Self-organizing attractor networks may comprise the building blocks for cortical dynamics, providing the basic operations of categorization, including analog-to-digital conversion, association and auto-association, which are then expressed as components of distinct cognitive functions depending on the contents of the neural codes in each region. To assess the viability of this scenario, we first review how a local cortical patch may be modeled as an attractor network, in which memory representations are not artificially stored as prescribed binary patterns of activity as in the Hopfield model, but self-organize as continuously graded patterns induced by afferent input. Recordings in macaques indicate that such cortical attractor networks may express retrieval dynamics over cognitively plausible rapid time scales, shorter than those dominated by neuronal fatigue. A cortical network encompassing many local attractor networks, and incorporating a realistic description of adaptation dynamics, may be captured by a Potts model. This network model has the capacity to engage long-range associations into sustained iterative attractor dynamics at a cortical scale, in what may be regarded as a mathematical model of spontaneous lateral thought. This article is part of a Special Issue entitled: Neural Coding.
Overactivity of poly(ADP-ribose) polymerase enzyme 1 (PARP-1) is suggested to be a major contributor to neuronal damage following brain or spinal cord injury, and has led to study the PARP-1 inhibitor 2-(dimethylamino)-N-(5,6-dihydro-6-oxophenanthridin-2yl)acetamide (PJ-34) as a neuroprotective agent. Unexpectedly, electrophysiological recording from the neonatal rat spinal cord in vitro showed that, under control conditions, 1-60 μM PJ-34 per se strongly increased spontaneous network discharges occurring synchronously on ventral roots, persisting for 24 h even after PJ-34 washout. The PARP-1 inhibitor PHE had no similar effect. The action by PJ-34 was reversibly suppressed by glutamate ionotropic receptor blockers and remained after applying strychnine and bicuculline. Fictive locomotion evoked by neurochemicals or by dorsal root stimulation was present 24 h after PJ-34 application. In accordance with this observation, lumbar neurons and glia were undamaged. Neurochemical experiments showed that PJ-34 produced up to 33% inhibition of synaptosomal glutamate uptake with no effect on GABA uptake. In keeping with this result, the glutamate uptake blocker TBOA (5 μM) induced long-lasting synchronous discharges without suppressing the ability to produce fictive locomotion after 24 h. The novel inhibition of glutamate uptake by PJ-34 suggested that this effect may compound tests for its neuroprotective activity which cannot be merely attributed to PARP-1 block. Furthermore, the current data indicate that the neonatal rat spinal cord could withstand a strong, long-lasting rise in network excitability without compromising locomotor pattern generation or circuit structure in contrast with the damage to brain circuits known to be readily produced by persistent seizures.
How does the brain dynamically convert incoming sensory data into a representation useful for classification? Neurons in inferior temporal (IT) cortex are selective for complex visual stimuli, but their response dynamics during perceptual classification is not well understood. We studied IT dynamics in monkeys performing a classification task. The monkeys were shown visual stimuli that were morphed (interpolated) between pairs of familiar images. Their ability to classify the morphed images depended systematically on the degree of morph. IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image). The responses tended to peak approximately 120 ms following stimulus onset with an amplitude that depended almost linearly on the degree of morph. The responses then declined, but remained above baseline for several hundred ms. This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image. Thus, these neurons represented the dynamic conversion of graded sensory information into a task-relevant classification. Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons.
Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a "Brain-Computer Interface," is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject's EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.