The malleable brain: plasticity of neural circuits and behavior: A review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation (LTP) and long-term depression (LTD) respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by LTP and LTD, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity.Fil: Schaefer, Natascha. University of Wuerzburg; AlemaniaFil: Rotermund, Carola. University of Tuebingen; AlemaniaFil: Blumrich, Eva Maria. Universitat Bremen; AlemaniaFil: Lourenco, Mychael V.. Universidade Federal do Rio de Janeiro; BrasilFil: Joshi, Pooja. Robert Debre Hospital; FranciaFil: Hegemann, Regina U.. University of Otago; Nueva ZelandaFil: Jamwal, Sumit. ISF College of Pharmacy; IndiaFil: Ali, Nilufar. Augusta University; Estados UnidosFil: García Romero, Ezra Michelet. Universidad Veracruzana; MéxicoFil: Sharma, Sorabh. Birla Institute of Technology and Science; IndiaFil: Ghosh, Shampa. Indian Council of Medical Research; IndiaFil: Sinha, Jitendra K.. Indian Council of Medical Research; IndiaFil: Loke, Hannah. Hudson Institute of Medical Research; AustraliaFil: Jain, Vishal. Defence Institute of Physiology and Allied Sciences; IndiaFil: Lepeta, Katarzyna. Polish Academy of Sciences; ArgentinaFil: Salamian, Ahmad. Polish Academy of Sciences; ArgentinaFil: Sharma, Mahima. Polish Academy of Sciences; ArgentinaFil: Golpich, Mojtaba. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Nawrotek, Katarzyna. University Of Lodz; ArgentinaFil: Paid, Ramesh K.. Indian Institute of Chemical Biology; IndiaFil: Shahidzadeh, Sheila M.. Syracuse University; Estados UnidosFil: Piermartiri, Tetsade. Universidade Federal de Santa Catarina; BrasilFil: Amini, Elham. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Pastor, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Wilson, Yvette. University of Melbourne; AustraliaFil: Adeniyi, Philip A.. Afe Babalola University; NigeriaFil: Datusalia, Ashok K.. National Brain Research Centre; IndiaFil: Vafadari, Benham. Polish Academy of Sciences; ArgentinaFil: Saini, Vedangana. University of Nebraska; Estados UnidosFil: Suárez Pozos, Edna. Instituto Politécnico Nacional; MéxicoFil: Kushwah, Neetu. Defence Institute of Physiology and Allied Sciences; IndiaFil: Fontanet, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Turner, Anthony J.. University of Leeds; Reino Unid

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning

Interplay between subthreshold oscillations and depressing synapses in single neurons

Latorre R, Torres JJ, Varona P (2016) Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons. PLoS ONE 11(1): e0145830. doi:10.1371/journal.pone.0145830In 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.This work was supported by MINECO TIN2012-30883 (RL and PV) and FIS2013-43201-P (JJT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). What are the neuronal mechanisms responsible for these changes and how does targeted stimulation by a BBCI shape population-level synaptic connectivity? The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites are strengthened for spike-stimulus delays consistent with experimentally derived spike time dependent plasticity (STDP) rules. However, the relationship between STDP mechanisms at the level of networks, and their modification with neural implants remains poorly understood. Using our model, we successfully reproduces key experimental results and use analytical derivations, along with novel experimental data. We then derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered stimulation in different regimes of cortical activity.Comment: 35 pages, 9 figure