Publications
9 results found
Wilmes K, Clopath C, 2023, Dendrites help mitigate the plasticity-stability dilemma, Scientific Reports, Vol: 13, Pages: 1-15, ISSN: 2045-2322
With Hebbian learning ‘who fires together wires together’, well-known problems arise. Hebbian plasticity can cause unstable network dynamics and overwrite stored memories. Because the known homeostatic plasticity mechanisms tend to be too slow to combat unstable dynamics, it has been proposed that plasticity must be highly gated and synaptic strengths limited. While solving the issue of stability, gating and limiting plasticity does not solve the stability-plasticity dilemma. We propose that dendrites enable both stable network dynamics and considerable synaptic changes, as they allow the gating of plasticity in a compartment-specific manner. We investigate how gating plasticity influences network stability in plastic balanced spiking networks of neurons with dendrites. We compare how different ways to gate plasticity, namely via modulating excitability, learning rate, and inhibition increase stability. We investigate how dendritic versus perisomatic gating allows for different amounts of weight changes in stable networks. We suggest that the compartmentalisation of pyramidal cells enables dendritic synaptic changes while maintaining stability. We show that the coupling between dendrite and soma is critical for the plasticity-stability trade-off. Finally, we show that spatially restricted plasticity additionally improves stability.
Poort J, Wilmes KA, Blot A, et al., 2022, Learning and attention increase visual response selectivity through distinct mechanisms., Neuron, Vol: 110, Pages: 689-697.e6, ISSN: 0896-6273
Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance and across seconds when animals switch attention. While both phenomena occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales.
Prince LY, Boven E, Eyono RH, et al., 2021, CCN GAC Workshop: Issues with learning in biological recurrent neural networks
This perspective piece came about through the Generative AdversarialCollaboration (GAC) series of workshops organized by the ComputationalCognitive Neuroscience (CCN) conference in 2020. We brought together a numberof experts from the field of theoretical neuroscience to debate emerging issuesin our understanding of how learning is implemented in biological recurrentneural networks. Here, we will give a brief review of the common assumptionsabout biological learning and the corresponding findings from experimentalneuroscience and contrast them with the efficiency of gradient-based learningin recurrent neural networks commonly used in artificial intelligence. We willthen outline the key issues discussed in the workshop: synaptic plasticity,neural circuits, theory-experiment divide, and objective functions. Finally, weconclude with recommendations for both theoretical and experimentalneuroscientists when designing new studies that could help to bring clarity tothese issues.
Wilmes KA, Clopath C, 2019, Inhibitory microcircuits for top-down plasticity of sensory representations, Nature Communications, Vol: 10, ISSN: 2041-1723
Rewards influence plasticity of early sensory representations. The underlying changes in cir-cuitry are however unclear. Recent experimental findings suggest that inhibitory circuits regu-late learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs,including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major rolein adjusting stimulus representations. We investigate how top-down modulation by rewards in-teracts with local plasticity to induce long-lasting changes in circuitry. Using a computationalmodel of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can storeinformation about rewarded stimuli to instruct long-term changes in excitatory connectivity inthe absence of further reward. In our model, stimulus-tuned somatostatin-positive interneuronsdevelop strong connections to parvalbumin-positive interneurons during reward such that theyselectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leadingto increased stimulus representation. We make specific testable predictions and show that thistwo-stage model allows for translation invariance of the learned representation.
Wilmes KA, Clopath C, 2018, Inhibitory microcircuits for top-down processing of sensory representations, Publisher: BioRxiv
Humans and animals are remarkable at detecting stimuli that predict rewards. While the underlying neural mechanisms are unknown, reward influences plasticity of sensory representations in early sensory areas. The underlying changes in excitatory and inhibitory circuitry are however unclear. Recently, experimental findings suggest that the inhibitory circuits can regulate learning. In addition, the inhibitory neurons in superficial layers are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that plasticity of interneuron circuits plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards can interact with local excitatory and inhibitory plasticity to induce long-lasting changes in sensory circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron networks can store information about the rewarded stimulus to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons (SSTs) develop strong connections to parvalbumin-positive interneurons (PVs) during reward presentation such that they selectively disinhibit the pyramidal layer henceforth. This triggers plasticity in the excitatory neurons, which leads to increased stimulus representation. We make specific testable predictions in terms of the activity of different neuron types. We finally show that this two-stage model allows for translation invariance of the learned representation.
Bono J, Wilmes K, Clopath C, 2017, Modelling plasticity in dendrites: from single cells to networks, Current Opinion in Neurobiology, Vol: 46, Pages: 136-141, ISSN: 0959-4388
One of the key questions in neuroscience is how our brain self-organises to efficiently process information. To answer this question, we need to understand the underlying mechanisms of plasticity and their role in shaping synaptic connectivity. Theoretical neuroscience typically investigates plasticity on the level of neural networks. Neural network models often consist of point neurons, completely neglecting neuronal morphology for reasons of simplicity. However, during the past decades it became increasingly clear that inputs are locally processed in the dendrites before they reach the cell body. Dendritic properties enable local interactions between synapses and location-dependent modulations of inputs, rendering the position of synapses on dendrites highly important. These insights changed our view of neurons, such that we now think of them as small networks of nearly independent subunits instead of a simple point. Here, we propose that understanding how the brain processes information strongly requires that we consider the following properties: which plasticity mechanisms are present in the dendrites and how do they enable the self-organisation of synapses across the dendritic tree for efficient information processing? Ultimately, dendritic plasticity mechanisms can be studied in networks of neurons with dendrites, possibly uncovering unknown mechanisms that shape the connectivity in our brains.
Wilmes KA, Schleimer J-H, Schreiber S, 2016, Spike-timing dependent inhibitory plasticity to learn a selective gating of backpropagating action potentials, European Journal of Neuroscience, Vol: 45, Pages: 1032-1043, ISSN: 0953-816X
Wilmes KA, Sprekeler H, Schreiber S, 2016, Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons, PLOS Computational Biology, Vol: 12, Pages: e1004768-e1004768
Cohen MX, Wilmes KA, van de Vijver I, 2011, Cortical electrophysiological network dynamics of feedback learning, Trends in Cognitive Sciences, Vol: 15, Pages: 558-566, ISSN: 1364-6613
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