STDP Pattern Onset Learning Depends on Background Activity

Humble, James; Furber, Steve; Denham, Susan; Wennekers, Thomas

doi:10.1007/978-1-4614-0164-3_3

Cited by 4 publications

(6 citation statements)

References 25 publications

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“…Results shown confirm a previous finding that the level of background activity present during and after learning needs to be similar for proper pattern recognition (Humble et al, 2011 ). Specifically, if the level of background activity increases after learning, a neuron may respond earlier, and if the activity is decreased, a neuron may no longer respond at all.…”

Section: Discussionsupporting

confidence: 89%

“…This happens during the first few seconds of the simulation (cf. Masquelier et al, 2008 ; Humble et al, 2011 ). The repeating pattern counteracts this weakening, leading to the development of specific ff synapses for segments of the input in some output neurons.…”

Section: Resultsmentioning

confidence: 99%

“…Whether, well-defined asymptotic weight distributions exist is likely, but given the analytically tough winner-takes-all dynamics those are difficult to predict. Beyond this general remark, Figure 4 provides some insight into the learning dynamics of the recurrent connections [aspects of training the ff synapses are studied in Masquelier et al ( 2008 ), Humble et al ( 2011 ) and not duplicated here].…”

Section: Resultsmentioning

confidence: 99%

“…When successful, several neurons respond one after the other, each recognizing a segment of the input pattern. The learning of short repeated spatio-temporal patterns has been studied and analysed previously (Masquelier et al, 2008 , 2009 ; Humble et al, 2011 ). It has been shown that a neuron with STDP is able to learn and respond to the beginning of a repeated spatio-temporal pattern even when it is embedded in a statistically identical carrier signal.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity

Humble¹,

Denham²,

Wennekers³

2012

Front. Comput. Neurosci.

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It has previously been shown that by using spike-timing-dependent plasticity (STDP), neurons can adapt to the beginning of a repeating spatio-temporal firing pattern in their input. In the present work, we demonstrate that this mechanism can be extended to train recognizers for longer spatio-temporal input signals. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feed-forwardly connected in such a way that both the correct input segment and the firing of the previous neurons are required in order to activate the next neuron in the chain. This is akin to a simple class of finite state automata. We show that nearest-neighbor STDP (where only the pre-synaptic spike most recent to a post-synaptic one is considered) leads to “nearest-neighbor” chains where connections only form between subsequent states in a chain (similar to classic “synfire chains”). In contrast, “all-to-all spike-timing-dependent plasticity” (where all pre- and post-synaptic spike pairs matter) leads to multiple connections that can span several temporal stages in the chain; these connections respect the temporal order of the neurons. It is also demonstrated that previously learnt individual chains can be “stitched together” by repeatedly presenting them in a fixed order. This way longer sequence recognizers can be formed, and potentially also nested structures. Robustness of recognition with respect to speed variations in the input patterns is shown to depend on rise-times of post-synaptic potentials and the membrane noise. It is argued that the memory capacity of the model is high, but could theoretically be increased using sparse codes.

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Section: Discussionsupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity

Humble¹,

Denham²,

Wennekers³

2012

Front. Comput. Neurosci.

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“…Cortical spontaneous activity may be a signature of neural circuitry (Tsodyks and Sejnowski, 1995; van Vreeswijk and Sompolinsky, 1996; Brunel, 2000), is modulated by attentional state (Steriade et al, 2001; Rudolph et al, 2005; Poulet and Petersen, 2008; Goard and Dan, 2009; Constantinople and Bruno, 2011; Nichols et al, 2011), and influences stimulus encoding (Kisley and Gerstein, 1999; Anderson et al, 2000; Petersen et al, 2003; Sachdev et al, 2004; Hasenstaub et al, 2007; Ringach, 2009; Destexhe, 2011; Mulders and Robertson, 2011) and synaptic plasticity (Mazzoni et al, 1991; Rokni et al, 2007; Legenstein et al, 2010; Humble et al, 2011; Hinton et al, 2012). A key parameter in models of cortical circuitry underlying such activity is the balance of synaptic excitation and inhibition (Kumar et al, 2008; Murphy and Miller, 2009; Sanchez-Vives et al, 2010; Vogels et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A spontaneous state of weakly correlated synaptic excitation and inhibition in visual cortex

Tan

Andoni

Priebe³

2013

Neuroscience

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Cortical spontaneous activity reflects an animal’s behavioral state and affects neural responses to sensory stimuli. The correlation between excitatory and inhibitory synaptic input to single neurons is a key parameter in models of cortical circuitry. Recent measurements demonstrated highly correlated synaptic excitation and inhibition during spontaneous “up-and-down” states, during which excitation accounted for approximately 80% of inhibitory variance (Shu et al., 2003; Haider et al., 2006). Here we report in vivo whole-cell estimates of the correlation between excitation and inhibition in the rat visual cortex under pentobarbital anesthesia, during which up-and-down states are absent. Excitation and inhibition are weakly correlated, relative to the up-and-down state: excitation accounts for less than 40% of inhibitory variance. Although these correlations are lower than when the circuit cycles between up-and-down states, both behaviors may arise from the same circuitry. Our observations provide evidence that different correlational patterns of excitation and inhibition underlie different cortical states.

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Application of constrained learning in making deep networks more transparent, regularized, and biologically plausible

Ghiassirad

Shoorehdeli

Farivar

2019

Engineering Applications of Artificial Intelligence

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STDP Pattern Onset Learning Depends on Background Activity

Cited by 4 publications

References 25 publications

Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity

Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity

A spontaneous state of weakly correlated synaptic excitation and inhibition in visual cortex

Application of constrained learning in making deep networks more transparent, regularized, and biologically plausible

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