Gamma Oscillations Underlie the Maintenance of Feature-Specific Information and the Contents of Visual Working Memory

Honkanen, Roosa; Rouhinen, Santeri; Wang, Sheng H.; Palva, Satu

doi:10.1093/cercor/bhu263

Cited by 139 publications

(144 citation statements)

References 72 publications

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“…The amnesic effects of targeted replay interruption via electrical stimulation (Girardeau et al, 2009;Ego-Stengel and Wilson, 2010) suggest that this link is causal, not merely correlational, and has previously been modeled as such by the authors (Fiebig and Lansner, 2014).…”

Section: Serial Position Effectmentioning

confidence: 86%

“…We know that attractors are typically active Ͻ200 ms, so Figure 5 (inset) implies that a peristimulus EPSP of 1.5 mV magnitude is apparently sufficient for attractor activity in our model. Thomson et al (2002) measured EPSPs of local layer 2/3 pyramidal-pyramidal cell connections in rat cortex at 1.7 Ϯ 1.3 mV, whereas long range connections have been estimated to be one order of magnitude weaker (Gilbert et al, 1990). Because the temporal coordination of EPSPs is crucial in attractor operation, it is worthwhile pointing out that intraminicolumnar connection delays in our model (1.5 Ϯ 0.23 ms, see Spatial organization) are very similar to those reported by Thomson et al (2002) between layer 2/3 pyramidal neurons (1.5 Ϯ 0.3 ms).…”

Section: Demo 2: Multi-item Wm: List Learning Without Intermittent Rementioning

confidence: 99%

“…Candidate mechanisms include increased presynaptic transmitter release, AMPAR phosphorylation by CaM-CaMKII, receptor insertions from intracellular cytosol, or translocation from perisynaptic locations. Computational modeling suggests that considerable effective synaptic conductance change can happen already some seconds after the induction signal (He et al, 2015). Stimuli too weak to induce LTP can still induce NMDAR-and GluR1-dependent Hebbian STP (Erickson et al, 2010).…”

Section: Experimental Support For Fast Hebbian Synaptic Plasticitymentioning

confidence: 99%

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A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

Fiebig

Lansner

2017

J. Neurosci.

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A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism.Key words: Hebbian plasticity; primacy; recency; short-term potentiation; word list learning; working memory IntroductionWorking memory (WM) is a key component of cognition. It maintains information over seconds and minutes in a form that allows animals to act beyond the here and now. WM is updated by selectively attended external information and activated longterm memory representations. Mammalian prefrontal cortex (PFC) is generally believed to play a key role in WM (Fuster, 2009; D'Esposito and Postle, 2015).The most common theory about the neural mechanisms of WM is that of persistent elevated activity in a recurrently connected neural network, presumably located in the PFC (Funahashi et al., 1989; Goldman-Rakic, 1995; Tsakanikas and Relkin, 2007). This theory was implemented in early spiking neural network models of persistent activity WM (Camperi and Wang, 1998; Compte et al., 2000). However, recent reexamina- Significance StatementWorking memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying cortical tissue. These findings are directly relevant to the ongoing paradigm shift in the WM field.The Journal of N...

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Section: Serial Position Effectmentioning

confidence: 86%

Section: Demo 2: Multi-item Wm: List Learning Without Intermittent Rementioning

confidence: 99%

Section: Experimental Support For Fast Hebbian Synaptic Plasticitymentioning

confidence: 99%

See 1 more Smart Citation

A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

Fiebig

Lansner

2017

J. Neurosci.

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“…Vogel and Machizawa, 2004;Sauseng et al, 2009), MEG (e.g. Palva et al, 2010;Mitchell and Cusack, 2011;Honkanen et al, 2015;Siebenhuhner et al, 2016), and fMRI (e.g. Todd and Marois, 2005;McNab and Klingberg, 2008;Linke et al, 2011;Stevens et al, 2012;Ester et al, 2013;Vicente-Grabovetsky et al, 2014;Galeano Weber et al, 2016;Veldsman et al, 2017), with some differences reported across age groups (for a review see Sander et al, 2012).…”

mentioning

confidence: 99%

Visual short-term memory through the lifespan: preserved benefits of context and metacognition

Mitchell¹,

Cam‐CAN²,

Cusack³

2017

Preprint

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Visual short-term memory ability falls throughout the lifespan in healthy adults. Using a continuous report task, in a large, population-based sample, we first confirmed that this decline affects the quality and quantity of reported memories as well as knowledge of which item went where. Visual and sensorimotor precision also worsened with advancing age, but this did not account for the reduced memory performance.We then considered two strategies that older individuals might be able to adopt, to offset these memory declines: the use of contextual encoding, and metacognitive monitoring of performance.Context and metacognitive awareness were both associated with significantly better performance, however these effects did not interact with age in our sample. This suggests that older adults retain their capacity to boost memory performance through attention to external context and monitoring of their performance. Strategies that focus on taking advantage of these preserved abilities may therefore help to maintain short-term memory performance with advancing age. The paper reports on analysis of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data.3

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“…All elements in the derivation, then, are created as simple γ assemblies, and only some (namely, labeled phase heads) become more complex β assemblies; consider the difference between adverbs like nearly and verbs like ran. The rhythmic division of complexity which follows from this is supported by Honkanen et al (2014), who demonstrated that simple objects represented in visual working memory employ the γ band, while more complex objects are represented by the β band. This oscillatory procedure also matches the generative view that phase heads have a longer derivational life than non-phase heads (Boeckx, 2014a;Narita, 2014a).…”

Section: Formal Considerationsmentioning

confidence: 85%