Biology subjects

Legenstein, R.

Publications and source records attributed to Legenstein, R..

8 recordsLinked to original sources

bioRxiv2023

Non-synaptic plasticity enables memory-dependent local learning

Synaptic plasticity is essential for memory formation and learning in the brain. In addition, recent results indicate that non-synaptic plasticity processes such as the regulation of neural membrane properties contribute to memory formation, its functional role in memory and learning has however remained elusive. Here, we propose that non-synaptic and synaptic plasticity are both essential components to enable memory-dependent processing in neuronal networks. While the former acts on a fast time scale for rapid information storage, the latter shapes network processing on a slower time scale to harness this memory as a functional component. We analyse this concept in a network model where pyramidal neurons regulate their apical trunk excitability in a Hebbian manner. We find that local synaptic plasticity rules can be derived for this model and show that the interplay between this synaptic plasticity and the non-synaptic trunk plasticity enables the model to successfully accommodate memory-dependent processing capabilities in a number of tasks, ranging from simple memory tests to question answering. The model can also explain contextual fear conditioning experiments, where freezing responses could be recovered by optogenetic reactivation of memory engrams under amnesia. Author summaryHow memory is organized in the brain in order to enable cognitive processing is a central open question in systems neuroscience. Traditionally, synaptic plasticity is considered the key mechanism for the establishment of memory in the brain. Recently however, this view has been questioned, and it was proposed that non-synaptic plasticity mechanisms play a more prominent role as previously considered. In this article, we propose that both, synaptic and non-synaptic plasticity are central components for the formation and utilization of memory in biological neuronal networks. Our results show that non-synaptic plasticity can act on a fast time-scale to store important information, while synaptic plasticity can adapt network function on a slow time scale in order to facilitate memory-dependent cognitive processing.

neuroscience↗

bioRxiv2021

Spike-based symbolic computations on bit strings and numbers

The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically plausible learning rule. The resulting networks operate in a rather low firing rate regime, where they could not simply emulate artificial neural networks by encoding continuous values through firing rates. Thus, we propose here a new paradigm for symbolic computation in neural networks that provides concrete hypotheses about the organization of symbolic computations in the brain. The employed spike-based network models are the basis for drastically more energy-efficient computer hardware - neuromorphic hardware. Hence, our results can be seen as creating a bridge from symbolic artificial intelligence to energy-efficient implementation in spike-based neuromorphic hardware.

neuroscience↗

bioRxiv2021

Synaptic plasticity in distal dendrites can optimize probabilistic top-down predictions in pyramidal cells

Sensory information is processed by the brain not in a simple feedforward fashion. Rather, bottom-up inputs are combined in pyramidal cells of sensory cortices with top-down information from higher brain areas that arrives through synapses in apical dendrites. The exact functional role of these top-down inputs has remained unknown. A promising abstract model posits that they provide probabilistic priors for bottom-up sensory inputs. We show that this hypothesis is consistent with a large number of experimental about synaptic plasticity in apical dendrites, in particular with the prominent role of NMDA-spikes. We identify conditions under which this synaptic plasticity could approximate the gold standard for self-supervised learning of probabilistic priors: logistic regression. Furthermore, this perspective suggests an additional functional role for the complex structure of the dendritic arborization plays: It enables the neuron to learn substantially more complex landscapes of probabilistic priors.

neuroscience↗

bioRxiv2021

Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks

Spike-based neural network models have so far not been able to reproduce the capability of the brain to learn from very few, often even from just a single example. We show that this deficiency of models disappears if one allows synaptic weights to store priors and other information that optimize the learning process, while using the network state to quickly absorb information from new examples. For that, it suffices to include biologically realistic neurons with spike frequency adaptation in the neural network model, and to optimize the learning process through meta-learning. We demonstrate this on a variety of tasks, including fast learning and deletion of attractors, adaptation of motor control to changes in the body, and solving the Morris water maze task - a paradigm for fast learning of navigation to a new goal. Significance StatementIt has often been conjectured that STDP or other rules for synaptic plasticity can only explain some of the learning capabilities of brains. In particular, learning a new task from few trials is likely to engage additional mechanisms. Results from machine learning show that artificial neural networks can learn from few trials by storing information about them in their network state, rather than encoding them in synaptic weights. But these machine learning methods require neural networks with biologically unrealistic LSTM (Long Short Term Memory) units. We show that biologically quite realistic models for neural networks of the brain can exhibit similar capabilities. In particular, these networks are able to store priors that enable learning from very few examples.

neuroscience↗

bioRxiv2020

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

The ability to base current computations on memories from the past is critical for many cognitive tasks such as story understanding. Hebbian-type synaptic plasticity is believed to underlie the retention of memories over medium and long time scales in the brain. However, it is unclear how such plasticity processes are integrated with computations in cortical networks. Here, we propose Hebbian Memory Networks (H-Mems), a simple neural network model that is built around a core hetero-associative network subject to Hebbian plasticity. We show that the network can be optimized to utilize the Hebbian plasticity processes for its computations. H-Mems can one-shot memorize associations between stimulus pairs and use these associations for decisions later on. Furthermore, they can solve demanding question-answering tasks on synthetic stories. Our study shows that neural network models are able to enrich their computations with memories through simple Hebbian plasticity processes.

neuroscience↗

bioRxiv2020

Spike-frequency adaptation provides a long short-term memory to networks of spiking neurons

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well known property of a substantial fraction of neurons in the neocortex -- especially in higher areas of the human neocortex -- moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.

neuroscience↗

bioRxiv2019

A solution to the learning dilemma for recurrent networks of spiking neurons

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. But in spite of extensive research, it has remained open how they can learn through synaptic plasticity to carry out complex network computations. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A new mathematical insight tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This new learning method - called e-prop - approaches the performance of BPTT (backpropagation through time), the best known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in novel energy-efficient spike-based hardware for AI.

neuroscience↗

bioRxiv2017

Associations between memory traces emerge in a generic neural circuit model through STDP

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. How-ever, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity (STDP). The model depends critically on two parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these two parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence our findings suggest that the brain can use both of these two neural codes for associations, and dynamically switch between them during consolidation.

neuroscience↗