Past events

I will summarize recent theoretical and experimental work that shows that similar circuit outputs can be produced with highly variable circuit parameters. This work argues that the nervous system of each healthy individual has found a set of different solutions that give “good enough” circuit performance. Studies using the rhythmic central pattern generating networks in the crustacean stomatogastric nervous system argue that synaptic and intrinsic currents can vary far more than the output of the circuit in which they are found. These data have significant implications for the mechanisms that maintain stable function over the animal’s lifetime, and for the kinds of changes that allow the nervous system to recover function after injury. In this kind of complex system, merely collecting mean data from many individuals can lead to significant errors, and it becomes important to measure as many individual network parameters in each individual as possible. Multiple solutions in the population provide a substrate for evolutionary change in response to environmental perturbations.

The plasticity of the brain plays a key role in shaping our behavior, learning and memory. It is well known that plasticity is associated with alteration in synaptic strength and efficacy. Some of these effects correlate with changes in the levels of synaptic proteins. However, the implications of genetic alteration in synaptic proteins on the network activity of neurons are not known. We examine the effect of DOC2B, a synaptic neuronal Ca2+ sensor that is known for its ability to enhance synaptic transmission, on neuronal network activity. For that purpose we use MicroElectrode Array (MEA) technology to simultaneously record action potentials from multiple neurons in ex vivo neuronal network. Networks grown on MEA plates exhibit a repeated pattern of synchronized network-wide spiking activity (network burst) separated by periods of reduced activity. At the single-neuron level, DOC2B increased the frequency of spontaneous neurotransmitter release. However, its effect at the network level was restricted to the network bursts and was reflected as an increase in the number of spikes and the number of active neurons throughout the network burst, while surprisingly there was no effect on inter-burst spiking activity. In addition, DOC2B enhanced the number of full-blown network bursts, suggesting an impact on the input/output ratio of synaptic transmission. Further analysis suggested DOC2B’s activity was augmented during neuronal bursts and enhanced spontaneous and asynchronous release. This can increase the neuron’s sensitivity to incoming EPSPs and the number of spikes in the network burst. Additionally, our experiments support the hypothesis that DOC2B efficiently enhances synaptic refilling. Hence, this work shows that the changes at the network level complemented our knowledge on the cellular activity of DOC2B and suggests a role for DOC2B in shaping the firing properties of highly active neuronal networks.

How do people monitor the correctness of their answers and judgments? A self-consistency model is proposed for the basis of confidence judgments and their accuracy. The model assumes that the process underlying subjective confidence in general-knowledge questions and perceptual judgments has much in common with that underlying statistical inference about the outside world. Participants behave like intuitive statisticians who attempt to reach a conclusion about a population on the basis of a small sample of observations. Subjective confidence is based on the sampling of clues from memory, and represent an assessment of the likelihood that a new sample will yield the same decision. Results consistent with the model were obtained across several two-alternative forced-choice tasks. The model explains some of the basic observations about subjective confidence and generates new predictions.