* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download presentation
Binding problem wikipedia , lookup
Catastrophic interference wikipedia , lookup
Action potential wikipedia , lookup
Subventricular zone wikipedia , lookup
Long-term potentiation wikipedia , lookup
Environmental enrichment wikipedia , lookup
Neuroeconomics wikipedia , lookup
NMDA receptor wikipedia , lookup
Neural modeling fields wikipedia , lookup
Neuroethology wikipedia , lookup
Artificial general intelligence wikipedia , lookup
Axon guidance wikipedia , lookup
Dendritic spine wikipedia , lookup
Mirror neuron wikipedia , lookup
Neural engineering wikipedia , lookup
Neuroregeneration wikipedia , lookup
Endocannabinoid system wikipedia , lookup
Artificial neural network wikipedia , lookup
Neural oscillation wikipedia , lookup
Multielectrode array wikipedia , lookup
Premovement neuronal activity wikipedia , lookup
Long-term depression wikipedia , lookup
End-plate potential wikipedia , lookup
Metastability in the brain wikipedia , lookup
Circumventricular organs wikipedia , lookup
Clinical neurochemistry wikipedia , lookup
Single-unit recording wikipedia , lookup
Caridoid escape reaction wikipedia , lookup
Holonomic brain theory wikipedia , lookup
Central pattern generator wikipedia , lookup
Apical dendrite wikipedia , lookup
Neuroanatomy wikipedia , lookup
Convolutional neural network wikipedia , lookup
Optogenetics wikipedia , lookup
Neuromuscular junction wikipedia , lookup
Feature detection (nervous system) wikipedia , lookup
Recurrent neural network wikipedia , lookup
Neural coding wikipedia , lookup
Molecular neuroscience wikipedia , lookup
Activity-dependent plasticity wikipedia , lookup
Pre-Bötzinger complex wikipedia , lookup
Stimulus (physiology) wikipedia , lookup
Types of artificial neural networks wikipedia , lookup
Neuropsychopharmacology wikipedia , lookup
Biological neuron model wikipedia , lookup
Neurotransmitter wikipedia , lookup
Nonsynaptic plasticity wikipedia , lookup
Channelrhodopsin wikipedia , lookup
Development of the nervous system wikipedia , lookup
Synaptogenesis wikipedia , lookup
Synaptic gating wikipedia , lookup
DARPA Autonomous Vehicle Grand Challenge 2003-2005 § BioRC Biomimetic Real-Time Cortex 2006- Reliable and Fault-Tolerant Safety-Critical Systems Nanoscale technologies can result in devices with variable behavior § Biological neurons do not fire with certainty and sometimes fire spontaneously: outcomes can vary § § § § Ion Channel Variability in Axon Hillock Synaptic Variability: Neurotransmitter Release Variability In spite of variable behavior (random or chaotic) individual biological neurons can be fault tolerant Biological neural networks are redundant, reliable and fault tolerant WITHIN A NEURON (INTRANEURON): BIOLOGICAL MECHANISMS LEAD TO NEUROMORPHIC MECHANISMS WITH A NEURAL NETWORK (INTERNEURON): BIOLOGICAL MECHANISMS LEAD TO NEUROMORPHIC MECHANISMS § Multiple vesicles and receptors for each synapse increase the probability of correct behavior even if some mechanisms fail NANOTUBE TRANSISTOR (PARALLEL NANOTUBES FORM THE CHANNEL) BUILT IN PROF. CHONGWU ZHOU’S NANOLAB § § Neuromorphic synapses can demonstrate variable behavior that mimics vesicle and receptor failure Axon hillocks can demonstrate variable behavior due to ion channel variability Synaptic Circuit Results with and without Variability Dr. Jason Mahvash n Synapse with no variability n V_neuro is constant (750mv) n Synapse with variability included n V_neuro is a Gaussian noise (mean=750mv, σ=50mv) n PSP peak does not change. V-neuro(v) 1 0.9 0.8 0.7 0 50 100 Time (ps) 150 200 Reliability of Spike Timing in Axon Hillock Circuit (Mahvash) Axon Hillock (Ion-Channel Variability Included) n Vth=170mv, Refractory period =100ps n Constant PSP = 180mv n Gaussian PSP generates spikes with more timing reliable n Ion-channel variability is included (Gaussian) Multiple synapses for each presynaptic/postsynaptic neuron pair increase the odds that the information is received by the post synaptic neuron even if some synapses fail Redundant synapses, dendrites, axon hillocks can form a single TMR neuron § Signal persistence in post-synaptic potentials: EPSP (excitatory +) and IPSP (inhibitory -) so precise timing is not absolutely necessary § Temporal Summation § Superlinear responses in neurons to specific input combinations of actively responding synapses (dendritic spikes) allow a small number of presynaptic neurons to invoke