Network Activity and Plasticity

The cortex must maintain balanced levels of neural activity to correctly integrate inputs and to provide contextually meaningful outputs. Neuronal excitation is counterbalanced by various forms of inhibition such as spike frequency adaptation, short- and long-term depression at excitatory synapses, and inhibitory cell coupling. When excitatory activity is low, homeostatic mechanisms enhance network excitability. When excitation is stronger than inhibition, network bursts of activity arise that also need to be dampened lest they run out of control. A disruption of the fine balance between excitatory and inhibitory systems is observed in an increasing numbers of brain disorders such as epilepsy, mental retardation and autism. This thesis aims to study aspects of the regulation of excitation in cortical networks at a molecular, cellular, network and disease level of investigation. We present in the first chapter an investigation of the impact of network bursts on standard spike-timing dependent plasticity (STDP) events in acute cortical brain slices. We discovered that these two kinds of events, which are the most well-established ways to induce synaptic plasticity in vitro, can block each other as well as cooperate to change the amplitude and direction of plasticity. Because the outcome of the interaction depends on the precise timing of network bursts relative to a STDP event in a single synaptic pathway, the observed network-timing dependent plasticity provides a novel mechanism for the modulation of local synaptic plasticity by the network level of activity, a form of global control of local synaptic plasticity. We hypothesized that network bursts consume extracellular resources needed to potentiate, restricting potentiation to active synapses at the burst onset while yielding depression of synapses activated at later times. As we demonstrate with simulation, this simple mechanism supports paradigm-shifting implications for models of learning, solving a persistent and fundamental problem since BCM (Bienenstock Cooper Munro, 1981) to incorporate synaptic learning into realistic neuronal networks while simultaneously establishing and maintaining excitatory-inhibitory balance. The proposed mechanism also links experimental evidence for interactions between STDP and network activity to observations of self-organized criticality in neural systems, a regime known to be optimal for information coding. In the second chapter, we investigated the effect of the loss of NLGN4 protein on synapse function and on network response to electrical stimulation. The neuroligin family of genes encodes for trans-synaptic proteins involved in synaptic differentiation and maturation. Former studies have reported that both NLGN1-KO and NLGN2-KO give rise to pathologic deregulations of the balance between excitation and inhibition in neural networks, and the presence of loss-of-function mutations in NLGN4 in a small number of autistic patients indicates a possible link between Neuroligin-4 gene and autism. Using extra-cellular stimulation and intra-cellular recordings in acute brain slices, we observed a decreased response of both excitatory and inhibitory response in NLGN4-KO animals, suggesting that NLGN4 is expressed at both excitatory and inhibitory synapses. Furthermore, we observed that the NLGN4-KO resulted in a significantly larger decrease of the excitatory response than of the inhibitory response, thus yielding a hypo-excitable network. The final chapter consists of the characterization of excitatory pathways in acute slices of the somato-sensory cortex. In the first study, we used chemical stimulation to promote the emergence of spontaneous recurrent activity and observed waves of activity originating from the cortical layer five. In the second study, we performed whole-cell patch-clamping of layer five pyramidal cells along with recording local field potentials (LFP). With the systematic layer-wise application of electrical stimulation at various frequencies coupled with LFP recordings, we obtained a frequency-dependent mapping of cortical inputs at all cortical layers at both the cellular and the network levels. Using this data, we developed a virtual framework for performing an analogous mapping in the virtual cortical column of the Blue Brain Project (BBP). This helped us understand the contribution of each cell population in the network response to stimulation. The framework also served as a validation test for the BBP cortical models.

Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-Term Plastic Synapses

Michele Giugliano, Eleni Vasilaki

Recent experimental data from the rodent cerebral cortex and olfactory bulb indicate that specific connectivity motifs are correlated with short-term dynamics of excitatory synaptic transmission. It was observed that neurons with short-term facilitating synapses form predominantly reciprocal pairwise connections, while neurons with short-term depressing synapses form predominantly unidirectional pairwise connections. The cause of these structural differences in excitatory synaptic microcircuits is unknown. We show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP). While the impact of STDP on SD was shown in simultaneous neuronal pair recordings in vitro, the mutual interactions between STDP and SD in large networks are still the subject of intense research. Our approach combines an SD phenomenological model with an STDP model that faithfully captures long-term plasticity dependence on both spike times and frequency. As a proof of concept, we first simulate and analyze recurrent networks of spiking neurons with random initial connection efficacies and where synapses are either all short-term facilitating or all depressing. For identical external inputs to the network, and as a direct consequence of internally generated activity, we find that networks with depressing synapses evolve unidirectional connectivity motifs, while networks with facilitating synapses evolve reciprocal connectivity motifs. We then show that the same results hold for heterogeneous networks, including both facilitating and depressing synapses. This does not contradict a recent theory that proposes that motifs are shaped by external inputs, but rather complements it by examining the role of both the external inputs and the internally generated network activity. Our study highlights the conditions under which SD-STDP might explain the correlation between facilitation and reciprocal connectivity motifs, as well as between depression and unidirectional motifs.

Public Library of Science2014

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same functional unit, the neuron, and develop through the same learning mechanism, synaptic plasticity. It motivates the conjecture of a unifying theory to explain cortical representational learning across sensory modalities. In this thesis we present theories and computational models of learning and optimization in neural networks, postulating functional properties of synaptic plasticity that support the apparent universal learning capacity of cortical networks. In the past decades, a variety of theories and models have been proposed to describe receptive field formation in sensory areas. They include normative models such as sparse coding, and bottom-up models such as spike-timing dependent plasticity. We bring together candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. A surprising result is that our findings are independent of specific details, and allow for robust predictions of the learned receptive fields. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The Hebbian learning theory substantiates that synaptic plasticity can be interpreted as an optimization procedure, implementing stochastic gradient descent. In stochastic gradient descent inputs arrive sequentially, as in sensory streams. However, individual data samples have very little information about the correct learning signal, and it becomes a fundamental problem to know how many samples are required for reliable synaptic changes. Through estimation theory, we develop a novel adaptive learning rate model, that adapts the magnitude of synaptic changes based on the statistics of the learning signal, enabling an optimal use of data samples. Our model has a simple implementation and demonstrates improved learning speed, making this a promising candidate for large artificial neural network applications. The model also makes predictions on how cortical plasticity may modulate synaptic plasticity for optimal learning. The optimal sampling size for reliable learning allows us to estimate optimal learning times for a given model. We apply this theory to derive analytical bounds on times for the optimization of synaptic connections. First, we show this optimization problem to have exponentially many saddle-nodes, which lead to small gradients and slow learning. Second, we show that the number of input synapses to a neuron modulates the magnitude of the initial gradient, determining the duration of learning. Our final result reveals that the learning duration increases supra-linearly with the number of synapses, suggesting an effective limit on synaptic connections and receptive field sizes in developing neural networks.

EPFL2016

Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-Term Plastic Synapses

Michele Giugliano, Eleni Vasilaki

Public Library of Science2014

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

EPFL2016