Neurons and networks organizing and sequencing memories

Barlow versus Hebb: When is it time to abandon the notion of feature detectors and adopt the cell assembly as the unit of cognition?

Research on how information is encoded by the brain is largely based on studies of feature detector properties of single neurons, but considerable new data shows that single neurons in many brain areas have mixed selectivity for multiple features and change their tuning properties across realistic information processing situations. Here I consider new approaches that explore cell assemblies as the units of information processing and how these approaches are revealing the structure and organization of neural representations in perception and cognition.

Random neuronal ensembles can inherently do context dependent coarse conjunctive encoding of input stimulus without any specific training

Conjunctive encoding of inputs has been hypothesized to be a key feature in the computational capabilities of the brain. This has been inferred based on behavioral studies and electrophysiological recording from animals. In this report, we show that random neuronal ensembles grown on multi-electrode array perform a coarse-conjunctive encoding for a sequence of inputs with the first input setting the context. Such an encoding scheme creates similar yet unique population codes at the output of the ensemble, for related input sequences, which can then be decoded via a simple perceptron and hence a single STDP neuron layer. The random neuronal ensembles allow for pattern generalization and novel sequence classification without needing any specific learning or training of the ensemble. Such a representation of the inputs as population codes of neuronal ensemble outputs, has inherent redundancy and is suitable for further decoding via even probabilistic/random connections to subsequent neuronal layers. We reproduce this behavior in a mathematical model to show that a random neuronal network with a mix of excitatory and inhibitory neurons and sufficient connectivity creates similar coarse-conjunctive encoding of input sequences.