The role of ongoing dendritic oscillations in single-neuron dynamics

The dendritic tree contributes significantly to the elementary computations a neuron performs while converting its synaptic inputs into action potential output. Traditionally, these computations have been characterized as temporally local, near-instantaneous mappings from the current input of the cell to its current output, brought about by somatic summation of dendritic contributions that are generated in spatially localized functional compartments. However, recent evidence about the presence of oscillations in dendrites suggests a qualitatively different mode of operation: the instantaneous phase of such oscillations can depend on a long history of inputs, and under appropriate conditions, even dendritic oscillators that are remote may interact through synchronization. Here, we develop a mathematical framework to analyze the interactions of local dendritic oscillations, and the way these interactions influence single cell computations. Combining weakly coupled oscillator methods with cable theoretic arguments, we derive phase-locking states for multiple oscillating dendritic compartments. We characterize how the phase-locking properties depend on key parameters of the oscillating dendrite: the electrotonic properties of the (active) dendritic segment, and the intrinsic properties of the dendritic oscillators. As a direct consequence, we show how input to the dendrites can modulate phase-locking behavior and hence global dendritic coherence. In turn, dendritic coherence is able to gate the integration and propagation of synaptic signals to the soma, ultimately leading to an effective control of somatic spike generation. Our results suggest that dendritic oscillations enable the dendritic tree to operate on more global temporal and spatial scales than previously thought

Homeostatic Scaling of Excitability in Recurrent Neural Networks

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

A cellular mechanism for system memory consolidation

Declarative memories initially depend on the hippocampus. Over a period of weeks to years, however, these memories become hippocampus-independent through a process called system memory consolidation. The underlying cellular mechanisms are unclear. Here, we suggest a consolidation mechanism, which is based on STDP and a ubiquitous anatomical network motif. As a first step in the memory consolidation process, we consider pyramidal neurons in the hippocampal CA1 area. These cells receive Schaffer collateral (SC) input from the CA3 area at the proximal dendrites, and perforant path (PP) input from entorhinal cortex at the distal dendrites. Both pathways carry sensory information that has been processed by cortical networks and that enters the hippocampus through the entorhinal cortex. Hence, information from entorhinal cortex reaches CA1 cells through an indirect pathway (via CA3 and SC) and a direct pathway (PP). Memories are assumed to be initially stored in the recurrent CA3 network and the SC synapses during the awake, exploratory state. During a subsequent consolidation phase (during slow-wave sleep) SC-dependent memories are partly transferred to the PP synapses. Through mathematical analysis and numerical simulations we show that this consolidation process occurs as a natural result from the combination of (1) STDP at PP synapses and (2) the temporal correlations between SC and PP activities, since the (indirect) SC input is delayed compared to the (direct) PP input by about 5-10 ms. With a detailed compartmental model we then show that the spatial tuning of a CA1 cell is copied from the proximal SC-synaptic inputs to the distal PP-inputs. Next, we repeated the network motif across many levels in a hierarchical network model: each direct connection at one level is part of the indirect pathway of the next level. Analysis and simulations of this hierarchical system demonstrate that memories gradually move from hippocampus into neocortex. Moreover, the memories show power-law forgetting, as seen with psychophysical forgetting functions. Hence, our work proposes a novel mechanism to underlie system memory consolidation, allowing us to bridge spatial scales from single cells to cortical areas, and time scales from milliseconds to years