Biology subjects

Memmesheimer, R.-M.

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

4 recordsLinked to original sources

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