Code-specific learning rules improve action selection by populations of spiking neurons

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Synaptic regulation of spike firing in interneurons of the striatum in vivo

The striatum, the major input nucleus of the basal ganglia, is numerically dominated by a single class of principal neurons, the GABAergic spiny projection neuron (SPN) that has been extensively studied both in vitro and in vivo. Much less is known about the sparsely distributed interneurons, principally the cholinergic interneuron (CIN) and the GABAergic fast-spiking interneuron (FSI). Here, we summarize results from two recent studies on these interneurons where we used in vivo intracellular recording techniques in urethane-anaesthetized rats (Schulz et al., J Neurosci 31[31], 2011; J Physiol, in press). Interneurons were identified by their characteristic responses to intracellular current steps and spike waveforms. Spontaneous spiking contained a high proportion (~45%) of short inter-spike intervals (ISI) of <30 ms in FSIs, but virtually none in CINs. Spiking patterns in CINs covered a broad spectrum ranging from regular tonic spiking to phasic activity despite very similar unimodal membrane potential distributions across neurons. In general, phasic spiking activity occurred in phase with the slow ECoG waves, whereas CINs exhibiting tonic regular spiking were little affected by afferent network activity. In contrast, FSIs exhibited transitions between Down and Up states very similar to SPNs. Compared to SPNs, the FSI Up state membrane potential was noisier and power spectra exhibited significantly larger power at frequencies in the gamma range (55-95 Hz). Cortical-evoked inputs had faster dynamics in FSIs than SPNs and the membrane potential preceding spontaneous spike discharge exhibited short and steep trajectories, suggesting that fast input components controlled spike output in FSIs. Intrinsic resonance mechanisms may have further enhanced the sensitivity of FSIs to fast oscillatory inputs. Induction of an activated ECoG state by local ejection of bicuculline into the superior colliculus, resulted in increased spike frequency in both interneuron classes without changing the overall distribution of ISIs. This manipulation also made CINs responsive to a light flashed into the contralateral eye. Typically, the response consisted of an excitation at short latency followed by a pause in spike firing, via an underlying depolarization-hyperpolarization membrane sequence. These results highlight the differential sensitivity of striatal interneurons to afferent synaptic signals and support a model where CINs modulate the striatal network in response to salient sensory bottom-up signals, while FSIs serve gating of top-down signals from the cortex during action selection and reward-related learning.

Tracking the subprocesses of decision-based action in the human frontal lobes

Situationally adaptive behavior relies on the identification of relevant target stimuli, the evaluation of these with respect to the current context and the selection of an appropriate action. We used functional magnetic resonance imaging (fMRI) to disentangle the neural networks underlying these processes within a single task. Our results show that activation of mid-ventrolateral prefrontal cortex (PFC) reflects the perceived presence of a target stimulus regardless of context, whereas context-appropriate evaluation is subserved by mid-dorsolateral PFC. Enhancing demands on response selection by means of response conflict activated a network of regions, all of which are directly connected to motor areas. On the midline, rostral anterior paracingulate cortex was found to link target detection and response selection by monitoring for the presence of behaviorally significant conditions. In summary, we provide new evidence for process-specific functional dissociations in the frontal lobes. In target-centered processing, target detection in the VLPFC is separable from contextual evaluation in the DLPFC. Response-centered processing in motor-associated regions occurs partly in parallel to these processes, which may enhance behavioral efficiency, but it may also lead to reaction time increases when an irrelevant response tendency is elicited.

Habitat selection of three cryptic Plecotus bat species in the European Alps reveals contrasting implications for conservation

Assessing the ecological requirements of species coexisting within a community is an essential requisite for developing sound conservation action. A particularly interesting question is what mechanisms govern the stable coexistence of cryptic species within a community, i.e. species that are almost impossible to distinguish. Resource partitioning theory predicts that cryptic species, like other sympatric taxa, will occupy distinct ecological niches. This prediction is widely inferred from eco-morphological studies. A new cryptic long-eared bat species, Plecotus macrobullaris, has been recently discovered in the complex of two other species present in the European Alps, with even evidence for a few mixed colonies. This discovery poses challenges to bat ecologists concerned with planning conservation measures beyond roost protection. We therefore tested whether foraging habitat segregation occurred among the three cryptic Plecotus bat species in Switzerland by radiotracking 24 breeding female bats (8 of each species). We compared habitat features at locations visited by a bat versus random locations within individual home ranges, applying mixed effects logistic regression. Distinct, species-specific habitat preferences were revealed. P. auritus foraged mostly within traditional orchards in roost vicinity, with a marked preference for habitat heterogeneity. P. austriacus foraged up to 4.7 km from the roost, selecting mostly fruit tree plantations, hedges and tree lines. P. macrobullaris preferred patchy deciduous and mixed forests with high vertical heterogeneity in a grassland dominated-matrix. These species-specific habitat preferences should inform future conservation programmes. They highlight the possible need of distinct conservation measures for species that look very much alike.