Tetzlaff, C. — Page 2 | Search bioRxiv

bioRxiv2019

The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity

Along sensory pathways, representations of environmental stimuli become increasingly sparse and expanded. If additionally the feed-forward synaptic weights are structured according to the inherent organization of stimuli, the increase in sparseness and expansion leads to a reduction of sensory noise. However, it is unknown how the synapses in the brain form the required structure, especially given the omnipresent noise of environmental stimuli. Here, we employ a combination of synaptic plasticity and intrinsic plasticity - adapting the excitability of each neuron individually - and present stimuli with an inherent organization to a feed-forward network. We observe that intrinsic plasticity maintains the sparseness of the neural code and thereby enables synaptic plasticity to learn the organization of stimuli in low-noise environments. Nevertheless, even high levels of noise can be handled after a subsequent phase of readaptation of the neuronal excitabilities by intrinsic plasticity. Interestingly, during this phase the synaptic structure has to be maintained. These results demonstrate that learning in the presence of noise requires adaptation of the synaptic structure but also of the neuronal properties in two distinct phases of learning: an encoding phase, during which the inherent organization of the environmental stimuli is learned, followed by a readaptation phase to readapt the neuronal system according to the current level of noise. The necessity of these distinct phases of learning suggests a new role for synaptic consolidation.

neuroscience↗

bioRxiv2018

Principles Underlying the Input-Dependent Formation and Organization of Memories

The brain of higher-order animals continuously and dynamically learns and adapts according to variable, complex environmental conditions. For this, the neuronal system of an agent exhibits the remarkable ability to dynamically store and organize the information of plenty of different environmental stimuli into a web of memories, which is essential for the generation of complex behaviors. The basic structures of this web are the functional organizations between two memories such as discrimination, association, and sequences. However, how these basic structures are formed robustly in an input-dependent manner by the underlying, complex and high-dimensional neuronal and synaptic dynamics is still unknown. Here, we develop a mathematical framework which reduces the complexity in several stages such that we obtain a low-dimensional mathematical description. This provides a direct link between the involved synaptic mechanisms, determining the neuronal and synaptic dynamics of the network, with the ability of the network to form diverse functional organizations. Given two widely-known synaptic mechanisms, correlation-based synaptic plasticity and homeostatic synaptic scaling, we use this new framework to identify that the interplay of these mechanisms enables the reliable formation of sequences and associations between two memory representations - missing the important functional organization of discrimination. We can show that this shortcoming can be compensated by considering a third mechanism as inhibitory synaptic plasticity. Thus, the here-presented framework and results provide a new link between diverse synaptic mechanisms and emerging functional organizations of memories. Furthermore, given our mathematical framework, one can now investigate the principles underlying the formation of the web of memories.\n\nAUTHOR SUMMARYHigher-order animals are permanently exposed to a variety of environmental inputs which have to be processed and stored such that the animal can react appropriate. Thereby, the ongoing challenge for the neuronal system is to continuously store novel and meaningful stimuli and, dependent on their content, to integrate them into the existing web of knowledge or memories. The smallest organizational entity of such a web of memories is described by the functional relation of two interconnected memories: They can be either unrelated (discrimination), mutually related (association), or uni-directionally related (sequence). However, the neuronal and synaptic dynamics underlying the formation of such structures is mainly unknown. To investigate possible links between physiological mechanisms and the organization of memories, in this work, we develop a general mathematical framework enabling analytical approach. Thereby, we show that the well-known mechanisms of synaptic plasticity and homeostatic scaling in conjunction with inhibitory synaptic plasticity enables the reliable formation of all basic relations between two memories. This work provides a further step in the understanding of the complex dynamics underlying the organization of knowledge in neural systems.

neuroscience↗

bioRxiv2018

The interplay of synaptic plasticity and scaling enables the self-organized allocation of multiple memory representations

It is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens through synaptic weight adaptation. It remains, however, largely unknown how memory allocation and storage can be achieved and coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, we show that the interplay between conventional synaptic plasticity and homeostatic synaptic scaling organizes synaptic weight adaptations such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry.

neuroscience↗