CMOS; plasticity; multi-electrode array; brain; learning and memory; MEA; embodyment; cortex; in vitro
Bakkum Douglas J, Radivojevic Milos, Frey Urs, Franke Felix, Hierlemann Andreas, Takahashi Hirokazu (2013), Parameters for burst detection., in Frontiers in computational neuroscience
, 7, 193-193.
Bakkum Douglas J, Frey Urs, Radivojevic Milos, Russell Thomas L, Müller Jan, Fiscella Michele, Takahashi Hirokazu, Hierlemann Andreas (2013), Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites., in Nature communications
, 4, 2181-2181.
Franke Felix, Jäckel David, Dragas Jelena, Müller Jan, Radivojevic Milos, Bakkum Douglas, Hierlemann Andreas (2012), High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity., in Frontiers in neural circuits
, 6, 105-105.
Müller Jan, Bakkum Douglas J, Hierlemann Andreas (2012), Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons., in Frontiers in neural circuits
, 6, 121-121.
The brain is arguably the most complex system studied in science. A human brain contains about one hundred billion brain cells (neurons), each of which communicate electrically and chemically with tens of thousands of other neurons and make up about a quadrillion (10^15) synaptic connections. The patterns and strengths of connections (the synapses) constantly change with experience and no two brains are alike. Neurons use voltage spikes called action potentials to relay sensations of the world, filtered through the connections in the brain, into commands for muscles to move the body and produce speech. Their manipulation gives rise to perceptions, interpretation, memories, consciousness, imagination, language, and adaptive behavior. Such manipulations are thought to emerge from the collective activity of ensembles of neurons, but much remains unknown about the fundamental rules governing information processing in the brain. With this in mind, individual neurons have been studied extensively at the cellular and molecular levels. However, mainly due to technological limitations, little is yet known about how the activity of individual neurons can combine to produce behavior, learning, and memory. Therefore, to investigate the population dynamics and plasticity of neural networks, a state-of-the-art 11,011-electrode complementary metal oxide semiconductor (CMOS) array was developed by the host laboratory . The densely packed electrodes allow for the first time 2-way access to any neuron grown over the array. Electrodes can both detect signals from multiple neurons in parallel and electrically evoke new signals, providing a long-term (months) communication between a neural network and a computer. Recording channels can be routed within milliseconds to connect to nearly any arbitrary set of 126 of the electrodes, and any electrode can supply stimulation. By then embodying the neurons with a simulated body situated within an artificial environment, we can observe in detail the population dynamics while the networks are expressing behaviors: recorded action potentials would determine movement while behavior would determine the subsequent feedback of electrical stimuli. For the Ambizione grant, we propose embodying cortical neurons (and glia) grown over 11,011-electrode CMOS arrays in order to investigate the network dynamics responsible for learning and memory in the brain. The proposed plan will continue past work in which I have already used a closed-loop approach to demonstrate simple behavior and learning in a cortical network grown over a 60-electrode array (selected as one of the Journal of Neural Engineering’s highlights of 2008). The plan is divided into two parallel themes. The first theme investigates what are the capabilities of embodied cortical networks to process information and achieve complex behavior. The second theme investigates what new capabilities can the CMOS array provide to describe cellular and network dynamics; analytical techniques from this theme and the preliminary data will then be used to determine how the networks processed information and produced behavior in the first theme. We seek to address both scientific and engineering issues. What is the capacity of neural tissue to encode and store information? What network and cellular dynamics are responsible for this? Can we find basic computational rules to create new types of artificial (or even biological) control systems? Could a better understanding of the electrode-neuron interface help inform the design of future sensory and motor neuroprosthetics? These are but a few of the questions that could be addressed with the CMOS array. Frey U, Egert U, Heer F, Hafizovic S, Hierlemann A. (2009). Microelectronic system for high-resolution mapping of extra-cellular electric fields applied to brain slices. Biosensors and Bioelectronics: 24.Bakkum DJ, Chao ZC, Potter SM. (2008) "Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task". Journal of Neural Engineering: 5(3) 310-323.