The role of postsynaptic potential decay rate in neural synchrony

Choe, Yoonsuck; Miikkulainen, Risto

doi:10.1016/s0925-2312(02)00747-6

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…In agreement with earlier work, the ability of the input layer to form the perceptual cycles of the individual stimuli (when presented simultaneously) was found to be be critically dependent upon a mechanism of delayed self-inhibition [22], [27] – in this case, cell firing-rate adaptation. The adaptation model used here is a more realistic implementation than in previous work [22], [55], yet instantiates the same core principle, thus indicating a convergence of views.…”

Section: Discussionsupporting

confidence: 90%

“…These regimes are as follows: (1) mutual excitatory connections without delays cause synchrony (quickly); (2) mutual excitatory connections with delays cause desynchrony; (3) mutual inhibitory connections without delays cause desynchrony; and (4) mutual inhibitory connections with delays cause synchrony (slowly). Similarly, synapses with fast PSP decay and synapses with slow PSP decay lead to synchrony, whilst the opposite combinations lead to desynchrony [27].…”

Section: Introductionmentioning

confidence: 99%

“…These form a mutually supportive basis for synchronising the spike volleys of spatially proximal features of a particular object, while inhibitory interneurons tend to desynchronise representations of different objects. The second requirement is either conduction delays [26], varying postsynaptic potential decay rate [27] or cell firing-rate adaptation [22], [23], [28], [29]. Together, these features act to generate periodic firing in each population of principal cells.…”

Section: Introductionmentioning

confidence: 99%

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How Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together

Evans

Stringer

2013

PLoS ONE

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Over successive stages, the ventral visual system of the primate brain develops neurons that respond selectively to particular objects or faces with translation, size and view invariance. The powerful neural representations found in Inferotemporal cortex form a remarkably rapid and robust basis for object recognition which belies the difficulties faced by the system when learning in natural visual environments. A central issue in understanding the process of biological object recognition is how these neurons learn to form separate representations of objects from complex visual scenes composed of multiple objects. We show how a one-layer competitive network comprised of ‘spiking’ neurons is able to learn separate transformation-invariant representations (exemplified by one-dimensional translations) of visual objects that are always seen together moving in lock-step, but separated in space. This is achieved by combining ‘Mexican hat’ functional lateral connectivity with cell firing-rate adaptation to temporally segment input representations of competing stimuli through anti-phase oscillations (perceptual cycles). These spiking dynamics are quickly and reliably generated, enabling selective modification of the feed-forward connections to neurons in the next layer through Spike-Time-Dependent Plasticity (STDP), resulting in separate translation-invariant representations of each stimulus. Variations in key properties of the model are investigated with respect to the network’s ability to develop appropriate input representations and subsequently output representations through STDP. Contrary to earlier rate-coded models of this learning process, this work shows how spiking neural networks may learn about more than one stimulus together without suffering from the ‘superposition catastrophe’. We take these results to suggest that spiking dynamics are key to understanding biological visual object recognition.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together

Evans

Stringer

2013

PLoS ONE

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“…Rinzel et al (1998) have studied the influence of PSP time courses on the activity patterns of neuronal networks. Choe and Miikkulainen (2003) have modeled the synchronizing effects of the PSP time courses. Also, experimental data have disclosed the synchronizing properties of the synaptic time course on networks of bursting neurons (Elson et al 2002).…”

Section: Introductionmentioning

confidence: 99%

A population level computational model of the basal ganglia that generates parkinsonian local field potential activity

et al. 2009

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Recordings from the basal ganglia's subthalamic nucleus are acquired via microelectrodes immediately prior to the application of Deep Brain Stimulation (DBS) treatment for Parkinson's Disease (PD) to assist in the selection of the final point for the implantation of the DBS electrode. The acquired recordings reveal a persistent characteristic beta band peak in the power spectral density function of the Local Field Potential (LFP) signals. This peak is considered to lie at the core of the causality-effect relationships of the parkinsonian pathophysiology. Based on LFPs acquired from human subjects during DBS for PD, we constructed a computational model of the basal ganglia on the population level that generates LFPs to identify the critical pathophysiological alterations that lead to the expression of the beta band peak. To this end, we used experimental data reporting that the strengths of the synaptic connections are modified under dopamine depletion. The hypothesis that the altered dopaminergic modulation may affect both the amplitude and the time course of the postsynaptic potentials is validated by the model. The results suggest a pivotal role of both of these parameters to the pathophysiology of PD.

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“…These form a mutually supportive basis for synchronising the spike volleys of spatially proximal features of a particular object, while inhibitory interneurons tend to desynchronise representations of different objects. The second requirement is either conduction delays [26], varying postsynaptic potential decay rate [27] or cell firing-rate adaptation [22,23,28,29]. Together, these features act to generate periodic firing in each population of principal cells.…”

Section: Conditions For Synchronous Cell Assembliesmentioning

confidence: 99%

Correction: How Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together

Evans¹,

Stringer²

2013

PLoS ONE

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Over successive stages, the ventral visual system of the primate brain develops neurons that respond selectively to particular objects or faces with translation, size and view invariance. The powerful neural representations found in Inferotemporal cortex form a remarkably rapid and robust basis for object recognition which belies the difficulties faced by the system when learning in natural visual environments. A central issue in understanding the process of biological object recognition is how these neurons learn to form separate representations of objects from complex visual scenes composed of multiple objects. We show how a one-layer competitive network comprised of 'spiking' neurons is able to learn separate transformation-invariant representations (exemplified by one-dimensional translations) of visual objects that are always seen together moving in lock-step, but separated in space. This is achieved by combining 'Mexican hat' functional lateral connectivity with cell firing-rate adaptation to temporally segment input representations of competing stimuli through antiphase oscillations (perceptual cycles). These spiking dynamics are quickly and reliably generated, enabling selective modification of the feed-forward connections to neurons in the next layer through Spike-Time-Dependent Plasticity (STDP), resulting in separate translation-invariant representations of each stimulus. Variations in key properties of the model are investigated with respect to the network's ability to develop appropriate input representations and subsequently output representations through STDP. Contrary to earlier rate-coded models of this learning process, this work shows how spiking neural networks may learn about more than one stimulus together without suffering from the 'superposition catastrophe'. We take these results to suggest that spiking dynamics are key to understanding biological visual object recognition.

show abstract

The role of postsynaptic potential decay rate in neural synchrony

Cited by 5 publications

References 17 publications

How Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together

How Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together

A population level computational model of the basal ganglia that generates parkinsonian local field potential activity

Correction: How Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together

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