firing in the postsynaptic neuron § Dendritic spikes increase precision of subsequent neural spiking Input Jitter Impact on AP Spike Timing in Our Artificial Neuron Dr. Chih-Chieh Hsu No input jitter 0 0 20 20 40 40 Trial # Ø precisely-timed output spikes and more resilient to input jitter Trial # Neuron with active dendrites 20 ps input jitter 60 80 100 0 80 50 100 150 200 250 Time (ps) 100 0 0 0 20 20 40 40 60 100 150 200 250 60 80 80 100 0 50 Time (ps) Trial # imprecise spiking and less tolerant to input jitter Trial # Neuron without active dendrites Ø 60 50 100 150 Time (ps) 200 250 100 0 50 100 150 Time (ps) 200 17 250 § § § Spiking patterns can be designed to ensure postsynaptic neural response Identical pathways in neural networks operate properly even when individual neurons fail Glial cells (astrocytes) interact with neurons e.g. to induce synchronous firing that enhances reliability § § § There are 10 times as many glial cells as neurons in the brain Glial cells control blood flow and propagation speed Glial cells affect processing and memory Astrocytes Neurons OUR EXCITATORY TRIPARTITE SYNAPSE INCLUDING ASTROCYTE WITH SLOW INWARD CURRENTS (SICS) Postsynaptic Presynaptic Synapse Circuit Presynaptic Side APs NT_Conc. cleft Postsynaptic Side + (3) Delay Reuptake (1) Receptor EPSP Delay AstroCa2+ Astro_glut_release Extrasynaptic Side Spread (2) BLOCK DIAGRAM OF A SMALL 2 NEURON NEURAL NETWORK WITH ASTROCYTES AP1_N1 AP2_N1 Presynaptic Side Postsynaptic Side Presynaptic Side Postsynaptic Side S1 EPSPs Dendritic Arbor Extrasynaptic NMDA (Fig.5) Astrocyte Microdomain_2 Fig.8 AP3_N2 AP4_N2 Total_EPSP_N1 Astrocyte glioTs_M1 Astrocyte glioTs_M2 Total_EPSP_N2 Extrasynaptic NMDA (Fig.5) S4 APout_N1 Extrasynaptic_side S2 Astrocyte Microdomain_1 Axon Hillock S3 Extrasynaptic_side Presynaptic Side Postsynaptic Side Presynaptic Side Postsynaptic Side Dendritic Arbor EPSPs Axon Hillock APout_N2 ASTROCYTE TEST BENCH Two astrocytic microdomains connected to two networks are able to interact with each other. The network connected to M1 spikes at a higher frequency and is able to trigger SICs (Slow Inward Currents) in both NTs networks. NTs N3 N1 SICs M1 N4 N2 NTs N7 N5 M2 SICs N8 N6 NTs SIMULATION RESULTS FOR NETWORK 2 NEURONS 7 AND 8 First, neurons 7 and 8 are unsynchronized, then we enable the astrocytes To inject slow inward currents EPSPs and they synchronize JOSHI STRUCTURAL PLASTICITY § § Biological neural networks provide quasi-redundancy, with multiple pathways arriving at a destination neuron through performance of different but related computations. For example, recognition of a square shape § § § § recognition of many edges at specific locations and orientations, or recognition of 90 degree corners at specific locations. Either approach could give provide square recognition, Both in combination could provide recognition even when part of the square shape is occluded or the image is noisy. WE CAN BUILD ELECTRONIC NEURONS AND PARTS OF NEURONS: Ø WITH SYNAPTIC PLASTICITY – THE CONNECTIONS BETWEEN NEURONS CAN CHANGE STRENGTHS ØWITH STRUCTURAL PLASTICITY – NEW CONNECTIONS CAN FORM AND OLD ONES CAN DISAPPEAR ØTHAT DEMONSTRATE VARIABLE BEHAVIOR (STOCHASTIC AND CHAOTIC) ØTHAT CONTAIN BOTH EXCITATORY AND INHIBITORY INPUTS ØTHAT MIMIC RETINAL NEURONS WITH GRADED POTENTIALS ØOUT OF NANOTRANSISTORS – CARBON NANOTUBES ØTHAT COMMUNICATE WITH ASTROCYTES (A FORM OF GLIAL CELL ) ØWITH DENDRITIC COMPUTATIONS – WE CAN ADD INPUTS IN COMPLICATED MANNER, INCLUDING DENDRITIC SPIKING ØWITH DENDRITIC PLASTICITY – THE ADDITIONS OF INPUTS CAN VARY A WE CAN BUILD SMALL NEURAL NETWORKS, INCLUDING MODELING OCD, MS, SCHZOPHRENIC HALLUCINATIONS, C. ELEGANS TOUCH-SENSITIVE NW DEMONSTRATE SELF AWARENESS DEMONSTRATE ABSTRACT REASONING DEMONSTRATE CONSCIOUSNESS DEMONSTRATE THE ROLE OF EMOTIONS DEMONSTRATE ALTRUISM …….. § Adam Levine @adamlevine Lead Singer, Maroon 5 on Twitter § “how awesome is it gonna be when we are updating the software.......in our BRAINS.” THANK YOU Vref dspike* Sodium Spike bAP* Threshold Adjustment VDD EPSP1 Vna Vca-in EPSP2 Na+ EPSP3 EPSP4 Calvl 6X EPSP5 Calcium Influx vss1 EPSP6 Apical/Basal Branchi vss2 X0 0.7 Basal1 Basal2 Apical1 Apical2 2X 2X 2X VDD VDD Basalattn Basal VDD VDD VDD 2X EPSPDDNx IPSPBONy Apical Dendritic Potential (V) VDD VDD 0.6 VDD 0.5 0.4 4X 0.3 0.2attn Apical DD PSPSOMA 4X 0.1 0 decreasing spike threshold V in dendrite VDD 4X 0 4X VDD 1 2 3 4 5 Number of Activated Synapses 6