A generic model of oscillating cortex, which assumes "minimal" coupling justified by known anatomy, is shown to function as an associative memory,using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long range excitatofy connections. Using a local Hebb-like learning rule for primary and higher order synapses at the ends of the long range connections, the system learns to store the kinds of oscillation amplitudepatterns observed in olfactory and visual cortex. This rule is derived from a more general "projection algorithm" for recurrent analog networks, that analytically guarantees content addressable memory storage of continuous periodic sequences - capacity: N/2 Fourier components for an N node network - no "spurious" attractors. 1 Introduction This is a sketch of recent results stemming from work which is discussed completely in [1, 2, 3]. Patterns of 40 to 80 hz oscillation have been observed in the large scale activity of olfactory cortex  and visual neocortex , and shown to predict the olfactory and visual pattern recognition responses of a trained animal.
Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms have allowed reliable learning and recall of an exponential number of patterns. Although these designs correct external errors in recall, they assume neurons that compute noiselessly, in contrast to the highly variable neurons in hippocampus and olfactory cortex. Here we consider associative memories with noisy internal computations and analytically characterize performance. As long as the internal noise level is below a specified threshold, the error probability in the recall phase can be made exceedingly small. More surprisingly, we show that internal noise actually improves the performance of the recall phase. Computational experiments lend additional support to our theoretical analysis. This work suggests a functional benefit to noisy neurons in biological neuronal networks.
Hippocampal CA3 is crucial for long-term associative memory. CA3 has heavily recurrent connectivity, and memories are thought to be stored as the pattern of synaptic weights in CA3. However, despite the well-known importance of the hippocampus for memory storage and retrieval, up until now, spiking neural network models of this crucial function only exist as small-scale, proof-of-concept models. Our work is the first to develop a biologically plausible spiking neural network model of hippocampus memory encoding and retrieval, with over two orders-of-magnitude as many neurons in CA3 as previous models. It is also the first to investigate the effect of neurogenesis in the dentate gyrus on a spiking model of CA3. Using this model, we first show that a recently developed plasticity rule is crucial for good encoding and retrieval. Then, we show how neural properties related to neurogenesis and neuronal death enhance storage and retrieval of associative memories in the recurrently connected CA3.
Researchers are developing an app which could help to prevent suicides by flagging those most at risk. Using a computer algorithm, it records conversations, analysing what people say and how they speak. By picking up on a range of subtle verbal and non-verbal cues, it can correctly classify if someone is suicidal with 93 per cent accuracy. At the heart of the app is a machine learning algorithm which classifies the person based on their responses. In an earlier study, researchers enrolled a mix of 379 patients, who were suicidal, diagnosed as mentally ill, or neither.