Statistical mechanics of temporal association in neural networks with transmission delays

We study the representation of static patterns and temporal sequences in neural networks with signal delays and a stochastic parallel dynamics. For a wide class of delay distributions, the asymptotic network behavior can be described by a generalized Gibbs distribution, generated by a novel Lyapunov functional for the determination dynamics. We extend techniques of equilibrium statistical mechanics so as to deal with time-dependent phenomena, derive analytic results for both retrieval quality and storage capacity, and compare them with numerical simulations

Global analysis of parallel analog networks with retarded feedback

We analyze the retrieval dynamics of analog ‘‘neural’’ networks with clocked sigmoid elements and multiple signal delays. Proving a conjecture by Marcus and Westervelt, we show that for delay-independent symmetric coupling strengths, the only attractors are fixed points and periodic limit cycles. The same result applies to a larger class of asymmetric networks that may be utilized to store temporal associations with a cyclic structure. We discuss implications for various learning schemes in the space-time domain

Exact mean field inference in asymmetric kinetic Ising systems

We develop an elementary mean field approach for fully asymmetric kinetic Ising models, which can be applied to a single instance of the problem. In the case of the asymmetric SK model this method gives the exact values of the local magnetizations and the exact relation between equal-time and time-delayed correlations. It can also be used to solve efficiently the inverse problem, i.e. determine the couplings and local fields from a set of patterns, also in cases where the fields and couplings are time-dependent. This approach generalizes some recent attempts to solve this dynamical inference problem, which were valid in the limit of weak coupling. It provides the exact solution to the problem also in strongly coupled problems. This mean field inference can also be used as an efficient approximate method to infer the couplings and fields in problems which are not infinite range, for instance in diluted asymmetric spin glasses.Comment: 10 pages, 7 figure

From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits

Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852

Prospective memory impairments in Alzheimer's Disease and behavioral variant frontotemporal dementia: Clinical and neural correlates

BACKGROUND: Prospective memory (PM) refers to a future-oriented form of memory in which the individual must remember to execute an intended action either at a future point in time (Time-based) or in response to a specific event (Event-based). Lapses in PM are commonly exhibited in neurodegenerative disorders including Alzheimer's disease (AD) and frontotemporal dementia (FTD), however, the neurocognitive mechanisms driving these deficits remain unknown. OBJECTIVE: To investigate the clinical and neural correlates of Time- and Event-based PM disruption in AD and the behavioral-variant FTD (bvFTD). METHODS: Twelve AD, 12 bvFTD, and 12 healthy older Control participants completed a modified version of the Cambridge Prospective Memory test, which examines Time- and Event-based aspects of PM. All participants completed a standard neuropsychological assessment and underwent whole-brain structural MRI. RESULTS: AD and bvFTD patients displayed striking impairments across Time- and Event-based PM relative to Controls, however, Time-based PM was disproportionately affected in the AD group. Episodic memory dysfunction and hippocampal atrophy was found to correlate strongly with PM integrity in both patient groups, however, dissociable neural substrates were also evident for PM performance across dementia syndromes. CONCLUSION: Our study reveals the multifaceted nature of PM dysfunction in neurodegenerative disorders, and suggests common and dissociable neurocognitive mechanisms, which subtend these deficits in each patient group. Future studies of PM disturbance in dementia syndromes will be crucial for the development of successful interventions to improve functional independence in the patient's daily life

Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)