Effects of fast presynaptic noise in attractor neural networks

How Memory Conforms to Brain Development

Nature exhibits countless examples of adaptive networks, whose topology evolves constantly coupled with the activity due to its function. The brain is an illustrative example of a system in which a dynamic complex network develops by the generation and pruning of synaptic contacts between neurons while memories are acquired and consolidated. Here, we consider a recently proposed brain developing model to study how mechanisms responsible for the evolution of brain structure affect and are affected by memory storage processes. Following recent experimental observations, we assume that the basic rules for adding and removing synapses depend on local synaptic currents at the respective neurons in addition to global mechanisms depending on the mean connectivity. In this way a feedback loop between "form" and "function" spontaneously emerges that influences the ability of the system to optimally store and retrieve sensory information in patterns of brain activity or memories. In particular, we report here that, as a consequence of such a feedback-loop, oscillations in the activity of the system among the memorized patterns can occur, depending on parameters, reminding mind dynamical processes. Such oscillations have their origin in the destabilization of memory attractors due to the pruning dynamics, which induces a kind of structural disorder or noise in the system at a long-term scale. This constantly modifies the synaptic disorder induced by the interference among the many patterns of activity memorized in the system. Such new intriguing oscillatory behavior is to be associated only to long-term synaptic mechanisms during the network evolution dynamics, and it does not depend on short-term synaptic processes, as assumed in other studies, that are not present in our model.

Concurrence of form and function in developing networks and its role in synaptic pruning

A fundamental question in neuroscience is how structure and function of neural systems are related. We study this interplay by combining a familiar auto-associative neural network with an evolving mechanism for the birth and death of synapses. A feedback loop then arises leading to two qualitatively different types of behaviour. In one, the network structure becomes heterogeneous and dissasortative, and the system displays good memory performance; furthermore, the structure is optimised for the particular memory patterns stored during the process. In the other, the structure remains homogeneous and incapable of pattern retrieval. These findings provide an inspiring picture of brain structure and dynamics that is compatible with experimental results on early brain development, and may help to explain synaptic pruning. Other evolving networks—such as those of protein interactions—might share the basic ingredients for this feedback loop and other questions, and indeed many of their structural features are as predicted by our model.

How structure and function coevolve in developing brains is little understood. Here, the authors study a coupled model of network development and memory, and find that due to the feedback networks with some initial memory capacity evolve into heterogeneous structures with high memory performance.

Latching dynamics in neural networks with synaptic depression

Prediction is the ability of the brain to quickly activate a target concept in response to a related stimulus (prime). Experiments point to the existence of an overlap between the populations of the neurons coding for different stimuli, and other experiments show that prime-target relations arise in the process of long term memory formation. The classical modelling paradigm is that long term memories correspond to stable steady states of a Hopfield network with Hebbian connectivity. Experiments show that short term synaptic depression plays an important role in the processing of memories. This leads naturally to a computational model of priming, called latching dynamics; a stable state (prime) can become unstable and the system may converge to another transiently stable steady state (target). Hopfield network models of latching dynamics have been studied by means of numerical simulation, however the conditions for the existence of this dynamics have not been elucidated. In this work we use a combination of analytic and numerical approaches to confirm that latching dynamics can exist in the context of a symmetric Hebbian learning rule, however lacks robustness and imposes a number of biologically unrealistic restrictions on the model. In particular our work shows that the symmetry of the Hebbian rule is not an obstruction to the existence of latching dynamics, however fine tuning of the parameters of the model is needed.

Bifurcation Analysis on Phase-Amplitude Cross-Frequency Coupling in Neural Networks with Dynamic Synapses

We investigate a discrete-time network model composed of excitatory and inhibitory neurons and dynamic synapses with the aim at revealing dynamical properties behind oscillatory phenomena possibly related to brain functions. We use a stochastic neural network model to derive the corresponding macroscopic mean field dynamics, and subsequently analyze the dynamical properties of the network. In addition to slow and fast oscillations arising from excitatory and inhibitory networks, respectively, we show that the interaction between these two networks generates phase-amplitude cross-frequency coupling (CFC), in which multiple different frequency components coexist and the amplitude of the fast oscillation is modulated by the phase of the slow oscillation. Furthermore, we clarify the detailed properties of the oscillatory phenomena by applying the bifurcation analysis to the mean field model, and accordingly show that the intermittent and the continuous CFCs can be characterized by an aperiodic orbit on a closed curve and one on a torus, respectively. These two CFC modes switch depending on the coupling strength from the excitatory to inhibitory networks, via the saddle-node cycle bifurcation of a one-dimensional torus in map (MT1SNC), and may be associated with the function of multi-item representation. We believe that the present model might have potential for studying possible functional roles of phase-amplitude CFC in the cerebral cortex.

