Biology subjects

Vogels, T. P.

Publications and source records attributed to Vogels, T. P..

23 records · Page 2Linked to original sources

bioRxiv2020

Context-modular memory networks support high-capacity, flexible, and robust associative memories

Biological memory is known to be flexible -- memory formation and recall depend on factors such as the behavioral context of the organism. However, this property is often ignored in associative memory models. Here, we bring this dynamic nature of memory to the fore by introducing a novel model of associative memory, which we refer to as the context-modular memory network. In our model, stored memory patterns are associated to one of several background network states, or contexts. Memories are accessible when their corresponding context is active, and are otherwise inaccessible. Context modulates the effective network connectivity by imposing a specific configuration of neuronal and synaptic gating - gated neurons (respectively synapses) have their activity (respectively weights) momentarily silenced, thereby reducing interference from memories belonging to other contexts. Memory patterns are randomly and independently chosen, while neuronal and synaptic gates may be selected randomly or optimized through a process of contextual synaptic refinement. Through signal-to-noise and mean field analyses, we show that context-modular memory networks can exhibit substantially increased memory capacity with random neuronal gating, but not with random synaptic gating. For contextual synaptic refinement, we devise a method in which synapses are gated off for a given context if they destabilize the memory patterns in that context, drastically improving memory capacity. Notably, synaptic refinement allows for patterns to be accessible in multiple contexts, stabilizing memory patterns even for weight matrices that do not contain any information about the memory patterns such as Gaussian random matrices. Lastly, we show that context modulates the relative stability of accessible versus inaccessible memories, thereby confirming that contextual control acts as a mechanism to temporarily hide or reveal particular memories. Overall, our model integrates recent ideas about context-dependent memory organization with classic associative memory models, highlights an intriguing trade-off between memory capacity and accessibility, and carries important implications for the understanding of biological memory storage and recall in the brain.

neuroscience↗

bioRxiv2019

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.

neuroscience↗

bioRxiv2019

Complementary inhibitory receptive fields emerge from synaptic plasticity and create an attentional switch in sensory circuits

Cortical areas comprise multiple types of inhibitory interneurons with stereotypical connectivity motifs, but their combined effect on postsynaptic dynamics has been largely unexplored. Here, we analyse the response of a single postsynaptic model neuron receiving tuned excitatory connections alongside inhibition from two plastic populations. Depending on the inhibitory plasticity rule, synapses remain unspecific (flat), become anti-correlated to, or mirror excitatory synapses. Crucially, the neurons receptive field, i.e., its response to presynaptic stimuli, depends on the modulatory state of inhibition. When both inhibitory populations are active, inhibition balances excitation, resulting in uncorrelated postsynaptic responses regardless of the inhibitory tuning profiles. Modulating the activity of a given inhibitory population produces strong correlations to either preferred or non-preferred inputs, in line with recent experimental findings showing dramatic context-dependent changes of neurons receptive fields. We thus confirm that a neurons receptive field doesnt follow directly from the weight profiles of its presynaptic afferents.

neuroscience↗

bioRxiv2018

Motor primitives in space and time via targeted gain modulation in cortical networks

Motor cortex (M1) exhibits a rich repertoire of activities to support the generation of complex movements. Although recent neuronal-network models capture many qualitative aspects of M1 dynamics, they can generate only a few distinct movements. Additionally, it is unclear how M1 efficiently controls movements over a wide range of shapes and speeds. We demonstrate that simple modulation of neuronal input-output gains in recurrent neuronal-network models with fixed architecture can dramatically reorganize neuronal activity and thus downstream muscle outputs. Consistent with the observation of diffuse neuromodulatory projections to M1, we show that a relatively small number of modulatory control units provide sufficient flexibility to adjust high-dimensional network activity using a simple reward-based learning rule. Furthermore, it is possible to assemble novel movements from previously learned primitives, and one can separately change movement speed while preserving movement shape. Our results provide a new perspective on the role of modulatory systems in controlling recurrent cortical activity.

neuroscience↗

bioRxiv2017

Inhibitory Engrams In Perception And Memory.

Nervous systems use excitatory cell assemblies or \"perceptual engrams\" to encode and represent sensory percepts. Similarly, synaptically connected cell assemblies or \"memory engrams\" are thought to represent memories of past experience. Multiple lines of recent evidence indicate that brain systems also create inhibitory replicas of excitatory engrams with important cognitive functions. Such matched inhibitory engrams may form through homeostatic potentiation of inhibition onto postsynaptic cells that show increased levels of excitation. Inhibitory engrams can reduce behavioral responses to familiar stimuli thereby resulting in behavioral habituation. In addition, by preventing inappropriate activation of excitatory memory engrams, inhibitory engrams can make memories quiescent, stored in a latent form that is available for contextrelevant activation. In neural networks with balanced excitatory and inhibitory engrams, the release of innate responses and recall of associative memories can occur through focussed disinhibition. Understanding mechanisms that regulate the formation and expression of inhibitory engrams in vivo may help not only to explain key features of cognition, but also to provide insight into transdiagnostic traits associated with psychiatric conditions such as autism, schizophrenia and post-traumatic stress disorder (PTSD).

neuroscience↗