Biology subjects

Memmesheimer, R.-M.

Publications and source records attributed to Memmesheimer, R.-M..

7 recordsLinked to original sources

bioRxiv2025

Brain-wide representational drift: memory consolidation and entropic force

Memory engrams change on the microscopic level with time and experience as the neurons that compose them switch in a process termed representational drift. On the macroscopic level the engrams are not static either: numbers of engram neurons in different brain regions change over time, which is considered to reflect the process of memory consolidation. Here we predict a link between these two levels, using a novel statistical physics approach to engram modeling. Importantly, it is general as it makes only minimal assumptions on the engrams nature, does not rely on a specific architecture, and its fundamental implications hold for any representational drift with random component. Our first, analytically well tractable model shows that an entropic force emerges at the region level from random representational drift at the neuronal level. We refine the model by incorporating the interaction of the entropic force with biological processes that shape neuronal engrams. Here, the connectivity between the brain regions strongly influences the (quasi-)equilibrium engram distribution. The obtained distributions are in qualitative agreement with the ones of a biologically detailed drifting assembly model. Our engram description allows to predict the engram evolution in large neuronal systems such as the mouse brain. We find several predictions that are consistent across the valid tested parameter ranges, such as a strong tendency of the engram to leave the hippocampus. The results suggest that the brain operates in a regime where engrams drift, both deterministically and randomly, to allow for memory consolidation.

neuroscience↗

bioRxiv2024

Cooperative coding of continuous variables in networks with sparsity constraint

A hallmark of biological and artificial neural networks is that neurons tile the range of continuous sensory inputs and intrinsic variables with overlapping responses. It is characteristic for the underlying recurrent connectivity in the cortex that neurons with similar tuning predominantly excite each other. The reason for such an architecture is not clear. Using an analytically tractable model, we show that it can naturally arise from a cooperative coding scheme. In this scheme neurons with similar responses specifically support each other by sharing their computations to obtain the desired population code. This sharing allows each neuron to effectively respond to a broad variety of inputs, while only receiving few feedforward and recurrent connections. Few strong, specific recurrent connections then replace many feedforward and less specific recurrent connections, such that the resulting connectivity optimizes the number of required synapses. This suggests that the number of required synapses may be a crucial constraining factor in biological neural networks. Synaptic savings increase with the dimensionality of the encoded variables. We find a trade-off between saving synapses and response speed. The response speed improves by orders of magnitude when utilizing the window of opportunity between excitatory and delayed inhibitory currents that arises if, as found in experiments, spike frequency adaptation is present or strong recurrent excitation is balanced by strong, shortly-lagged inhibition. Author summaryNeurons represent continuous sensory or intrinsic variables in their joint activity, with rather broad and overlapping individual response profiles. In particular there are often many neurons with highly similar tuning. In the cortex, these neurons predominantly excite each other. We provide a new explanation for this type of recurrent excitation, showing that it can arise in a novel cooperative coding scheme that minimizes the number of required synapses. This suggests the number of required synapses as a crucial constraining factor in biological neural networks. In our cooperative coding scheme, neurons use few strong and specific excitatory connections to share their computations with those neurons that also need it. This way, neurons can generate a large part of their response by leveraging inputs from neurons with similar responses. This allows to replace many feedforward and less specific recurrent connections by few specific recurrent connections. We find a trade-off between saving synapses and response speed. Theoretical estimates and numerical simulations show that specific features of biological single neurons and neural networks can drastically increase the response speed, improving the trade-off.

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bioRxiv2022

Purely STDP-based assembly dynamics: stability, learning, overlaps, drift and aging

