Introducing double bouquet cells into a modular cortical associative memory model

Chrysanthidis, Nikolaos; Fiebig, Florian; Lansner, Anders

doi:10.1101/462010

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…The network model used here features two reciprocally connected networks, the so-called Item and Context networks. The architecture of each network follows our previous spiking implementations of attractor memory networks (Lansner, 2009; Tully et al, 2014, 2016; Lundqvist et al, 2011; Fiebig and Lansner, 2017; Chrysanthidis et al, 2019; Fiebig et al, 2020), and is best understood as a subsampled cortical layer 2/3 patch with nested hypercolumns (HCs) and minicolumns (MCs; Fig. 1A, see STAR⋆METHODS for details).…”

Section: Resultsmentioning

confidence: 99%

“…For simplicity, we assumed that item and context information engage different modalities and cortical areas and thus the corresponding networks are located at a substantial distance (Table 2). The architecture of each network follows our previous spiking implementations of attractor memory networks (Lansner, 2009;Tully et al, 2014Tully et al, , 2016Lundqvist et al, 2011;Fiebig and Lansner, 2017;Chrysanthidis et al, 2019;Fiebig et al, 2020), and is best understood as a subsampled cortical layer 2/3 patch with nested hypercolumns (HCs) and minicolumns (MCs; Fig. 1a).…”

Section: Two-network Architecture and Connectivitymentioning

confidence: 99%

“…1a only for MC1 in HC1 of the Context network), binding items and contextual information (Ranganath, 2010). Recurrent connectivity establishes around 100 active plastic synapses onto each pyramidal cell from A recent study based on the same basic model architecture demonstrated that learned mono-synaptic inhibition between competing attractors is functionally equivalent to the disynaptic inhibition mediated by double bouquet 338 and basket cells (Chrysanthidis et al, 2019). Parameters 339 characterising other neural and synaptic properties in-340 cluding BCPNN can be found in Table 1.…”

Section: Two-network Architecture and Connectivitymentioning

confidence: 99%

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Traces of semantization - from episodic to semantic memory in a spiking cortical network model

Chrysanthidis

Fiebig

Lansner

et al. 2021

Preprint

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Episodic memory is the recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or "semantization", which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We also examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.

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Section: Resultsmentioning

confidence: 99%

Section: Two-network Architecture and Connectivitymentioning

confidence: 99%

Section: Two-network Architecture and Connectivitymentioning

confidence: 99%

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Traces of semantization - from episodic to semantic memory in a spiking cortical network model

Chrysanthidis

Fiebig

Lansner

et al. 2021

Preprint

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“…There are more modern versions of associative memories. For example Chrysanthidis et al (2019), like the simulations described below, use NEST, spiking neurons, and Hebbian learning to associate memories; this impressive system is aligned to biological spiking neurons and topology, but performs a rather weak cognitive associative task.…”

Section: Associative Memorymentioning

confidence: 99%

Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons

Huyck¹,

Vergani²

2020

J Comput Neurosci

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“…In the former, they can be straightforwardly interpreted as the conductance between two units, whereas in the latter case we interpret them as a disynaptic connection through an inhibitory interneuron. The argument for the biological plausibility of this arrangement using double bouquet cells as the inhibitory interneurons in this architecture is developed furhter by Chrysanthidis et al (2018).…”

Section: Previous Work and Biological Contextmentioning

confidence: 99%

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences

Martínez

Lansner

Herman

2019

Preprint

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Many brain phenomena both at the cognitive and behavior level exhibit remarkable sequential characteristics. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.

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Introducing double bouquet cells into a modular cortical associative memory model

Cited by 5 publications

References 20 publications

Traces of semantization - from episodic to semantic memory in a spiking cortical network model

Traces of semantization - from episodic to semantic memory in a spiking cortical network model

Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences

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