory food stimulus accelerated the CPG and this acceleration was shown to depend
on dopamine. Single-trial conditioning on the MEA allowed fictive feeding to be
induced by a previously neutral taste stimulus. In addition to the activity of the feeding
CPG the MEA also revealed a second neuronal population that had not previously been
characterized. This population fires continuously in-between the cycles of the feeding
CPG but becomes quiescent for a variable period following each cycle. The duration of
this quiescent period often predicted the timing of the next activation of the CPG.
Stimulation of a nerve associated with food reward failed to activate the CPG during
the quiescent period, indicating that it reflects a ‘network refractory period’ (NRP) of
the kind previously observed in locomotor CPGs. The sucrose and dopamine stimuli
both significantly shortened the NRP. These results show that the MEA recording
method can identify distinct populations of neurons associated with adaptive feeding
behaviour, and suggest a general mechanism that allows a CPG to adapt its behavioural
output to maximise rewar
Spatially structured information in attractor neural networks using metric connectivity
Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, departamento de IngenierÃa Informática, noviembre de 201
Self-organizing continuous attractor networks and motor function.
Motor skill learning may involve training a neural system to automatically perform sequences of movements, with the training signals provided by a different system, used mainly during training to perform the movements, that operates under visual sensory guidance. We use a dynamical systems perspective to show how complex motor sequences could be learned by the automatic system. The network uses a continuous attractor network architecture to perform path integration on an efference copy of the motor signal to keep track of the current state, and selection of which motor cells to activate by a movement selector input where the selection depends on the current state being represented in the continuous attractor network. After training, the correct motor sequence may be selected automatically by a single movement selection signal. A feature of the model presented is the use of 'trace' learning rules which incorporate a form of temporal average of recent cell activity. This form of temporal learning underlies the ability of the networks to learn temporal sequences of behaviour. We show that the continuous attractor network models developed here are able to demonstrate the key features of motor function. That is, (i) the movement can occur at arbitrary speeds; (ii) the movement can occur with arbitrary force; (iii) the agent spends the same relative proportions of its time in each part of the motor sequence; (iv) the agent applies the same relative force in each part of the motor sequence; and (v) the actions always occur in the same sequence