Continual Learning in a Multi-Layer Network of an Electric Fish

Muller, Salomon; Zadina, Abigail N.; Abbott, L. F.; Sawtell, Nate

doi:10.2139/ssrn.3393520

Cited by 11 publications

(24 citation statements)

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“…Empirical studies also suggest that plateau potentials generated from electrotonically segregated dendrites can alter the plasticity in feedforward connections [158][159][160] , sometimes with as few as five pairings of subthreshold presynaptic activity and at behavioural time scales 161,162 . These plasticity findings may help explain how dendritic segregation may be used to compute differences and inform synaptic updates.…”

Section: Implementing Ngrads In Biological Circuitsmentioning

confidence: 99%

Backpropagation and the brain

et al. 2020

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During learning the brain modifies synapses to improve behaviour. In the cortex synapses are embedded within multi-layered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in deep artificial neural networks, but has historically been viewed as biologically problematic. Nonetheless, recent developments in neuroscience and the successes of artificial neural networks have reinvigorated interest in whether backpropagation offers insights for understanding learning in the cortex. The backpropagation algorithm learns quickly by computing synaptic updates using feedback connections to deliver error signals. While feedback connections are ubiquitous in the cortex, it is difficult to see how they could deliver the error signals required by strict formulations of backpropagation. Here we build on past and recent developments to argue that feedback connections may instead induce neural activities whose differences can be used to locally approximate these signals, and hence drive effective learning in deep networks in the brain.

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Section: Implementing Ngrads In Biological Circuitsmentioning

confidence: 99%

Backpropagation and the brain

et al. 2020

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“…This dramatic effect is due to synaptic plasticity of the corollary discharge descending input when it is paired with afferent sensory input, and results in a near perfect match between EOD signal and its negative image. Muller et al [2] now provide deeper insight into the intricacies of EOD stimulus cancellation and how it is implemented by operations similar to those of artificial neural networks.…”

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confidence: 99%

“…Unlike the case for output cells, the narrow spike response of ganglionic cells remains strongly modulated even when EOD afferent and corollary discharge inputs are paired, that is, when there is no cancellation. Muller et al [2] discovered that the effects of such modulation only made sense upon a further subdivision of the ganglionic cells: ganglionic cells were defined as BS + when their broad spikes were evoked by EOD activation of ampullary receptors, and BSwhen their broad spikes were inhibited by EOD input. Muller et al [2] propose a clever circuit scheme by which the synaptic plasticity of BS + and BScells and their connectivity with output neurons could account for EOD input cancellation in output cells.…”

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confidence: 99%

“…Mueller et al's [2] well defined model makes a critical prediction: for it to work, BS + cells would have to project selectively to OFF cells and BScells would have to project selectively to ON cells. Careful anatomical reconstruction of both types of ganglionic neurons perfectly verified this prediction.…”

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confidence: 99%

“…In artificial neural network terms, Mueller et al [2] have answered all the questions raised above. The intermediate ganglionic cell layer estimates the intensity and time course of the output cell response to an EOD, and can therefore generate a negative image to cancel this response.…”

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Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals

Maler

2020

Current Biology

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about MEK-ERK docking, it is entirely plausible to think that there is a mix of processive and distributive phosphorylation, depending on the probability of catalysis versus the probability of undocking. Mutants that change the former but not the latter would be expected to increase both the processivity and the overall rate of ERK activation. It will be interesting to see if other activated MEK mutants follow this pattern.

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Bidirectional synaptic plasticity rapidly modifies hippocampal representations

Milstein

Bittner

et al. 2020

Preprint

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15According to standard models of synaptic plasticity, correlated activity between 16 connected neurons drives changes in synaptic strengths to store associative 17 memories. Here we tested this hypothesis in vivo by manipulating the activity of 18 hippocampal place cells and measuring the resulting changes in spatial selectivity. 19 We found that the spatial tuning of place cells was rapidly reshaped via 20 bidirectional synaptic plasticity. To account for the magnitude and direction of 21 plasticity, we evaluated two models -a standard model that depended on 22 synchronous pre-and post-synaptic activity, and an alternative model that 23 1 depended instead on whether active synaptic inputs had previously been 1 potentiated. While both models accounted equally well for the data, they predicted 2 opposite outcomes of a perturbation experiment, which ruled out the standard 3 correlation-dependent model. Finally, network modeling suggested that this form 4 of bidirectional synaptic plasticity enables population activity, rather than pairwise 5 neuronal correlations, to drive plasticity in response to changes in the 6 environment. 7 8 Main Text 9 Activity-dependent changes in synaptic strength can flexibly alter the selectivity of 10 neuronal firing for particular features of the environment, providing a cellular substrate for 11 learning and memory. Various forms of Hebbian synaptic plasticity have been considered 12 for decades to be the main or even only synaptic plasticity mechanisms present within 13 most brain regions of a number of species. The core feature of such plasticity 14mechanisms is that they are autonomously driven by the repeated presence of correlated 15 presynaptic and postsynaptic activity that leads to either increases or decreases in 16 synaptic strength depending on the exact temporal coincidence (1-4). 17 The hippocampus plays an important role in many forms of learning and memory, 18 and the spatial firing rates of hippocampal place cells have been shown to change with 19 alterations in environmental context or the locations of salient features, like reward (5-20 11). Furthermore in CA1 neurons, place cell activity can emerge in a single trial following 21 a dendritic calcium spike (also called a plateau potential) (12-14). The form of synaptic 22 plasticity responsible for this rapid change in selectivity, termed behavioral timescale 23 2 synaptic plasticity (BTSP), modifies synaptic inputs active within a multi-second time 1 window around the plateau potential. That BTSP strengthens many synaptic inputs whose 2 activation did not cause or even coincide with postsynaptic activity suggests that it might 3 be a fundamentally different form of plasticity than classical correlation-driven Hebbian 4 plasticity (1-3). Such a plasticity rule could enable representation learning in cortical brain 5 regions like the hippocampus to be guided by delayed behavioral outcomes, rather than 6 by short timescale associations of neuronal input and output. However, it was not clear 7 from previous experiments...

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Continual Learning in a Multi-Layer Network of an Electric Fish

Cited by 11 publications

References 0 publications

Backpropagation and the brain

Backpropagation and the brain

Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals

Bidirectional synaptic plasticity rapidly modifies hippocampal representations

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