Reliable Sequential Activation of Neural Assemblies by Single Pyramidal Cells in a Three-Layered Cortex

Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.

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“…Further perturbation experiments can test this idea and shed light on the mechanisms of sequential learning [54].…”

Section: Discussionmentioning

confidence: 99%

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

2020

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“…5F). Such dominant synapses have been observed in vivo [Song et al, 2005, Lefort et al, 2009, Hemberger et al, 2019] and are thought to play an important part in network function [Lefort et al, 2009]. Taken together, these dominant synapses establish preferential paths along which activity can propagate reliably for multiple timesteps.…”

Section: Resultsmentioning

confidence: 98%

“…How does this increase in antisymmetry help the network to memorize sequences? Along the “strong” directions, activity can propagate with high reliability regardless of the background activity in the network - activity thus propagates along preferential paths [Fiete et al, 2010, Klampfl and Maass, 2013, Hemberger et al, 2019]. The propagation probability along a path increases with the synaptic strength in relation to the thresholds.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, lag-one activation was defined as the mean of sequential activations, corrected for the individual firing rates: , with T = 10 4 . Applying the ordering obtained from W EE to Q EE reveals endogenous sequences [Carrillo-Reid et al, 2015, Dechery and MacLean, 2017, Hemberger et al, 2019] on its lower secondary diagonal (Fig. 6E): multiple neural activations following each other with higher-than chance probability.…”

Section: Resultsmentioning

confidence: 99%

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Sequence memory in recurrent neuronal network can develop without structured input

Loidolt

Rudelt

Priesemann

2020

Preprint

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How does spontaneous activity during development prepare cortico-cortical connections for sensory input? We here analyse the development of sequence memory, an intrinsic feature of recurrent networks that supports temporal perception. We use a recurrent neural network model with homeostatic and spike-timing-dependent plasticity (STDP). This model has been shown to learn specific sequences from structured input. We show that development even under unstructured input increases unspecific sequence memory. Moreover, networks “pre-shaped” by such unstructured input subsequently learn specific sequences faster. The key structural substrate is the emergence of strong and directed synapses due to STDP and synaptic competition. These construct self-amplifying preferential paths of activity, which can quickly encode new input sequences. Our results suggest that memory traces are not printed on a tabula rasa, but instead harness building blocks already present in the brain.

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“…To enhance the success rate of recording synaptic connections in local neuronal microcircuits, the number of simultaneously recorded neurons (n) has been increased from dual (2), triple (3), quadruple (4), octuple (8) up to 12 (Thomson et al, 2002;Song et al, 2005;Kampa et al, 2006;Brown and Hestrin, 2009;Lefort et al, 2009;Yu et al, 2009;Ko et al, 2011;Perin et al, 2011;Rieubland et al, 2014;Jiang et al, 2015;Guzman et al, 2016;Peng et al, 2017;Hemberger et al, 2019). Multiple (n > 2) recordings may yield more synaptic connections because the number of potential synaptic connections (m) established between n neurons increases steeply with increasing n: m = n × (n−1).…”

Section: Discussionmentioning

confidence: 99%

Unveiling the Synaptic Function and Structure Using Paired Recordings From Synaptically Coupled Neurons

Yang

Ding

et al. 2020

Front. Synaptic Neurosci.

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Synaptic transmission between neurons is the basic mechanism for information processing in cortical microcircuits. To date, paired recording from synaptically coupled neurons is the most widely used method which allows a detailed functional characterization of unitary synaptic transmission at the cellular and synaptic level in combination with a structural characterization of both pre-and postsynaptic neurons at the light and electron microscopic level. In this review, we will summarize the many applications of paired recordings to investigate synaptic function and structure. Paired recordings have been used to study the detailed electrophysiological and anatomical properties of synaptically coupled cell pairs within a synaptic microcircuit; this is critical in order to understand the connectivity rules and dynamic properties of synaptic transmission. Paired recordings can also be adopted for quantal analysis of an identified synaptic connection and to study the regulation of synaptic transmission by neuromodulators such as acetylcholine, the monoamines, neuropeptides, and adenosine etc. Taken together, paired recordings from synaptically coupled neurons will remain a very useful approach for a detailed characterization of synaptic transmission not only in the rodent brain but also that of other species including humans.

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Reliable Sequential Activation of Neural Assemblies by Single Pyramidal Cells in a Three-Layered Cortex

Cited by 45 publications

References 87 publications

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Sequence memory in recurrent neuronal network can develop without structured input

Unveiling the Synaptic Function and Structure Using Paired Recordings From Synaptically Coupled Neurons

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