Hippocampal Memory Modification Induced by Pattern Completion and Spike-Timing Dependent Synaptic Plasticity

Samura, Toshikazu; Hattori, Motonobu

doi:10.1142/s0129065705000025

Cited by 13 publications

(15 citation statements)

References 7 publications

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“…Furthermore, it has been suggested that the inherently asymmetric nature of STDP directly contradicts rate-coded associative learning, which explicitly depend on the development of strong bi-directional connections [13], [82], [ but see 41], [42], [44], [45], [77]. Here, we examine whether auto-associative learning can be achieved in a network model that incorporates the main features of neural and synaptic dynamics observed in CA3 – namely, phase precession and STDP.…”

Section: Resultsmentioning

confidence: 99%

“…Phase precession in ensembles of place cells encoding for overlapping place fields represents a prominent empirical model of cell assembly dynamics in the brain [50], [57], [59]. However, previous models of hippocampal mnemonic function have generally focussed on the learning and recall of either discrete rate-coded or sequential temporally-coded activity patterns, while few studies have attempted to integrate these computational models within a single framework [27], [39], [42]–[44]. Here, we demonstrate that both auto- and hetero- associative learning can proceed simultaneously in our network model, such that repeatedly synchronous firing with weak sequence bias produces bi-directional connections while repeatedly asynchronous firing produces asymmetric connections.…”

Section: Resultsmentioning

confidence: 99%

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Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus

et al. 2010

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The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus

et al. 2010

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“…Subsequently, interconnected place cells encoding for neighboring place fields exhibit temporal correlations in firing activity with repeatedly alternating direction but approximately equal ISIs. It has been suggested that the asymmetric nature of the STDP learning window dictates that purely asymmetric connections will develop under these conditions (Skaggs et al, 1996; Wagatsuma and Yamaguchi, 2007; Song and Abbott, 2001; but see Mongillo et al, 2005; Samura and Hattori, 2005). For the purposes of cognitive map theory, however, it is essential that strong bi-directional connections which reflect the relative location of corresponding place fields in the environment and the long-term history of navigation through this one-dimensional environment are generated (Muller et al, 1991; Burgess et al, 1994; Burgess, 2007).…”

Section: Resultsmentioning

confidence: 99%

“…Although STDP, combined with the temporally coded patterns of neural activity observed in hippocampus, is ideally suited for route or sequence learning, it has been suggested that the inherent asymmetry of this plasticity rule may not allow the development of strong bi-directional connections between place cells that are required by cognitive map theory (Skaggs et al, 1996; Song and Abbott, 2001; Wagatsuma and Yamaguchi, 2007; but see Mongillo et al, 2005; Samura and Hattori, 2005). Computational modeling has also demonstrated that STDP can provide a putative homeostatic function by reducing synaptic currents in response to an increase in input firing rate, and thus provide global stability for local Hebbian learning rules (Song et al, 2000; Song and Abbott, 2001).…”

Section: Introductionmentioning

confidence: 99%

Spike-timing dependent plasticity and the cognitive map

Bush¹

2010

Front. Comput. Neurosci.

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Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighboring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post-synaptic firing according to a spike-timing dependent plasticity (STDP) rule. Furthermore, electrophysiology studies have identified persistent “theta-coded” temporal correlations in place cell activity in vivo, characterized by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post-synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilizes this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

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“…13,14 These mechanisms have been complemented with strong lateral inhibition, allowing multiple nonoverlapping patterns to be detected in multi-neuronal networks in a winnertakes-all scheme. 15 Inhibitory synapses have also been experimentally shown to express a particular type of symmetric STDP in the hippocampus 11,16 and the auditory cortex. 17 In these cases, LTP is induced when presynaptic and postsynaptic spikes occur in close time vicinity, irrespective of the reciprocal order, whilst LTD is induced when the presynaptic and postsynaptic spikes occur further away in time, with distances ranging from between 40 ms and 100 ms.…”

Section: Introductionmentioning

confidence: 99%

Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

Garrido

Luque

Tolu

et al. 2016

Int. J. Neur. Syst.

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The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse representations of information in excitatory neuron populations falling under their control.

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Hippocampal Memory Modification Induced by Pattern Completion and Spike-Timing Dependent Synaptic Plasticity

Cited by 13 publications

References 7 publications

Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus

Dual Coding with STDP in a Spiking Recurrent Neural Network Model of the Hippocampus

Spike-timing dependent plasticity and the cognitive map

Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

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