The generation of cortical novelty responses through inhibitory plasticity

Schulz, Auguste; Miehl, Christoph; Berry, Michael; Gjorgjieva, Julijana

doi:10.7554/elife.65309

Cited by 41 publications

(56 citation statements)

References 117 publications

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“…Therefore, we extend previously studied roles of inhibitory plasticity, which include the stabilization of excitatory rates (Vogels et al, 2011;Sprekeler, 2017), decorrelation of neuronal responses (Duarte and Morrison, 2014), preventing winner-take-all mechanisms in networks with multiple stable states (Litwin-Kumar and Doiron, 2014) or generating differences among novel versus familiar stimuli (Schulz et al, 2021). Recent computational studies also include novel ways of inhibitory influence, like presynaptic inhibition via GABA spillover (Naumann and Sprekeler, 2020) or an input-dependent inhibitory plasticity mechanism (Kaleb et al, 2021).…”

Section: Predictionsmentioning

confidence: 72%

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Miehl

Gjorgjieva

2022

Preprint

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Synaptic changes underlie learning and memory formation in the brain. But synaptic plasticity of excitatory synapses on its own is unstable, leading to unlimited growth of synaptic strengths without additional homeostatic mechanisms. To control excitatory synaptic strengths we propose a novel form of synaptic plasticity at inhibitory synapses. We identify two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change in the same direction as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity achieve a fixed excitatory/inhibitory set-point in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.

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Section: Predictionsmentioning

confidence: 72%

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Miehl

Gjorgjieva

2022

Preprint

Self Cite

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“…For unpredicted stimuli, this feedback is not effective, resulting in non-sparse firing indicating a mismatch. In [ 60 ], a similar mechanism is employed to generate mismatch signals for novel stimuli. In this study, the strength of the inhibitory feedback needs to be learned by means of inhibitory synaptic plasticity.…”

Section: Discussionmentioning

confidence: 99%

“…In our model, the WTA mechanism is controlled by the predictions (dAPs) and implemented by static inhibitory connections. Furthermore, the model in [ 60 ] can learn a set of elements, but not the order of these elements in the sequence.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

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“…Many related studies have shown evidence for cortical novelty responses across brain regions, with wide variation in the effort to control for adaptation ( Kato et al, 2015 ; Makino and Komiyama, 2015 ; Garrett et al, 2020 ; Poort et al, 2021 ; Schulz et al, 2021 ; Homann et al, 2022 ; Montgomery et al, 2022 ). In most cases, consistent with efficient coding, novel stimuli evoke more spiking activity than familiar.…”

Section: Efficient Coding In Primary Visual Cortexmentioning

confidence: 99%

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

Price

Gavornik

2022

Front. Comput. Neurosci.

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While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.

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The generation of cortical novelty responses through inhibitory plasticity

Cited by 41 publications

References 117 publications

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

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

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

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