Percorrer por autor "Duarte, Renato"
A mostrar 1 - 2 de 2
Resultados por página
Opções de ordenação
- Self-organized sequence processing in recurrent neural networks with multiple interacting plasticity mechanismsPublication . Duarte, Renato; Petersson, Karl MagnusThe highly recurrent connectivity encountered in the neocortical circuitry makes recurrent neural network (RNN) models highly suitable when investigating the computational properties of biologically inspired model neurodynamics. The recent reservoir computing (RC) models, an extension of the RNN paradigm, provide a framework for state-dependent computations, where information is encoded in the form of state-space trajectories, which is similar to recent ndings in neurobiology. Over the past few years, several attempts have been made to endow these network models with adaptive mechanisms, capable of mimicking the various neural plasticity mechanisms known to exist in the brain and to play a fundamental role in shaping the dynamics and information processing capabilities of the underlying neural networks. In this thesis, we analyze the dynamic properties of a simple reservoir computer model, with self-organizing plasticity mechanisms operating concomitantly. We investigate how di erent combinations of three forms of biologically inspired adaptive mechanisms shape the reservoir's dynamic properties and their e ectiveness in acquiring an internal representation of structured symbol sequences. We demonstrate, replicating previous work, that only combined do these mechanisms allow the dynamic reservoir networks to achieve an input separation that outperforms static (i.e., without plasticity) reservoir networks. We further assess how the symbol sequences are internally represented in di erent network settings. All reservoir networks are shown to re ect the input structure in their state dynamics, but plasticity is clearly bene cial by modifying network parameters, increasing the network's ability to `learn' the temporal structure of the input sequences.
- The Tripod neuron: a minimal structural reduction of the dendritic treePublication . Quaresima, Alessio; Fitz, Hartmut; Duarte, Renato; Broek, Dick van den; Hagoort, Peter; Petersson, Karl MagnusNeuron models with explicit dendritic dynamics have shed light on mechanisms for coincidence detection, pathway selection and temporal filtering. However, it is still unclear which morphological and physiological features are required to capture these phenomena. In this work, we introduce the Tripod neuron model and propose a minimal structural ion of the dendritic tree that is able to reproduce these computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modelled as an adaptive, exponential integrate-and-tire neuron. It incorporates dendritic geometry, membrane physiology and receptor dynamics as measured in human pyramidal cells. We characterize the response of the Tripod to glutamatergic and GABAergic inputs and identify parameters that support supra-linear integration, coincidence-detection and pathway-specific gating through shunting inhibition. Following NMDA spikes, the Tripod neuron generates plateau potentials whose duration depends on the dendritic length and the strength of synaptic input. When titled with distal compartments, the Tripod encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding, and we show that it solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod neuron can be used efficiently in simulations of larger cortical circuits.