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Dive into the research topics where Everton J. Agnes is active.

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Featured researches published by Everton J. Agnes.


Nature Communications | 2015

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Friedemann Zenke; Everton J. Agnes; Wulfram Gerstner

Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.


PHYSICS, COMPUTATION, AND THE MIND - ADVANCES AND CHALLENGES AT INTERFACES: Proceedings of the 12th Granada Seminar on Computational and Statistical Physics | 2013

Spike timing analysis in neural networks with unsupervised synaptic plasticity

Beatriz E. P. Mizusaki; Everton J. Agnes; Leonardo Gregory Brunnet; R. Erichsen

The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following eac...


international conference on artificial neural networks | 2012

Associative memory in neuronal networks of spiking neurons: architecture and storage analysis

Everton J. Agnes; R. Erichsen; Leonardo Gregory Brunnet

A synaptic architecture featuring both excitatory and inhibitory neurons is assembled aiming to build up an associative memory system. The connections follow a hebbian-like rule. The network activity is analyzed using a multidimensional reduction method, Principal Component Analysis (PCA), applied to neuron firing rates. The patterns are discriminated and recognized by well defined paths that emerge within PCA subspaces, one for each pattern. Detailed comparisons among these subspaces are used to evaluate the network storage capacity. We show a transition from a retrieval to a non-retrieval regime as the number of stored patterns increases. When gap junctions are implemented together with the chemical synapses, this transition is shifted and a larger number of memories is associated to the network.


PHYSICS, COMPUTATION, AND THE MIND - ADVANCES AND CHALLENGES AT INTERFACES: Proceedings of the 12th Granada Seminar on Computational and Statistical Physics | 2013

UNSUPERVISED LEARNING IN NEURAL NETWORKS WITH SHORT RANGE SYNAPSES

Leonardo Gregory Brunnet; Everton J. Agnes; Beatriz E. P. Mizusaki; R. Erichsen

Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.


Annual Review of Neuroscience | 2017

Inhibitory Plasticity: Balance, Control, and Codependence

Guillaume Hennequin; Everton J. Agnes; Tim P. Vogels


Physica A-statistical Mechanics and Its Applications | 2012

Model architecture for associative memory in a neural network of spiking neurons

Everton J. Agnes; R. Erichsen; Leonardo Gregory Brunnet


Physica A-statistical Mechanics and Its Applications | 2010

Synchronization regimes in a map-based model neural network

Everton J. Agnes; R. Erichsen; Leonardo Gregory Brunnet


COSYNE 2015 | 2015

Hebbian and non-Hebbian plasticity orchestrated to form and retrieve memories in spiking networks

Friedemann Zenke; Everton J. Agnes; Wulfram Gerstner


COSYNE 2014 | 2014

Learning Multi-Stability in Plastic Neural Networks

Friedemann Zenke; Everton J. Agnes


PHYSICS, COMPUTATION, AND THE MIND - ADVANCES AND CHALLENGES AT INTERFACES: Proceedings of the 12th Granada Seminar on Computational and Statistical Physics | 2013

Strategies to associate memories by unsupervised learning in neural networks

Everton J. Agnes; Beatriz E. P. Mizusaki; R. Erichsen; Leonardo Gregory Brunnet

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Leonardo Gregory Brunnet

Universidade Federal do Rio Grande do Sul

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R. Erichsen

Universidade Federal do Rio Grande do Sul

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Beatriz E. P. Mizusaki

Universidade Federal do Rio Grande do Sul

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Wulfram Gerstner

École Polytechnique Fédérale de Lausanne

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