Irregularity in the cortical spike code : noise or information?

How random is the discharge pattern of cortical neurons? We examined recordings from primary visual cortex (V1) and extrastriate cortex (MT) of awake, behaving macaque monkey, and compared them to analytical predictions. We measured two indices of firing variability: the ratio of the variance to the mean for the number of action potentials evoked by a constant stimulus, and the rate-normalized Coefficient of Variation (C_v) of the interspike interval distribution. Firing in virtually all V1 and MT neurons was nearly consistent with a completely random process (e.g., C_v ≈ 1). We tried to model this high variability by small, independent, and random EPSPs converging onto a leaky integrate-and-fire neuron (Knight, 1972). Both this and related models predicted very low firing variability ( C_v ≪ 1) for realistic EPSP depolarizations and membrane time constants. We also simulated a biophysically very detailed compartmental model of an anatomically reconstructed and physiologically characterized layer V cat pyramidal cell with passive dendrites and active soma. If independent, excitatory synaptic input fired the model cell at the high rates observed in monkey, the C_v and the variability in the number of spikes were both very low, in agreement with the integrate-and- fire models but in strong disagreement with the majority of our monkey data. The simulated cell only produced highly variable firing when Hodgkin-Huxley- like currents (I_(Na) and very strong I_(DR) were placed on the distal basal dendrites. Now the simulated neuron acted more as a millisecond-resolution detector of dendritic spike coincidences than as a temporal integrator, thereby increasing its bandwidth by an order of magnitude above traditional estimates. This hypothetical submillisecond coincidence detection mainly uses the cell's capacitive localization of very transient signals in thin dendrites. For millisecond-level events, different dendrites in the cell are electrically isolated from one another by dendritic capacitance, so that the cell can contain many independent computational units. This de-coupling occurs because charge takes time to equilibrate inside the cell, and can occur even in the presence of long membrane time constants. Simple approximations using cellular parameters (e.g., R_m, C_m, R_i, G_(Na) etc) can predict many effects of dendritic spiking, as confirmed by detailed compartmental simulations of the reconstructed pyramidal cell. Such expressions allow the extension of simulated results to untested parameter regimes. Coincidence-detection can occur by two methods: (1) Fast charge-equilization inside dendritic branches creates submillisecond EPSPs in those dendrites, so that individual branches can spike in response to coincidences among those fast EPSP's, (2) strong delayed-rectifier currents in dendrites allow the soma to fire only upon the submillisecond coincidence of two or more dendritic spikes. Such fast EPSPs and dendritic spikes produce somatic voltages consistent with intracellular observations. A simple measure of coincidence-detection "effectiveness" shows that cells containing these hypothetical dendritic spikes are far more sensitive to coincident EPSPs than to temporally separated ones, and suggest a conceptual mechanism for fast, parallel, nonlinear computations inside single cells. If a simplified model neuron acts as a coincidence-detector of single pulses, networks of such neurons can solve a simple but important perceptual problem-the "binding problem" -more easily and flexibly than traditional neurons can. In a simple toy model, different classes of coincidence-detecting neurons respond to different aspects of simple visual stimuli, for example shape and motion. The task of the population of neurons is to respond to multiple simultaneous stimuli while still identifying those neurons which respond to a particular stimulus. Because a coincidence-detecting neuron's output spike train retains some very precise information about the timing of its input spikes, all neurons which respond the same stimulus will produce output spikes with an above-random chance of coincidence, and hence will be easily distinguished from neurons responding to a different stimulus. This scheme uses the traditional average-rate code to represent each stimulus separately, while using precise single-spike times to multiplex information about the relation of different aspects of the stimuli to each other: In this manner the model's highly irregular spiking actually reflects information rather than noise.</p

Modeling and Analysis of Neural Spike Trains

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Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201