Memories may be encoded in the brain via strongly interconnected groups of neurons, called assemblies. The concept of Hebbian plasticity suggests that these assemblies are generated through synaptic plasticity, strengthening the recurrent connections within select groups of neurons that receive correlated stimulation. To remain stable in absence of such stimulation, the assemblies need to be self-reinforcing under the plasticity rule. Previous models of such assembly maintenance require additional mechanisms of fast homeostatic plasticity often with biologically implausible timescales. Here we provide a model of neuronal assembly generation and maintenance purely based on spike-timing-dependent plasticity (STDP) between excitatory neurons. It uses irregularly and stochastically spiking neurons and STDP that depresses connections of uncorrelated neurons. We find that assemblies do not grow beyond a certain size, because temporally imprecisely correlated spikes dominate the plasticity in large assemblies. Assemblies in the model can be learned or spontaneously emerge. The model allows for prominent, stable overlap structures between static assemblies. Further, assemblies can drift, particularly according to a novel, transient overlap-based mechanism. Finally the model indicates that assemblies grow in the aging brain, where connectivity decreases. Author summaryIt is widely assumed that memories are represented by ensembles of nerve cells that have strong interconnections with each other. It is to date not clear how such strongly interconnected nerve cell ensembles form, persist, change and age. Here we show that already a basic rule for activity-dependent synaptic strength plasticity can explain the learning or spontaneous formation and the stability of assemblies. In particular, it is not necessary to explicitly keep the overall total synaptic strength of a neuron nearly constant, a constraint that was incorporated in previous models in a manner inconsistent with current experimental knowledge. Furthermore, our model achieves the challenging task of stably maintaining many overlaps between assemblies and generating the experimentally observed drift of memory representations. Finally, the model predicts that if the number of synaptic connections in the brain decreases, as observed during aging, the size of the neuron ensembles underlying memories increases. This may render certain memories in the aging brain more robust and prominent but also less specific.

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bioRxiv2021

Weight perturbation learning outperforms node perturbation on broad classes of temporally extended tasks

Biological constraints often impose restrictions for plausible plasticity rules such as locality and reward-based rather than supervised learning. Two learning rules that comply with these restrictions are weight (WP) and node (NP) perturbation. NP is often used in learning studies, in particular as a benchmark; it is considered to be superior to WP and more likely neurobiologically realized, as the number of weights and therefore their perturbation dimension typically massively exceeds the number of nodes. Here we show that this conclusion no longer holds when we take two biologically relevant properties into account: First, tasks extend in time. This increases the perturbation dimension of NP but not WP. Second, tasks are low dimensional, with many weight configurations providing solutions. We analytically delineate regimes where these properties let WP perform as well as or better than NP. Furthermore we find that the changes in weight space directions that are irrelevant for the task differ qualitatively between WP and NP and that only in WP gathering batches of subtasks in a trial decreases the number of trials required. This may allow to experimentally distinguish which of the two rules underlies a learning process. Our insights suggest new learning rules, which combine for specific task types the advantages of WP and NP. If the inputs are similarly correlated, temporally correlated perturbations improve NP. Using numerical simulations, we generalize the results to networks with various architectures solving biologically relevant and standard network learning tasks. Our findings, together with WPs practicability suggest WP as a useful benchmark and plausible model for learning in the brain.

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bioRxiv2021

High-frequency oscillations and replay in a two-population model of hippocampal region CA1

Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with onmodulated (140 - 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons and investigate its ability to generate ripple oscillations using extensive simulations. We find that with biologically plausible values for single neuron, synapse and connectivity parameters, random connectivity and absent strong feedforward drive to the inhibitory population, oscillation patterns similar to in vivo sharp wave/ripples can only be generated if excitatory cell spiking is triggered by short pulses of external excitation. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter implies phases of silence in specific basket cell groups and selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well. Author summaryDuring certain phases of sleep, rest and consummatory behavior the hippocampus brain area of many species, including humans, is known to intermittently generate strong high frequency oscillations. These oscillations are important for memory formation and consolidation. To date, the mechanisms underlying their generation remain incompletely understood. We find that in unstructured networks carefully designing how excitation is transmitted in the hippocampus is required for the generation of robust fast oscillations in its main output region. Broad, temporally extended excitation of cells results in unrealistic single cell activity, whereas temporally narrow input that differs from cell to cell gives rise to oscillations with realistic single cell and network activity. We show that the biophysical mechanism to generate the required temporally narrow excitation may be related to spiking events in the dendrites, which are triggered by coincident input. Our results in structured networks suggest that the interplay of hippocampal excitation and inhibition can serve as a means to generate robust sequential activity, which is thought to be crucial for memory formation and recall. The sequence generation mechanism also leads to strong high frequency oscillations with sparse excitatory cell and frequent inhibitory cell spiking, as observed in the hippocampus.

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bioRxiv2020

Drifting Assemblies for Persistent Memory

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless behaviors and memories often persist over long times. In a standard model, memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of connections and neural representations. The assemblies drift freely as spontaneous synaptic turnover or random activity induce neuron exchange. The gradual exchange allows activity dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on the temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

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bioRxiv2019

From Single Neurons to Behavior in the Jellyfish Aurelia aurita

Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-eZcient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfishs muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control.

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