Effects of Spike Timing on Winner-Take-All Competition in Model Cortical Circuits

2000 ◽  
Vol 12 (1) ◽  
pp. 181-194 ◽  
Author(s):  
Erik D. Lumer
Keyword(s):  
Action Potentials ◽  
Spike Timing ◽  
Neuronal Responses ◽  
Cortical Circuits ◽  
Cortical Tissue ◽  
Motor Action ◽  
Recurrent Excitation ◽  
Synaptic Interactions ◽  
Winner Take All ◽  
Take All

Synaptic interactions in cortical circuits involve strong recurrent excitation between nearby neurons and lateral inhibition that is more widely spread. This architecture is commonly thought to promote a winner-takeall competition, in which a small fraction of neuronal responses is selected for further processing. Here I report that such a competition is remarkably sensitive to the timing of neuronal action potentials. This is shown using simulations of model neurons and synaptic connections representing a patch of cortical tissue. In the simulations, uncorrelated discharge among neuronal units results in patterns of response dominance and suppression, that is, in a winner-take-all competition. Synchronization of firing, however, prevents such competition. These results demonstrate a novel property of recurrent cortical-like circuits, suggesting that the temporal patterning of cortical activity may play an important part in selection among stimuli competing for the control of attention and motor action.

scholarly journals Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits

2014 ◽  
Vol 26 (9) ◽  
pp. 1973-2004 ◽  
Author(s):  
Hesham Mostafa ◽  
Giacomo Indiveri
Keyword(s):  
Network Architecture ◽  
External Input ◽  
Spike Timing ◽  
Fine Tuning ◽  
Learning Mechanisms ◽  
State Dependent ◽  
Synaptic Dynamics ◽  
Experimental Findings ◽  
Winner Take All ◽  
Take All

Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. However, most of the models proposed in the literature are either not compatible with neuroanatomy and neurophysiology experimental findings, or are not robust to noise and rely on fine tuning of the parameters. In this work, we propose a novel model of sequence learning and generation that is based on the interactions among multiple asymmetrically coupled winner-take-all (WTA) circuits. The network architecture is consistent with mammalian cortical connectivity data and uses realistic neuronal and synaptic dynamics that give rise to noise-robust patterns of sequential activity. The novel aspect of the network we propose lies in its ability to produce robust patterns of sequential activity that can be halted, resumed, and readily modulated by external input, and in its ability to make use of realistic plastic synapses to learn and reproduce the arbitrary input-imposed sequential patterns. Sequential activity takes the form of a single activity bump that stably propagates through multiple WTA circuits along one of a number of possible paths. Because the network can be configured to either generate spontaneous sequences or wait for external inputs to trigger a transition in the sequence, it provides the basis for creating state-dependent perception-action loops. We first analyze a rate-based approximation of the proposed spiking network to highlight the relevant features of the network dynamics and then show numerical simulation results with spiking neurons, realistic conductance-based synapses, and spike-timing dependent plasticity (STDP) rules to validate the rate-based model.

Conductance-Based Integrate-and-Fire Models

1997 ◽  
Vol 9 (3) ◽  
pp. 503-514 ◽  
Author(s):  
Alain Destexhe
Keyword(s):  
High Frequency ◽  
Action Potentials ◽  
Time Course ◽  
Quantitative Model ◽  
Spike Timing ◽  
Repetitive Firing ◽  
Network Behavior ◽  
Neuronal Responses ◽  
Potential Generation ◽  
Integrate And Fire

A conductance-based model of Na+ and K+ currents underlying action potential generation is introduced by simplifying the quantitative model of Hodgkin and Huxley (HH). If the time course of rate constants can be approximated by a pulse, HH equations can be solved analytically. Pulse-based (PB) models generate action potentials very similar to the HH model but are computationally faster. Unlike the classical integrate-and fire (IAF) approach, they take into account the changes of conductances during and after the spike, which have a determinant influence in shaping neuronal responses. Similarities and differences among PB, IAF, and HH models are illustrated for three cases: high-frequency repetitive firing, spike timing following random synaptic inputs, and network behavior in the presence of intrinsic currents.

Soft-Reward Based Reinforcement Learning by Spiking Neural Networks

2011 ◽  
Vol 219-220 ◽  
pp. 770-773
Author(s):  
Wei Ya Shi
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Synaptic Weight ◽  
Spike Timing ◽  
Benchmark Dataset ◽  
Spiking Neural Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Winner Take All ◽  
Take All

In this paper, we propose algorithm based reinforcement learning for spiking neural networks. The algorithm simulates biological adaptability and uses the soft-reward from environment to modulate the synaptic weight, which combines spike-timing-dependent plasticity (STDP), winner-take-all mechanism. The algorithm is tested to classify a number of standard benchmark dataset. The obtained results show the effectiveness of the proposed algorithm.

scholarly journals Mechanisms for Stable Bump Activity, Winner-Take-All and Group Selection in Neuronal Spiking Networks

2016 ◽  
Author(s):  
Yanqing Chen
Keyword(s):  
Group Selection ◽  
Major Function ◽  
Recurrent Excitation ◽  
Spiking Network ◽  
Sensory Maps ◽  
Central Nervous Systems ◽  
Spiking Networks ◽  
The Stability ◽  
Winner Take All ◽  
Take All

AbstractA major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not totally understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a soft winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network.

Interest Groups and Elections

2019 ◽  
pp. 339-357
Author(s):  
Jeffrey M. Berry
Keyword(s):  
Political Parties ◽  
Interest Groups ◽  
Campaign Finance ◽  
Organizational Forms ◽  
Super Pacs ◽  
New Organizational Forms ◽  
Winner Take All ◽  
The Impact ◽  
Take All

The relationships between interest groups, political parties, and elections have always been dynamic, but in recent years change has accelerated in ways that have favored some interests over others. This chapter considers these developments as the result of a variety of factors, the most critical of which are the growth of polarization, a new legal landscape for campaign finance, and new organizational forms. The chapter goes on to suggest, that as bipartisanship has ebbed, elections have become winner-take-all affairs and interest groups are pushed to choose sides. The chapter further suggests that the rise of super PACs is especially notable as wealthy individuals have become increasingly important, single sources of campaign money, supplanting in part traditional interest groups, especially conventional PACs. It concludes that even as sums spent by super PACs and other interest groups have skyrocketed, the impact of their direct spending on persuading voters remains uncertain.

scholarly journals Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley
Keyword(s):  
Action Potentials ◽  
Radiation Effects ◽  
Synaptic Weight ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Synaptic Action ◽  
Drift Model ◽  
Spatio Temporal ◽  
Post Synaptic Neuron

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

2001 ◽  
Vol 13 (10) ◽  
pp. 2221-2237 ◽  
Author(s):  
Rajesh P. N. Rao ◽  
Terrence J. Sejnowski
Keyword(s):  
Cortical Neuron ◽  
Action Potentials ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Excitatory Synapses ◽  
Temporal Difference ◽  
Temporal Difference Learning ◽  
Neocortical Neurons ◽  
Hebbian Plasticity

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

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