Predictive coding with spiking neurons and feedforward gist signaling
Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-04-01
|
| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2024.1338280/full |
| _version_ | 1797213532037578752 |
|---|---|
| author | Kwangjun Lee Shirin Dora Shirin Dora Jorge F. Mejias Sander M. Bohte Sander M. Bohte Cyriel M. A. Pennartz |
| author_facet | Kwangjun Lee Shirin Dora Shirin Dora Jorge F. Mejias Sander M. Bohte Sander M. Bohte Cyriel M. A. Pennartz |
| author_sort | Kwangjun Lee |
| collection | DOAJ |
| description | Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature. |
| first_indexed | 2024-04-24T10:59:46Z |
| format | Article |
| id | doaj.art-713c72276f8d44a08c5cc98cca48e404 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-24T10:59:46Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-713c72276f8d44a08c5cc98cca48e4042024-04-12T04:18:48ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882024-04-011810.3389/fncom.2024.13382801338280Predictive coding with spiking neurons and feedforward gist signalingKwangjun Lee0Shirin Dora1Shirin Dora2Jorge F. Mejias3Sander M. Bohte4Sander M. Bohte5Cyriel M. A. Pennartz6Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, NetherlandsCognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, NetherlandsDepartment of Computer Science, School of Science, Loughborough University, Loughborough, United KingdomCognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, NetherlandsCognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, NetherlandsMachine Learning Group, Centre of Mathematics and Computer Science, Amsterdam, NetherlandsCognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, NetherlandsPredictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature.https://www.frontiersin.org/articles/10.3389/fncom.2024.1338280/fullpredictive processingvisual cortexspiking neural networkHebbian learningunsupervised learningrepresentation learning |
| spellingShingle | Kwangjun Lee Shirin Dora Shirin Dora Jorge F. Mejias Sander M. Bohte Sander M. Bohte Cyriel M. A. Pennartz Predictive coding with spiking neurons and feedforward gist signaling Frontiers in Computational Neuroscience predictive processing visual cortex spiking neural network Hebbian learning unsupervised learning representation learning |
| title | Predictive coding with spiking neurons and feedforward gist signaling |
| title_full | Predictive coding with spiking neurons and feedforward gist signaling |
| title_fullStr | Predictive coding with spiking neurons and feedforward gist signaling |
| title_full_unstemmed | Predictive coding with spiking neurons and feedforward gist signaling |
| title_short | Predictive coding with spiking neurons and feedforward gist signaling |
| title_sort | predictive coding with spiking neurons and feedforward gist signaling |
| topic | predictive processing visual cortex spiking neural network Hebbian learning unsupervised learning representation learning |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2024.1338280/full |
| work_keys_str_mv | AT kwangjunlee predictivecodingwithspikingneuronsandfeedforwardgistsignaling AT shirindora predictivecodingwithspikingneuronsandfeedforwardgistsignaling AT shirindora predictivecodingwithspikingneuronsandfeedforwardgistsignaling AT jorgefmejias predictivecodingwithspikingneuronsandfeedforwardgistsignaling AT sandermbohte predictivecodingwithspikingneuronsandfeedforwardgistsignaling AT sandermbohte predictivecodingwithspikingneuronsandfeedforwardgistsignaling AT cyrielmapennartz predictivecodingwithspikingneuronsandfeedforwardgistsignaling |