Functional Autapses Form in Striatal Parvalbumin Interneurons but not Medium Spiny Projection Neurons.

Autapses selectively form in specific cell types in many brain regions. Previous studies have also found putative autapses in principal spiny projection neurons (SPNs) in the striatum. However, it remains unclear whether these neurons indeed form physiologically functional autapses. We applied whole-cell recording in striatal slices and identified autaptic cells by the occurrence of prolonged asynchronous release (AR) of neurotransmitters after bursts of high-frequency action potentials (APs). Surprisingly, we found no autaptic AR in SPNs, even in the presence of Sr2+. However, robust autaptic AR was recorded in parvalbumin (PV)-expressing neurons. The autaptic responses were mediated by GABAA receptors and their strength was dependent on AP frequency and number. Further computer simulations suggest that autapses regulate spiking activity in PV cells by providing self-inhibition and thus shape network oscillations. Together, our results indicate that PV neurons, but not SPNs, form functional autapses, which may play important roles in striatal functions.

Adaptive neurons compute confidence in a decision network.

Humans and many animals have the ability to assess the confidence of their decisions. However, little is known about the underlying neural substrate and mechanism. In this study we propose a computational model consisting of a group of 'confidence neurons' with adaptation, which are able to assess the confidence of decisions by detecting the slope of ramping activities of decision neurons. The simulated activities of 'confidence neurons' in our simple model capture the typical features of confidence observed in humans and animals experiments. Our results indicate that confidence could be online formed along with the decision formation, and the adaptation properties could be used to monitor the formation of confidence during the decision making.

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation.

Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural encoding. We believe that our study shed lights on the mechanism underlying the efficient neural information processing via adaptation.