In vitro neural networks of cortical neurons interfaced to a computer via multichannel microelectrode arrays (MEA) provide a unique paradigm to create a hybrid neural computer. Unfortunately, only rudimentary information about these in vitro network's computational properties or the extent of their abilities are known. To study those properties, a Liquid State Machine (LSM) approach was employed in which the liquid (typically an artificial neural network) was replaced with a living cortical network and the input and readout functions were replaced by the MEA-computer interface. A key requirement of the LSM architecture is that inputs into the liquid state must result in separable outputs based on the liquid's response (separation property). In this paper, high and low frequency multi-site stimulation patterns were applied to the living cortical networks. Two template-based classifiers, one based on Euclidean distance and a second based on a cross-correlation were then applied to measure the separation of the input-output relationship. The result was over a 95% (99.8% when non-stationarity is compensated) input reconstruction accuracy for the high and low frequency patterns, confirming the existence of the separation property in these biological networks.

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Spike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN) model that learns noisy pattern sequences through the use of homeostasis and spike-timing dependent plasticity (STDP). We find that the changes in the synaptic weight vector during learning of patterns of random ensembles are approximately orthogonal in a reduced dimension space when the patterns are constructed to minimize overlap in representations. Using this model, representations of sparse patterns maybe associated through co-activated firing and integrated into ensemble representations. While the model is tolerant to noise, prospective activity, and pattern completion differ in their ability to adapt in the presence of noise. One version of the model is able to demonstrate the recently discovered phenomena of preplay and replay reminiscent of hippocampal-like behaviors.

Many spike timing dependent plasticity (STDP) rules generate a bimodal distribution of synaptic weights because there is no stable equilibrium state. Our approach augments STDP with amplitude dependence providing negative feedback of synaptic weight to plasticity resulting in weights being driven toward stable values and unimodal distributions. The affects of input correlation on synaptic weight are shown using simulated cortical neurons. It was found that pre-and post-synaptic spike trains effect the mean, variance, and skew of the synaptic weight distributions using amplitude and spiketiming dependent plasticity. In addition, multiplicative synaptic modification noise was found to increase the variance of the weight distribution and induce positive skew.