Neural Models of Temporally Organized Behaviors: Handwriting Production and Working Memory

Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309

Interaural time difference processing in the mammalian medial superior olive

The dominant cue for localization of low-frequency sounds are microsecond differences in the time-of-arrival of sounds at the two ears [interaural time difference (ITD)]. In mammals, ITD sensitivity is established in the medial superior olive (MSO) by coincidence detection of excitatory inputs from both ears. Hence the relative delay of the binaural inputs is crucial for adjusting ITD sensitivity in MSO cells. How these delays are constructed is, however, still unknown. Specifically, the question of whether inhibitory inputs are involved in timing the net excitation in MSO cells, and if so how, is controversial. These inhibitory inputs derive from the nuclei of the trapezoid body, which have physiological and structural specializations for high-fidelity temporal transmission, raising the possibility that well timed inhibition is involved in tuning ITD sensitivity. Here, we present physiological and pharmacological data from in vivo extracellular MSO recordings in anesthetized gerbils. Reversible blockade of synaptic inhibition by iontophoretic application of the glycine antagonist strychnine increased firing rates and significantly shifted ITD sensitivity of MSO neurons. This indicates that glycinergic inhibition plays a major role in tuning the delays of binaural excitation. We also tonically applied glycine, which lowered firing rates but also shifted ITD sensitivity in a way analogous to strychnine. Hence tonic glycine application experimentally decoupled the effect of inhibition from the timing of its inputs. We conclude that, for proper ITD processing, not only is inhibition necessary, but it must also be precisely timed

SuperSpike: Supervised learning in multi-layer spiking neural networks

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in-vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in-silico. Here we revisit the problem of supervised learning in temporally coding multi-layer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three factor learning rule capable of training multi-layer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike-time patterns

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

Phasic firing and coincidence detection by subthreshold negative feedback: divisive or subtractive or, better, both

Phasic neurons typically fire only for a fast-rising input, say at the onset of a step current, but not for steady or slow inputs, a property associated with type III excitability. Phasic neurons can show extraordinary temporal precision for phase locking and coincidence detection. Exemplars are found in the auditory brain stem where precise timing is used in sound localization. Phasicness at the cellular level arises from a dynamic, voltage-gated, negative feedback that can be recruited subthreshold, preventing the neuron from reaching spike threshold if the voltage does not rise fast enough. We consider two mechanisms for phasicness: a low threshold potassium current (subtractive mechanism) and a sodium current with subthreshold inactivation (divisive mechanism). We develop and analyze three reduced models with either divisive or subtractive mechanisms or both to gain insight into the dynamical mechanisms for the potentially high temporal precision of type III-excitable neurons. We compare their firing properties and performance for a range of stimuli. The models have characteristic non-monotonic input-output relations, firing rate vs. input intensity, for either stochastic current injection or Poisson-timed excitatory synaptic conductance trains. We assess performance according to precision of phase-locking and coincidence detection by the models' responses to repetitive packets of unitary excitatory synaptic inputs with more or less temporal coherence. We find that each mechanism contributes features but best performance is attained if both are present. The subtractive mechanism confers extraordinary precision for phase locking and coincidence detection but only within a restricted parameter range when the divisive mechanism of sodium inactivation is inoperative. The divisive mechanism guarantees robustness of phasic properties, without compromising excitability, although with somewhat less precision. Finally, we demonstrate that brief transient inhibition if properly timed can enhance the reliability of firing.Postprint (published version