2013
DOI: 10.3389/fncom.2013.00010
|View full text |Cite
|
Sign up to set email alerts
|

Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity

Abstract: This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1). This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral genicula… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

1
27
0

Year Published

2013
2013
2020
2020

Publication Types

Select...
6
2
1

Relationship

3
6

Authors

Journals

citations
Cited by 19 publications
(28 citation statements)
references
References 118 publications
(172 reference statements)
1
27
0
Order By: Relevance
“…With an emphasis on high-performance, HRLSim was developed to support the modeling efforts of the SyNAPSE project and its team members. It has also proven extremely useful as a general neural simulation environment for other studies (Srinivasa and Cho, 2012; O'Brien and Srinivasa, 2013; Srinivasa and Jiang, 2013). …”
Section: Methodsmentioning
confidence: 99%
“…With an emphasis on high-performance, HRLSim was developed to support the modeling efforts of the SyNAPSE project and its team members. It has also proven extremely useful as a general neural simulation environment for other studies (Srinivasa and Cho, 2012; O'Brien and Srinivasa, 2013; Srinivasa and Jiang, 2013). …”
Section: Methodsmentioning
confidence: 99%
“…Through balancing long-term potentiation and depression together with imposing a time asymmetry, STDP has been shown to regulate weights within a quiescent operating range, but still drive symmetry breaking of weights, leading to input feature selectivity in a variety of reciprocally connected networks without any additional requirement for synaptic normalization (Song et al, 2000; Song and Abbott, 2001). More recently STDP based models have been shown to be capable of modulating input selectivity in coupled populations of excitatory and inhibitory neurons in the olfactory (Finelli et al, 2008; Linster and Cleland, 2010) and mammalian visual pathways (Young et al, 2007; Srinivasa and Jiang, 2013). …”
Section: Discussionmentioning
confidence: 99%
“…The inhibitory synapse rule is symmetrical, and functionally implements a rule where co-active neurons reduce their inhibitory coupling, but neurons that fire independently have strong inhibitory connectivity. Inhibitory plasticity uses an inverted top-hat shaped symmetric STDP curve, which is similar to a Mexican-hat plasticity curve (Caporale and Dan, 2008; Srinivasa and Jiang, 2013). Upon presynaptic or postsynaptic action potential: wi={leftδiLTDif|tposttpre|<τiLTDleftδiLTPelse if|tposttpre|τiLTPleft0otherwise where w is constrained to 0 ≤ w i ≤ w max i , τ LTD i is the time window of long-term depression (LTD) for inhibitory STDP.…”
Section: Methodsmentioning
confidence: 99%