Non-monotonic effects of GABAergic synaptic inputs on neuronal firing.
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2022-06-06
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GABA is generally known as the principal inhibitory neurotransmitter in the nervous system, usually acting by hyperpolarizing membrane potential. However, GABAergic currents sometimes exhibit non-inhibitory effects, depending on the brain region, developmental stage or pathological condition. Here, we investigate the diverse effects of GABA on the firing rate of several single neuron models, using both analytical calculations and numerical simulations. We find that GABAergic synaptic conductance and output firing rate exhibit three qualitatively different regimes as a function of GABA reversal potential, EGABA: monotonically decreasing for sufficiently low EGABA (inhibitory), monotonically increasing for EGABA above firing threshold (excitatory); and a non-monotonic region for intermediate values of EGABA. In the non-monotonic regime, small GABA conductances have an excitatory effect while large GABA conductances show an inhibitory effect. We provide a phase diagram of different GABAergic effects as a function of GABA reversal potential and glutamate conductance. We find that noisy inputs increase the range of EGABA for which the non-monotonic effect can be observed. We also construct a micro-circuit model of striatum to explain observed effects of GABAergic fast spiking interneurons on spiny projection neurons, including non-monotonicity, as well as the heterogeneity of the effects. Our work provides a mechanistic explanation of paradoxical effects of GABAergic synaptic inputs, with implications for understanding the effects of GABA in neural computation and development.
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Abed Zadeh, Aghil, Brandon D Turner, Nicole Calakos and Nicolas Brunel (2022). Non-monotonic effects of GABAergic synaptic inputs on neuronal firing. PLoS computational biology, 18(6). p. e1010226. 10.1371/journal.pcbi.1010226 Retrieved from https://hdl.handle.net/10161/25453.
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Nicole Calakos
We all know that as part of our daily lives we are constantly interacting with our environment - learning, adapting, establishing new memories and habits, and for better or for worse, forgetting as well. At the cellular level, these processes can be encoded by changes in the strength of synaptic transmission between neurons. The process by which neuronal connections change in response to experience is known as “synaptic plasticity” and this process is a major interest of our laboratory. Our goals are to understand the molecular mechanisms for synaptic plasticity and identify when these processes have gone awry in neurological diseases. In doing so, we will establish the necessary framework to target these processes for therapeutic interventions; potentially identifying novel and improved treatment options.
We focus these interests on the striatal circuitry of the basal ganglia. The striatum is a key entry point for cortical information into the basal ganglia. The basal ganglia are involved in a wide variety of behaviors because they are critical for our movement, including the learning of motor routines and when to call them into action. Disorders in this process have wide ranging manifestations and substantially contribute to diseases like Parkinson’s disease, OCD, dystonia, Tourette’s and addictive behavior.
Our lab has pioneered a number of molecular and circuit-cracking methodologies that have provided new views into the workings of the striatal circuitry and its plasticity rules. Our lab has deep expertise in electrophysiology and optical physiology (two photon calcium imaging) and state-of-the-art molecular genetic mouse modeling techniques. Yet, our insights are further amplified by the highly collaborative approach we have with colleagues at Duke and beyond.
To get a better view of how pathway balance in basal ganglia circuitry may be affected, our lab has developed tools and approaches that make it possible to study the function of striatal medium spiny neurons in the direct and indirect pathways simultaneously in living tissue (Shuen et al., 2008; Ade et al., 2011, O’Hare and Ade et al., 2016). We use them to identify functional differences between these two types of medium spiny neurons and their role in normal adaptive plasticity and disease processes.
In habit, we identified circuit predictors of behavior. These include some classic expectations for mechanisms of plasticity such as increased firing activity, but also some surprises, like finding shifts in the timing of firing between these two cell types (O’Hare and Ade et al., 2016) and that a key coordinator is an interneuron (O’Hare et al., eLife 2017).
In disease settings, we leverage the Sapap3 KO model to understand what causes repetitive, self-injurious behavior and anxiety-like behaviors (“OCD-like”). We find a central role for striatal group 1 metabotropic glutamate receptor overactivity (Ade et al., Biol. Psych. 2016). By developing a unique high-throughput screening assay for an inherited cause of the movement disorder, dystonia, we came to recognize that multiple forms of this disease were united by a common defect in signaling by the proteostasis pathway known as the “integrated stress response” or ISR (also eIF2alpha phosphorylation) (Rittiner and Caffall et al., Neuron 2016).
Currently, ISR research in the lab has markedly expanded to address both its basic mechanisms (Helseth and Hernandez-Martinez et al., Science 2021) and its translational potential (Caffall et al., Sci. Transl. Med. 2021) for dystonia, Parkinson’s and other brain diseases.
Nicolas Brunel
We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods from
statistical physics, we have shown recently that the synaptic
connectivity of a network that maximizes storage capacity reproduces
two key experimentally observed features: low connection probability
and strong overrepresentation of bidirectionnally connected pairs of
neurons. We have also inferred `synaptic plasticity rules' (a
mathematical description of how synaptic strength depends on the
activity of pre and post-synaptic neurons) from data, and shown that
networks endowed with a plasticity rule inferred from data have a
storage capacity that is close to the optimal bound.
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