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I E T de Araujo

Publications and source records attributed to I E T de Araujo.

4 recordsLinked to original sources

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.

Excitatory Postsynaptic Potentials↗

Representation of umami taste in the human brain.

Umami taste stimuli, of which an exemplar is monosodium glutamate (MSG) and which capture what is described as the taste of protein, were shown using functional MRI (fMRI) to activate similar cortical regions of the human taste system to those activated by a prototypical taste stimulus, glucose. These taste regions included the insular/opercular cortex and the caudolateral orbitofrontal cortex. A part of the rostral anterior cingulate cortex (ACC) was also activated. When the nucleotide 0.005 M inosine 5'-monophosphate (IMP) was added to MSG (0.05 M), the blood oxygenation-level dependent (BOLD) signal in an anterior part of the orbitofrontal cortex showed supralinear additivity; this may reflect the subjective enhancement of umami taste that has been described when IMP is added to MSG. These results extend to humans previous studies in macaques showing that single neurons in these taste cortical areas can be tuned to umami stimuli.

Brain↗

Self-organizing continuous attractor networks and path integration: one-dimensional models of head direction cells.

Some neurons encode information about the orientation or position of an animal, and can maintain their response properties in the absence of visual input. Examples include head direction cells in rats and primates, place cells in rats and spatial view cells in primates. 'Continuous attractor' neural networks model these continuous physical spaces by using recurrent collateral connections between the neurons which reflect the distance between the neurons in the state space (e.g. head direction space) of the animal. These networks maintain a localized packet of neuronal activity representing the current state of the animal. We show how the synaptic connections in a one-dimensional continuous attractor network (of for example head direction cells) could be self-organized by associative learning. We also show how the activity packet could be moved from one location to another by idiothetic (self-motion) inputs, for example vestibular or proprioceptive, and how the synaptic connections could self-organize to implement this. The models described use 'trace' associative synaptic learning rules that utilize a form of temporal average of recent cell activity to associate the firing of rotation cells with the recent change in the representation of the head direction in the continuous attractor. We also show how a nonlinear neuronal activation function that could be implemented by NMDA receptors could contribute to the stability of the activity packet that represents the current state of the animal.

Animals↗

Self-organizing continuous attractor networks and path integration: two-dimensional models of place cells.

Single-neuron recording studies have demonstrated the existence of neurons in the hippocampus which appear to encode information about the place where a rat is located, and about the place at which a macaque is looking. We describe 'continuous attractor' neural network models of place cells with Gaussian spatial fields in which the recurrent collateral synaptic connections between the neurons reflect the distance between two places. The networks maintain a localized packet of neuronal activity that represents the place where the animal is located. We show for two related models how the representation of the two-dimensional space in the continuous attractor network of place cells could self-organize by modifying the synaptic connections between the neurons, and also how the place being represented can be updated by idiothetic (self-motion) signals in a neural implementation of path integration.

Animals↗