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| Hauptseite > Publikationsdatenbank > Probabilistic sequential memory recall in spiking neuronal networks > print |
| Hauptseite > Publikationsdatenbank > Probabilistic sequential memory recall in spiking neuronal networks > print |
| 001 | 1022054 | ||
| 005 | 20250203103137.0 | ||
| 037 | _ | _ | |a FZJ-2024-01191 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Bouhadjar, Younes |0 P:(DE-Juel1)176778 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a OCNS |c Leipzig |d 2023-07-15 - 2023-07-19 |w Germany |
| 245 | _ | _ | |a Probabilistic sequential memory recall in spiking neuronal networks |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
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| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1730981528_13200 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a 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. The model consists of a sparsely connected network of excitatory neurons and a single inhibitory neuron. Excitatory neurons are divided into subpopulations with identical stimulus preferences. External inputs representing specific sequence elements provide neurons in the same subpopulation with identical input. The inhibitory neuron mediates competition between neurons within subpopulations and promotes sparse activity and context sensitivity. Synapses between excitatory neurons are plastic and subject to spike-timing-dependent plasticity and homeostatic control. After learning, the network develops specific subnetworks representing the learned sequences, such that the presentation of a sequence element leads to a context dependent prediction of the subsequent element. 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. |
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