Sequence learning, prediction, and replay in networks of spiking neurons

Bouhadjar, Younes; Wouters, Dirk J.; Diesmann, Markus; Tetzlaff, Tom

doi:10.1371/journal.pcbi.1010233

Cited by 11 publications

(45 citation statements)

References 76 publications

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“…We then study the network responses to ambiguous cues and the influence of the occurrence frequencies on the recall behavior in the absence or presence of noise. Similar to (Bouhadjar et al, 2021), the model consists of a randomly and sparsely connected network of N E excitatory neurons (population E) and a single inhibitory neuron (Fig. 1A).…”

Section: Resultsmentioning

confidence: 99%

“…After successful learning, the presentation of some sequence element leads to a context dependent prediction of the subsequent stimulus. In case the prediction is wrong the network generates a mismatch signal (see Bouhadjar et al, 2021). The network can also be configured into a replay mode where it autonomously replays learned sequences in response to a cue signal.…”

Section: Learning Protocol and Taskmentioning

confidence: 99%

“…In many circumstances, we have to make a decision of what elements to recall next in response to a cue. A number of previous studies have introduced spiking neuronal network implementations of sequence learning and replay (Klampfl & Maass, 2013;Klos et al, 2018;Maes et al, 2020;Cone & Shouval, 2021;Bouhadjar et al, 2021). The spiking temporal-memory (TM) model proposed in (Bouhadjar et al, 2021) provides an energy efficient sequence processing mechanism with high storage capacity by virtue of its sparse activity.…”

Section: Introductionmentioning

confidence: 99%

“…A number of previous studies have introduced spiking neuronal network implementations of sequence learning and replay (Klampfl & Maass, 2013;Klos et al, 2018;Maes et al, 2020;Cone & Shouval, 2021;Bouhadjar et al, 2021). The spiking temporal-memory (TM) model proposed in (Bouhadjar et al, 2021) provides an energy efficient sequence processing mechanism with high storage capacity by virtue of its sparse activity. It learns high-order sequences in an unsupervised, continuous manner using biological, local learning rules.…”

Section: Introductionmentioning

confidence: 99%

“…This study addresses the problem of sequential decision making in the face of ambiguity and the role of coherent noise in shaping decision strategies. We investigate how the spiking TM model of Bouhadjar et al (2021) recalls sequences in response to ambiguous cues in the presence of coherent noise, to what extent noise averaging can be overcome by increasing the noise amplitude, and how different recall strategies can be achieved by adjusting the noise characteristics. We further explore whether shared synaptic input and random stimulus locking to spatiotemporal oscillations can serve as appropriate, natural sources of coherent noise.…”

Section: Introductionmentioning

confidence: 99%

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Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Bouhadjar¹,

Wouters²,

Diesmann³

et al. 2022

Preprint

Self Cite

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Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type of decision making central to cognition is sequential memory recall in response to ambiguous cues. A previously developed spiking neuronal network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. In response to an ambiguous cue, the model deterministically recalls the sequence shown most frequently during training. Here, we present an extension of the model enabling a range of different decision strategies. In this model, explorative behavior is generated by supplying neurons with noise. As the model relies on population encoding, uncorrelated noise averages out, and the recall dynamics remain effectively deterministic. In the presence of locally correlated noise, the averaging effect is avoided without impairing the model performance, and without the need for large noise amplitudes. We investigate two forms of correlated noise occurring in nature: shared synaptic background inputs, and random locking of the stimulus to spatiotemporal oscillations in the network activity. Depending on the noise characteristics, the network adopts various replay strategies. This study thereby provides potential mechanisms explaining how the statistics of learned sequences affect decision making, and how decision strategies can be adjusted after learning. Significance statementHumans and other animals often benefit from exploring multiple alternative solutions to a given problem. Such explorative behavior is frequently attributed to noise in the neuronal dynamics. Supplying each neuron or synapse in a neuronal circuit with noise, however, does not necessarily lead to explorative dynamics. If decisions are triggered by the compound activity of ensembles of neurons or synapses, noise averages out, unless it is correlated within these ensembles. This modeling study equips a neuronal sequence-processing circuit with explorative behavior by introducing configurable coherent noise. It contributes to an understanding of the neuronal mechanisms underlying different decision strategies in the face of ambiguity, and highlights the role of coherent network activity during sequential memory recall.

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Section: Resultsmentioning

confidence: 99%

Section: Learning Protocol and Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Bouhadjar¹,

Wouters²,

Diesmann³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Phenomenological Modeling of Diverse and Heterogeneous Synaptic Dynamics at Natural Density

Korcsak-Gorzo,

Linssen,

Albers

et al. 2024

Neuromethods

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System model of neuromorphic sequence learning on a memristive crossbar array

Siegel

Bouhadjar

Tetzlaff

et al. 2023

Neuromorph. Comput. Eng.

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Machine learning models for sequence learning and processing often suffer from high energy consumption and require large amounts of training data. The brain presents more efficient solutions to how these types of tasks can be solved. While this has inspired the conception of novel brain-inspired algorithms, their realizations remain constrained to conventional von-Neumann machines. Therefore, the potential power efficiency of the algorithm cannot be exploited due to the inherent memory bottleneck of the computing architecture.  Therefore, we present in this paper a dedicated hardware implementation of a biologically plausible version of the Temporal Memory component of the Hierarchical Temporal Memory (HTM) concept. Our implementation is built on a memristive crossbar array and is the result of a hardware-algorithm co-design process. Rather than using the memristive devices solely for data storage, our approach leverages their specific switching dynamics to propose a formulation of the peripheral circuitry, resulting in a more efficient design. By combining a brain-like algorithm with emerging non-volatile memristive device technology we strive for maximum energy efficiency. We present simulation results on the training of complex high-order sequences and discuss how the system is able to predict in a context-dependent manner.  Finally, we investigate the energy consumption during the training and conclude with a discussion of scaling prospects.

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Sequence learning, prediction, and replay in networks of spiking neurons

Cited by 11 publications

References 76 publications

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Phenomenological Modeling of Diverse and Heterogeneous Synaptic Dynamics at Natural Density

System model of neuromorphic sequence learning on a memristive crossbar array

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