A divide-and-conquer strategy in tumor sampling enhances detection of intratumor heterogeneity in routine pathology: A modeling approach in clear cell renal cell carcinoma

Intratumor heterogeneity (ITH) is an inherent process in cancer development which follows for most of the cases a branched pattern of evolution, with different cell clones evolving independently in space and time across different areas of the same tumor. The determination of ITH (in both spatial and temporal domains) is nowadays critical to enhance patient treatment and prognosis. Clear cell renal cell carcinoma (CCRCC) provides a good example of ITH. Sometimes the tumor is too big to be totally analyzed for ITH detection and pathologists decide which parts must be sampled for the analysis. For such a purpose, pathologists follow internationally accepted protocols. In light of the latest findings, however, current sampling protocols seem to be insufficient for detecting ITH with significant reliability. The arrival of new targeted therapies, some of them providing promising alternatives to improve patient survival, pushes the pathologist to obtain a truly representative sampling of tumor diversity in routine practice. How large this sampling must be and how this must be performed are unanswered questions so far.  Here we present a very simple method for tumor sampling that enhances ITH detection without increasing costs. This method follows a divide-and-conquer (DAC) strategy, that is, rather than sampling a small number of large-size tumor-pieces as the routine protocol (RP) advises, we suggest sampling many small-size pieces along the tumor. We performed a computational modeling approach to show that the usefulness of the DAC strategy is twofold: first, we show that DAC outperforms RP with similar laboratory costs, and second, DAC is capable of performing similar to total tumor sampling (TTS) but, very remarkably, at a much lower cost. We thus provide new light to push forward a shift in the paradigm about how pathologists should sample tumors for achieving efficient ITH detection.

A divide-and-conquer strategy in tumor sampling enhances detection of intratumor heterogeneity in routine pathology: A modeling approach in clear cell renal cell carcinoma

Intratumor heterogeneity (ITH) is an inherent process in cancer development which follows for most of the cases a branched pattern of evolution, with different cell clones evolving independently in space and time across different areas of the same tumor. The determination of ITH (in both spatial and temporal domains) is nowadays critical to enhance patient treatment and prognosis. Clear cell renal cell carcinoma (CCRCC) provides a good example of ITH. Sometimes the tumor is too big to be totally analyzed for ITH detection and pathologists decide which parts must be sampled for the analysis. For such a purpose, pathologists follow internationally accepted protocols. In light of the latest findings, however, current sampling protocols seem to be insufficient for detecting ITH with significant reliability. The arrival of new targeted therapies, some of them providing promising alternatives to improve patient survival, pushes the pathologist to obtain a truly representative sampling of tumor diversity in routine practice. How large this sampling must be and how this must be performed are unanswered questions so far.  Here we present a very simple method for tumor sampling that enhances ITH detection without increasing costs. This method follows a divide-and-conquer (DAC) strategy, that is, rather than sampling a small number of large-size tumor-pieces as the routine protocol (RP) advises, we suggest sampling many small-size pieces along the tumor. We performed a computational modeling approach to show that the usefulness of the DAC strategy is twofold: first, we show that DAC outperforms RP with similar laboratory costs, and second, DAC is capable of performing similar to total tumor sampling (TTS) but, very remarkably, at a much lower cost. We thus provide new light to push forward a shift in the paradigm about how pathologists should sample tumors for achieving efficient ITH detection.

Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons

In this paper we analyze the interplay between the subthreshold oscillations of a single neuron conductance-based model and the short-term plasticity of a dynamic synapse with a depressing mechanism. In previous research, the computational properties of subthreshold oscillations and dynamic synapses have been studied separately. Our results show that dynamic synapses can influence different aspects of the dynamics of neuronal subthreshold oscillations. Factors such as maximum hyperpolarization level, oscillation amplitude and frequency or the resulting firing threshold are modulated by synaptic depression, which can even make subthreshold oscillations disappear. This influence reshapes the postsynaptic neuron’s resonant properties arising from subthreshold oscillations and leads to specific input/output relations. We also study the neuron’s response to another simultaneous input in the context of this modulation, and show a distinct contextual processing as a function of the depression, in particular for detection of signals through weak synapses. Intrinsic oscillations dynamics can be combined with the characteristic time scale of the modulatory input received by a dynamic synapse to build cost-effective cell/channel-specific information discrimination mechanisms, beyond simple resonances. In this regard, we discuss the functional implications of synaptic depression modulation on intrinsic subthreshold dynamics.

Emerging phenomena in neural networks with dynamic synapses and their computational implications

In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilization of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behavior can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses) and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

Stochastic resonance crossovers in complex networks

Here we numerically study the emergence of stochastic resonance as a mild phenomenon and how this transforms into an amazing enhancement of the signal-to-noise ratio at several levels of a disturbing ambient noise. The setting is a cooperative, interacting complex system modelled as an Ising-Hopfield network in which the intensity of mutual interactions or “synapses” varies with time in such a way that it accounts for, e.g., a kind of fatigue reported to occur in the cortex. This induces nonequilibrium phase transitions whose rising comes associated to various mechanisms producing two types of resonance. The model thus clarifies the details of the signal transmission and the causes of correlation among noise and signal. We also describe short-time persistent memory states, and conclude on the limited relevance of the network wiring topology. Our results, in qualitative agreement with the observation of excellent transmission of weak signals in the brain when competing with both intrinsic and external noise, are expected to be of wide validity and may have technological application. We also present here a first contact between the model behavior and psychotechnical data.

Irregular dynamics in up and down cortical states

Complex coherent dynamics is present in a wide variety of neural systems. A typical example is the voltage transitions between up and down states observed in cortical areas in the brain. In this work, we study this phenomenon via a biologically motivated stochastic model of up and down transitions. The model is constituted by a simple bistable rate dynamics, where the synaptic current is modulated by short-term synaptic processes which introduce stochasticity and temporal correlations. A complete analysis of our model, both with mean-field approaches and numerical simulations, shows the appearance of complex transitions between high (up) and low (down) neural activity states, driven by the synaptic noise, with permanence times in the up state distributed according to a power-law. We show that the experimentally observed large fluctuation in up and down permanence times can be explained as the result of sufficiently noisy dynamical synapses with sufficiently large recovery times. Static synapses cannot account for this behavior, nor can dynamical synapses in the absence of noise.