Mathew Jones

Information in the brain is encoded in the electrical activity of neurons that communicate with each other using chemical synapses. These synapses can be excitatory or inhibitory. Recent work from many laboratories has revealed that, rather than merely suppressing neuronal firing, inhibitory synapses are intimately involved in shaping the flow and timing of information in neural circuits.

My research attempts to understand the mechanisms by which biophysical events at inhibitory synapses give rise to the computational roles that inhibition plays in neural circuitry. This topic requires study on several levels including the molecular function of GABA-A receptors (i.e., the postsynaptic ion channels), the time course and spread of GABA in the synapse and the connectivity and timing of signals across many excitatory and inhibitory neurons within a circuit.

I use a combination of patch-clamp electrophysiology, rapid solution exchange methods and computer modeling to address these issues in cultured neurons, brain slices and heterologous expression systems. Some of my previous work in this area and my current research directions are outlined below.

GABAA Receptor Biophysics

1. Gating
   After a brief pulse of GABA, such as might occur at the synapse, GABA-A receptors spend much of their time in desensitized (i.e., bound but closed) states. Although their existence has been known for a long time, the significance of these states was poorly understood. My colleagues and I showed that they are responsible for prolonging the IPSC by “buffering” the channel in the GABA-bound conformation. Desensitized states also make inhibition dependent on the recent history of transmission at each synapse (Jones & Westbrook, 1995, 1996; Jones et al., 1999b; Overstreet et al., 1999c).
2. Regulation
   People often say that changes in the subunit composition of GABA-A receptors may be an important developmental mechanism for tailoring synapses for particular tasks. In contrast, we found that moment-to-moment intracellular regulation of these receptors can bring about changes in the postsynaptic response that are just as large as those caused by subunit differences between synapses. This implies that a neuron may rapidly regulate its own inhibition, potentially one synapse at a time, and in response to the constantly changing context of local network activity (Jones & Westbrook, 1997).
3. Coupling of GABA Binding to Gating
   GABA binds to the GABA-A receptor much more slowly than one would predict based on models of ligands colliding with a rigid binding site through diffusion alone. There is also a striking correlation between agonist length and affinity. These data suggested that an energy-requiring event precedes or accompanies GABA binding, and we were able to measure this energy. To account for this energy requirement, we developed and tested a very simple molecular model of the GABA binding site in which the receptor must undergo a conformational movement before it can bind GABA. This movement appears to be the first step in transducing the energy of GABA binding into channel opening. More recent experiments comparing agonist and antagonists kinetics further support our conclusions. Future experiments will be aimed at a comprehensive understanding of how GABA-A receptor structure produces its function, with particular emphasis on the kinetics of channel opening and desensitization that underlie the inhibitory postsynaptic current (Jones et al., 1998, 1999a).

The Time Course and Spatial Spread of GABA at the Synapse
A slightly “higher” level in the spectrum of inhibitory mechanisms involves questions like: “What happens to the GABA that gets released from a synaptic vesicle? Does it get taken back up or can it diffuse over to receptors at neighboring synapses? What happens to those receptors if or when it gets there?”

We have begun to answer these questions at synapses on granule cells of the dentate gyrus by taking advantage of the fact that GABA-A receptor antagonists that unbind very rapidly can be replaced at their binding sites by the GABA released into the synapse. This introduces a late component into the rising phase of inhibitory postsynaptic currents. We therefore used measurements of the IPSC rising phase in the presence of antagonists to show that the GABA released from a single vesicle reaches concentrations in the millimolar range, but decays to low micromolar levels within only a few hundred microseconds.

We have incorporated our physiological data, as well as published measurements of synaptic geometry and GABA transporter kinetics, into Monte Carlo models of GABA diffusion at synapses in an attempt to understand the ability of GABA to act as a signal between neighboring synapses (Jones et al., 1999b).

Inhibition and Information Processing by Neural Circuits
The traditional view of inhibition is that it acts like an activity-sensitive clutch, to keep neural excitability under control. However, recent work has revealed many more subtle and computationally flexible features. For example, a single GABAergic interneuron is capable of synchronizing the firing of hundreds of target cells, providing just one illustration of how inhibition can sculpt the flow and timing of “information” in neural circuits.

However, the word “information” is still rather poorly defined in this context. A common approach is to ask what aspects of a behavioral stimulus are “encoded” by neural activity, generally using extracellular recordings in vivo. In such experiments, spikes can be observed but the sources and nature of the synaptic drive are very difficult to isolate.

An alternative approach, which I employ, is to begin with a well-isolated circuit using brain slices which allow for simultaneous high resolution recordings and control at multiple pre- and postsynaptic sites in the circuit. One can then ask what operations the circuit performs on signals that are fed into it, and attempt to identify the biophysical mechanisms that allow it to perform its function as a signal processing device.

With quantitative knowledge of the input and output of the circuit, one is in a much better position to precisely define “information” and characterize its flow within the circuit using the well established methods of Signal Processing and Information Theory. I am currently using these methods to investigate signal processing in the dentate gyrus.

Full list of publications can be found here.